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As an aerial base station, unmanned aerial vehicle (UAV) has been considered as a promising technology to assist future wireless communications due to its flexible, swift and low cost features, where resource allocation is the basis for ensuring energy-efficient UAV-assisted networks. This paper formulates a joint optimization problem of user association, UAV trajectory design and power control to maximize the channel capacity among all ground users at a limited power level in a downlink transmission. To tackle the mixed-integer non-linear programming problem, this paper proposes a clustering-aided reinforcement learning approach consisting of three consecutive stages. Firstly, modified expectation-maximization unsupervised learning algorithm is investigated to cluster the ground users, which reduces the dimensions and hence, the association complexity is reduced as well. Then, Kuhn-Munkres algorithm is incorporated for user association, which associates a UAV with the ground users via matching to the cluster, and assigns the UAVs to the centroid of the matching cluster for pre-placement, with the aim of speeding up the convergence of the following deep reinforcement learning algorithm. Finally, a multi-agent twin delayed deep deterministic (MATD3) policy gradient is proposed to solve the non-convex sub-problem, which determines the transmit power and designs the fine-tuned trajectory of UAVs. By incorporating low-bias value estimation technique, the reward of the proposed MATD3 algorithm is improved. Simulation results have demonstrated that our proposed approach achieves higher reward as well as converging faster than existing reinforcement algorithms. Besides, the clustering-aided reinforcement learning has lower computational complexity than the benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider an energy efficient resource allocation technique for a hybrid time division multiple access (TDMA) - non-orthogonal multiple access (NOMA) system. In such a hybrid system, the available time for transmission is divided into several sub-time slots, and a sub-time slot is allocated to serve a group of users (i.e., cluster). Furthermore, signals for the users in each cluster are transmitted based on the NOMA approach. With NOMA, multiple users can be served simultaneously through utilizing power domain multiplexing at transmitter and successive interference cancellation (SIC) at receiver. In this paper, to maximize the energy efficiency (EE), we jointly allocate both the available time slots and the available transmit power in the hybrid TDMA-NOMA system. In particular, we formulate an EE maximization (EE-Max) problem aiming to maximize the overall EE of the system with a per-user minimum rate and transmit power constraints. However, this joint optimization problem is non-convex in nature, and thus, cannot be solved directly. Therefore, we develop an iterative algorithm by approximating the original problem into a convex one with sequential convex approximation (SCA) and a novel second-order cone (SOC) approach. Simulation results demonstrate that the performance of the proposed hybrid TDMA-NOMA system with joint resource allocation outperforms the system with equal time allocation in terms of the overall EE. Simulation results further confirm that the proposed iterative approaches with SCA and SOC techniques converge within a few number of iterations while yielding the solution to the original non-convex problem. [ABSTRACT FROM AUTHOR]

In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [ABSTRACT FROM AUTHOR]

With the development of new radio (NR) technologies, sidelink (SL) transmission plays a more vital role in supporting low latency, higher reliability vehicle-to-everything (V2X) communications. Compared with the Long-Term Evolution (LTE), NR V2X aims to support enhanced V2X use cases with more stringent latency and reliability requirements. 3GPP introduces NR V2X distributed resource allocation to fulfill the low latency requirement to reduce the delay caused by requesting transmission resources from gNB. In distributed mode, UEs autonomously select resources without the scheduling from gNB. NR V2X introduces the physical sidelink feedback channel (PSFCH) to enhance transmission reliability. This paper presents the resource allocation mechanism for NR V2X distributed mode. Since the SL feedback channel is a new feature different from LTE V2X, we discuss the potential enhancements on resource allocation utilizing SL feedback channel to improve the reliability. This paper proposes a Receiver Grant-based resource allocation, which allows the receiver to send the grant with the feedback channel. By allowing the transmitter to indicate the pre-selected resources before the end of the previous reservation, the receiver can inform the transmitter about the collided resource selection through the PSFCH. The analytical models for NR V2X distributed mode with the feedback channel are also provided for UE to estimate the collision ratio. The simulation results and the analytical model show that the proposed Receiver (Rx) Grant-based resource allocation improves transmission reliability in terms of collision ratio. For future advanced V2X use cases, the analytical models provide an evaluation method and help UE make decisions to meet the reliability requirements. [ABSTRACT FROM AUTHOR]

Due to the non-uniform geographic distribution and time-varying characteristics of the ground traffic request, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for beam hopping satellite systems. The conventional greedy-based beam hopping methods do not consider the long-term reward, which is difficult to deal with the time-varying traffic demand. Meanwhile, the heuristic algorithms such as genetic algorithm have a slow convergence time, which can not achieve real-time scheduling. Furthermore, existing methods based on deep reinforcement learning (DRL) only make decisions on beam patterns, lack of the freedom of bandwidth. This paper proposes a dynamic beam pattern and bandwidth allocation scheme based on DRL, which flexibly uses three degrees of freedom of time, space and frequency. Considering that the joint allocation of bandwidth and beam pattern will lead to an explosion of action space, a cooperative multi-agents deep reinforcement learning (MADRL) framework is presented in this paper, where each agent is only responsible for the illumination allocation or bandwidth allocation of one beam. The agents can learn to collaborate by sharing the same reward to achieve the common goal, which refers to maximize the throughput and minimize the delay fairness between cells. Simulation results demonstrate that the offline trained MADRL model can achieve real-time beam pattern and bandwidth allocation to match the non-uniform and time-varying traffic request. Furthermore, when the traffic demand increases, our model has a good generalization ability. [ABSTRACT FROM AUTHOR]

Benefiting from the rapid progress of wireless communication and artificial intelligence, multi-agent collaboration opens up new opportunities for various fields. To facilitate multi-agent acting as a group, effective communication plays a crucial role. Recently, many efforts based on multi-agent reinforcement learning have been made to enable effective multi-agent communication under limited bandwidth or noisy channel. However, current methods do not explore wireless resource allocation strategy explicitly. Moreover, due to ignoring task-relevant significance of information, traditional wireless resource allocation schemes may fail to guarantee the transmission efficiency and reliability for multi-agent communication. To this end, in this paper, we propose a task-oriented communication principle for multi-agent communication. We model the task-oriented channel allocation problem as a decentralized partially observable Markov decision process and propose a multi-agent reinforcement learning framework as a solution. Specifically, we design a novel variational information bottleneck to extract task-relevant information from local observation. Furthermore, a task-oriented channel allocation mechanism is developed to choose the allocation pattern with maximum expected gain. Finally, a double attention mechanism is developed to motivate the efficient utilization of task-relevant information. Experimental results show that our method can improve the effectiveness and efficiency of multi-agent communication, enhancing collaboration performance compared to baselines. [ABSTRACT FROM AUTHOR]

The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages. [ABSTRACT FROM AUTHOR]

Mobile edge computing is popular for providing services with low latency and high privacy. It becomes more powerful by leveraging the widely deployed Small cell Base Stations (SBS) with zooming cells for energy-efficient service placement. However, the literature ignores the joint optimization of SBS power control and service placement. The Quality of Services (QoS) guarantee is also challenging, taking into account the component dependency and parallelization in multi-component services and the resource slicing for separated service deployment. Moreover, prior works assume the same amount of allocated resources of links on a placed path for service edge allocation, leading to the ossification of resource allocation and inevitable resource fragments. Towards the above challenges, this paper addresses the energy-efficient and flexible service placement in cell zooming enabled Mobile Edge Cloud (MEC) networks. The delay of multi-component services is constructed depending on the allocated resource slice. Besides, the joint optimization of service placement and SBS power control is formulated and transformed into a Mixed Integer Linear Programming (MILP). More importantly, the equivalent bandwidth for an edge allocation is defined and analyzed to obtain flexible edge placement with minimum resource cost. Leveraging the results of MILP, an Energy-efficient Service placement algorithm based on equivalent Bandwidth in Cell zooming enabled MEC networks (ESBC) is proposed to improve the probability of successful service placement with QoS guarantee by optimizing the delay distribution among components and edges and reducing resource fragments. Finally, simulation results validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The number of individuals and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers is rapidly increasing, thus overloading them and raising the risk of service interruptions. Hence, reactive service replication has been suggested to enable individuals and groups of users to access services from remote edge servers, thus guaranteeing system scalability. This paper proposes a task offloading and service replication scheme on local and remote MEC servers. The scheme minimizes the response time of all users while satisfying the delay requirements of user groups in traffic-heavy and multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate an integer linear problem that minimizes the average response time of all users while satisfying the time and time difference constraints of the user groups running the same applications. We then use linear relaxation programming using Lagrangian analysis and solve the problem using a numerical solver. In addition, we compare the optimal solution to distance-based and resource-based greedy approaches. The results demonstrate the merits of our proposed optimized decision scheme compared to these two greedy approaches. [ABSTRACT FROM AUTHOR]

In this article, we focus on the pilot-assisted transmission design for secure ultra-reliable and low-latency communication (URLLC) systems, which has not yet been studied in the existing literatures. In each transmission period of this system, the multi-antenna controller sends confidential URLLC messages integrated with artificial noise (AN) to a single-antenna actuator for some mission-critical operations (e.g., remote control), and while an unknown number of passive eavesdroppers attempt to intercept this confidential message in a non-colluding manner. Since the conventional ergodic secrecy capacity is not accurate to describe the maximal achievable secrecy rate of URLLC with short blocklength coding, we establish the concept of achievable effective secrecy rate (AESR) to measure the secrecy performance of our URLLC system, which is defined as the average number of information bits can be transmitted per channel use under given reliability and information leakage constraints. Due to the exact expression of AESR has an intractable form, we derive a closed-form approximation on the AESR to simplify the calculation, via leveraging the Laplace approximation and Gaussian-Chebyshev quadrature method. Following by this approximation, we propose an efficient two-dimensional search algorithm to determine the optimal transmission parameters that maximize the AESR, i.e., the training pilot length and the power allocation ratio between confidential data signals and AN. Furthermore, the asymptotic performance of AESR in the high-SNR regime is also analyzed to gain more insights. Numerical results validate the accuracy of theoretical approximations presented in this paper, and illustrate how the particular system parameters impact the AESR of our considered URLLC system. [ABSTRACT FROM AUTHOR]

The application of non-orthogonal multiple access (NOMA) to multi-layer multicast video streaming is envisioned to address the explosively growing demand for capacity which is dominated mostly by multimedia content. In this paper, a joint power allocation and subgrouping scheme is developed to enhance the performance of NOMA-based multi-layer multicast systems. An optimization problem is formulated to maximize the overall sum multicast rate whilst satisfying the maximum transmission power and proportional rate constraints. Due to the complexity of the optimization problem, we first derive two power allocation techniques for the 2-layer case considering arbitrary subgrouping which are based on the iterative implementation and closed-form analysis, respectively. We then generalize the low-complexity closed-form solution for the general multi-layer case. This scheme successively allocates power to each layer stream while assuring that the minimum target rate and proportionality are guaranteed, particularly for the transmission rate of the high-priority base layer stream. A sub-optimal joint power allocation and subgrouping scheme is also designed by incorporating the power allocation scheme into the proposed iterative subgrouping techniques. Simulation results show the effectiveness of the power allocation and subgrouping schemes in enhancing the sum multicast rate performance. In addition, the proposed power allocation scheme ensures substantially higher rates for the base layer stream, which is crucial in robust delivery of standard quality video to all users. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication has been designed to afford improvements in the traffic congestion and traffic accidents by directly exchanging information between nearby vehicles. However, V2V communication may have difficulty providing service reliability due to interference between V2V links and vehicle-to-cellular user equipment links. In this paper, we consider the transmit power control algorithm to minimize interference among cellular users and vehicles in the context of V2V communication underlaid uplink cellular networks. First, we formulate the problem, which is an NP-hard combinatorial optimization problem with linear constraints. Addressing this problem with traditional optimization methods is ineffective; therefore, we design and train a deep neural network to address this optimization problem. Computational complexity analyses and simulation results reveal that the proposed algorithm outperforms weighted minimum mean squared error (WMMSE), fixed transmit power, and Dinkelbach's methods, and achieves near-global optimum with lower computation complexity than the exhaustive search (ES). [ABSTRACT FROM AUTHOR]

In this paper, we investigate a downlink non-orthogonal multiple access (NOMA) transmission scheme for hybrid millimeter-wave (mmWave) system and study its user grouping and power allocation. In order to maximize the weighted sum-rate (WSR) of the system, we first propose a user grouping algorithm based on min-sum message passing (MSMP) strategy under the limited number of radio frequency (RF) chains. Then, with the zero-forcing (ZF) digital precoding, we propose a power allocation scheme for multiple-RF-chain NOMA (MRFC-NOMA) structure based on a non-convex transformation method, and design a low-complexity algorithm. The simulation results show that the proposed joint user grouping and power allocation method with lower complexity can achieve better WSR performance than the traditional schemes. [ABSTRACT FROM AUTHOR]

The spectrum usage often comes in an online fashion. Considering the selfish behaviors of both primary users(PUs) and sencond users(SUs), we design online double spectrum allocation methods. We propose a truthful online double auction for spectrum allocation. Preempting existing spectrum usage is not allowed. We design a strategyproof mechanism for both the primary user side and the SU side. In previous studies, users who do not interfere with each other have not been reasonably allocated to use channels. There is one and only one user on the used spectrum channel at a certain time, which will result in discarding many user requests. Our model allows multiple users to use a spectrum channel at the same time. Aiming at the shortcomings of previous research, we propose the concept of grouping for online auctions, so that all SUs that do not interfere with each other can use spectrum channels at the same time. This will greatly increase the number of users who use the spectrum at the same time. We thus increase the utilization rate of the user market, and a large number of users will not be idle and abandoned.In the design of our experiments, the grouping model proposed in this paper has obtained at least $43{\%}$ utilization rate on the set channel, and our experiments have obtained good generality for different interference radius. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks. [ABSTRACT FROM AUTHOR]

Control- and user-plane separation (CUPS) and network slicing are two key technologies to support increasing network traffic and diverse wireless services. However, the benefit of CUPS in decoupling the network coverage and data service functions has not been fully utilized to facilitate network slicing. In this paper, we present a novel CUPS-based end-to-end (CUPS-E2E) network slicing scheme. First, the base stations (BS) are classified into control BSs (CBS) that provide control plane (CP) coverage and traffic BSs (TBS) that deliver user plane (UP) traffic. Next, upon CBSs and TBSs being virtualized, we define four typical end-to-end (E2E) network slices: one for CP coverage, one for high-throughput services, one for computation-intensive services, and the other for delay-sensitive services. The utilities of the four E2E network slices are defined based on their coverage, throughput, computing capability and delay requirements, respectively. Then, a deep deterministic policy gradient (DDPG)-based algorithm is employed to maximize the long-term sum-utility of the four E2E network slices by jointly optimizing the allocation of communication and computing resources to the four network slices and the activation of virtual TBSs, while meeting the service requirements of all users. Simulation results show that our proposed CUPS-E2E network slicing scheme in conjunction with a DDPG-based sum-utility maximization algorithm can support the CP wide-coverage and massive access requirements as well as the UP high-throughput, computation-intensive and delay-sensitive services simultaneously, and outperforms the existing E2E network slicing schemes in terms of the sum-utility, coverage percentage, throughput and delay. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [ABSTRACT FROM AUTHOR]

The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 to support direct communication between vehicles. In Mode 4, the sensing-based semipersistent scheduling (SPS) scheme enables vehicles to autonomously reserve and select radio resources. In particular, SPS has three processes to realize the resource scheduling, including continuously sensing resources, probabilistically reselecting resources, and periodically reserving resources. However, vehicles randomly select resources from the available resource lists in the resource reselection process, resulting in frequent packet collisions especially when radio resources are insufficient. Unlike the traditional SPS, this paper proposes a multiagent deep reinforcement learning-based SPS (RL-SPS) algorithm to help vehicles select appropriate radio resources with the aim of reducing packet collisions. Furthermore, a multi-head attention mechanism is adopted to improve the training efficiency by helping vehicles selectively pay attention to the observations and actions of neighbouring vehicles. It is worth noting that the RL-SPS algorithm fits the characteristics of Mode 4, which selects resources without requiring any global information. Simulation results show that RL-SPS outperforms other decentralized approaches and demonstrate the scalability and robustness of RL-SPS in a dynamic vehicular network. [ABSTRACT FROM AUTHOR]

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing algorithms, dynamic vehicular environments and quantization of continuous power become the bottlenecks for providing an effective and timely resource allocation policy. In this paper, we develop two algorithms to deal with these difficulties. First, we propose a deep reinforcement learning (DRL)-based resource allocation algorithm to improve the performance of both V2I and V2V links. Specifically, the algorithm uses deep Q-network (DQN) to solve the sub-band assignment and deep deterministic policy-gradient (DDPG) to solve the continuous power allocation problem. Second, we propose a meta-based DRL algorithm to enhance the fast adaptability of the resource allocation policy in the dynamic environment. Numerical results demonstrate that the proposed DRL-based algorithm can significantly improve the performance compared to the DQN-based algorithm that quantizes continuous power. In addition, the proposed meta-based DRL algorithm can achieve the required fast adaptation in the new environment with limited experiences. [ABSTRACT FROM AUTHOR]

This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users’ short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed algorithms focus on joint resource allocation and scheduling for 3GPP-compliant multiple unicast vehicle-to-vehicle (V2V) communications where the vehicles are group leaders (GLs) in 3GPP Mode 4(d). The resource allocation stands for the roadside unit (RSU) allocation to scheduled V2V GL links where RSU is essentially required for overcoming blockage by establishing two-hop relaying. Because vehicles stay for short times at junctions, this paper designs two algorithms without or with delay considerations. Without delay considerations, the joint optimization of RSU allocation and scheduling was originally formulated as mixed 0-1 non-convex optimization. However our proposed algorithm reformulates the problem into mixed 0-1 convex optimization, which is computationally easier to solve. With delay considerations, our proposed algorithm dynamically controls video contents frame rates for time-average on-driving video sharing quality maximization subject to delay constraints, inspired by Lyapunov optimization. Extensive simulation results demonstrate that our algorithms can significantly outperform in a variety of scenarios. Furthermore, we conduct the cost analysis for the proposed algorithms in terms of capital expenditure (CAPEX) and operating expenditure (OPEX). [ABSTRACT FROM AUTHOR]

The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, a downlink multi-cluster NOMA network is considered, where each cluster is supported by one IRS. This paper aims to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to be solved due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, an alternating optimization based algorithm is proposed. Specifically, the primal problem is divided into two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, a simplified algorithm based on partial exhaustive search for this system model is also proposed. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, NOMA with random IRS phase shift scheme and OMA-IRS scheme. [ABSTRACT FROM AUTHOR]

The densification of wireless infrastructure to meet ever-increasing quality of service (QoS) demands, and the ever-growing number of wireless devices may lead to higher levels of electromagnetic field (EMF) exposure in the environment, in the 5G era. The possible long term health effects related to the EMF radiation are still an open debate and requires attention. Therefore, in this paper, we propose a novel EMF-aware resource allocation scheme based on the power domain non-orthogonal multiple access (PD-NOMA) and machine learning (ML) technologies for reducing the EMF exposure in the uplink of cellular systems. More specifically, we use the K-means approach (an unsupervised ML approach) to create clusters of users to be allocated together and to then strategically group and assign them on the subcarriers, based on their associated channel properties. Finding the best number of clusters in the PD-NOMA environment is a key challenge, and in this paper, we have used the elbow method in conjunction with the F-test method to effectively control the maximum number of users to be allocated at the same time per subcarrier. We have also derived an EMF-aware power allocation by formulating and solving a convex optimization problem. Based on the simulation results, our proposed ML-based strategy effectively reduces the EMF exposure, in comparison with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a cloud-assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-and-play charging during run-time operations. The charging towers should be cost-effective, thus it is equipped with photovoltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness by intelligent energy sharing. Based on the needs and setting, this paper proposes 1) charging scheduling between UAVs and towers and 2) cooperative energy managements among towers. For charging scheduling, the UAVs and towers should be scheduled for maximizing charging energy amounts and the scheduled pairs should determine charging energy allocation amounts. Here, two decisions are correlated, i.e., it is a non-convex problem. We re-formulate the non-convex to convex for guaranteeing optimal solutions. Lastly, the cooperative energy sharing among towers is designed and implemented with multi-agent deep reinforcement learning and then intelligent energy sharing can be realized. We can observe that the two methods are related and it should be managed, coordinated, and harmonized by a centralized orchestration manager under the consideration of fairness, energy-efficiency, and cost-effectiveness. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance. [ABSTRACT FROM AUTHOR]

The multi-hop unmanned aerial vehicle (UAV) network can serve as data relays where ground users (GUs) do not have reliable direct connections to the base station (BS). Existing works mainly focus on simple dual-hop system. In this paper, we investigate the packet routing problem in a multi-hop UAV relay network to minimize the data transmission time and enhance the network throughput. However, the dynamic network topology due to UAV mobility makes the packet routing challenging since the limited communication range of each UAV leads to volatile wireless connection. Moreover, the line-of-sight communication links may cause strong interference among UAVs. Towards this end, we propose a novel multi-agent deep reinforcement learning based algorithm, named as multi-agent QMIX (MAQMIX) to: 1) design proper UAVs’ trajectories to serve the moving GUs while maintaining the network connection; 2) allocate frequency resource properly among UAVs to alleviate the impact of interference; and 3) choose a proper next hop UAV for each data packet to reduce the transmission time and probability of network congestion. The proposed MAQMIX has two novel training mechanisms, i.e., intra-UAV and inter-UAV training mechanisms, which can tackle the large action space issue and coordinate the training among UAVs in the multi-hop UAV relay network. Simulation results demonstrate that the MAQMIX outperforms baseline schemes in terms of the network congestion avoidance, throughput, and transmission time. [ABSTRACT FROM AUTHOR]

Most of the existing works in millimeter wave (mmWave) and non-orthogonal multiple access (NOMA) communications (mmWave NOMA) employs phased array (PA) antennas which yield only angle beamspace without range dependent, range-interference suppression and “dot-shaped” array resolution. In this paper, we propose a collocated transmitter (Tx) - receiver (Rx) mmWave NOMA system where frequency diverse array (FDA) antennas are utilized. FDA employs small frequency increments across the element indices which yields angle-range dependent beamspace. Herein, an equivalent radiate beamspace is designed to serve the users. Further, to ensure user equity, we formulate objective function to max-min achievable sum rate subject to power allocation and BS equivalent FDA transmit beamspace along the users. Since the problem is not convex, a boundary-compressed Bat meta-heuristic optimization algorithm is devised to provide a sub-optimal solution by optimizing the frequency increments to synthesis the BS beamspace along the users. The achievable secrecy rate, cost efficiency (CE), economic efficiency (ECE), and secrecy energy efficiency (SEE) are derived for the users. Both theoretical analysis and simulation results indicate that the proposed scheme would constitute an excellent nominee for 5G mmWave NOMA communication applications. [ABSTRACT FROM AUTHOR]

Adaptive bitrate streaming can confront dynamic environment and satisfy multifarious user requirements by encoding video files into different bitrate versions. It can significantly improve the user quality of experience (QoE). But at the same time, it can also lead to a further increase in video data volume. The interaction of massive video data tends to aggravate network congestion and increase transmission energy, which triggers new challenges for wireless networks in terms of delivery delay and energy consumption. In this paper, taking different video sizes and playback rate requirements into account, we aim to minimize the weighted sum of delivery delay and energy consumption for adaptive video streaming. The problem is formulated by jointly optimizing caching, computing and power allocation, which turns out to be a mixed integer nonlinear programming (MINLP) problem. To tackle it, a joint caching, computing and power allocation (JCCPA) algorithm is proposed. More specifically, the original problem is decomposed into two subproblems: 1) power allocation problem and 2) joint caching placement and computing decision problem. The power allocation problem is proved to be a quasi-convex problem, and the closed-form solution is derived. With the optimal transmit power, the joint caching placement and computing decision problem is further divided into two subproblems, which are solved in an iterative manner. Simulation results demonstrate that the proposed algorithms can effectively reduce the weighted sum of delivery delay and energy consumption (outperforms the baselines around 5%–12% within a certain range). [ABSTRACT FROM AUTHOR]

The edge computing service mode has entered the edge federation service stage, where comprehensive resources are supported to collaborate between vertical cloud-edge and horizontal edge nodes. The massive data processing poses a severe challenge to the traditional storage mechanism. In this paper, we propose an efficient storage resource collaboration model that leverages the selection of storage collaborative edge nodes and the allocation of storage resources among collaborative nodes, which can provide efficient and balanced storage services solutions. Specifically, selecting storage collaborative edge nodes is a multi-objective integer linear programming problem. We transform it into a single objective 0-1 integer linear programming problem and use the dynamic programming method to obtain the optimal solution. In addition, we condense the storage resource allocation problem into a mixed-linear integer programming problem and use the greedy auction algorithm to obtain an approximate optimal solution, which significantly improves efficiency and performance. We evaluate the performance of the proposed two mechanisms by performing extensive experiments. The experimental results show that they are efficient solutions to solve the problem of the storage resource collaboration model among edge nodes in edge federation service. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the maximum flow routing strategy with dynamic link allocation under a constraint on the number of transceivers for space information networks (SINs). Specifically, the time-expanded graph (TEG) is exploited to characterize the dynamic topology of SINs. Furthermore, although there exist multiple feasible links for SINs, only a limited number of them can be actually established due to the constraint on the number of transceivers. Traditionally the established link is fixed within one time interval in the TEG. In order to fully exploit the resource of multiple feasible links, we divide each time interval in the TEG into multiple fine-grained time periods and design the maximum flow routing strategy by jointly optimizing both the fine-grained time period duration and the link allocation as well as the amount of data transmitted on each transmission link and the amount of data stored in each caching link. This problem can be formulated as a mixed-integer quadratic program (MIQP), which is difficult to solve. To overcome this difficulty, we transform the MIQP into an equivalent mixed-integer linear program (MILP), which can be effectively solved by existing methods. Simulation results show that the proposed dynamic link allocation strategy can significantly outperform the fixed link allocation strategy within each time interval. [ABSTRACT FROM AUTHOR]

Aerial base stations (ABSs) are expected to be important supplementary components for the 5G-and-beyond communication systems to achieve global Internet of Everything. To fully exploit the advantages of the ABSs seamless connection, in this paper, we propose a high altitude platform (HAP) and low altitude platforms (LAPs) cooperated network architecture. Specifically, the HAP can extend the network coverage and LAPs can act as relays to improve the transmission performance for hot spots. In the considered network, we investigate the sum rate maximization problem by optimizing the downlink and the uplink transmissions, respectively. In the downlink, we adopt the orthogonal frequency division multiple access technique and take the basic rate requirement into account. In addition, we optimize the height of LAPs and the spectrum allocation between the HAP and LAPs, and between the LAP and users. Then, we decouple the downlink sum rate maximation problem as three subproblems. An alternating optimization framework is designed to deal with these non-convex optimization problems, where the height and spectrum allocation are tackled in turn. In the uplink, we adopt the non-orthogonal multiple access technique, and consider the decoding threshold of the successive interference cancellation technique. Afterward, we optimize the power allocation of each user, and the spectrum allocation between users and the LAP, and between LAPs and the HAP. Finally, simulation results show the effects of spectrum allocation, height optimization and power allocation on network performance, which verify that the proposed scheme can achieve higher sum rate on both downlink and uplink in comparison with the current works. [ABSTRACT FROM AUTHOR]

In future wireless communication systems, intelligent reflecting surface (IRS) plays an important role since it is capable of dynamically reconfiguring the channel conditions. In this paper, we apply IRS to facilitate the communication of an uplink multiple-input multiple-output non-orthogonal multiple access system. The objective is to maximize the system energy-efficiency (EE), requiring a joint optimization of the active beamforming at the base station, passive beamforming at the IRS, and power allocation. An iterative method is proposed to solve the formulated multi-variable non-convex optimization problem. Moreover, a low-complexity user ordering method is provided. Numerical results verify the rapid convergence of the iterative optimization scheme and the effectiveness of the user ordering. In addition, a substantial gain can be obtained by employing the proposed scheme over the benchmark schemes without IRS and with orthogonal multiple access. [ABSTRACT FROM AUTHOR]

In this paper, a new game-theoretic multi-layer computing resource and network resource allocation algorithm is proposed for computation offloading in Multi-access Edge Computing (MEC) networks. We establish a new multi-layer system model for Data Center Operators and develop resource allocation relation between the MEC data center operators and users. We also investigate the matching relationship between the cloudlets and users. Based on the proposed multi-layer allocation algorithm, a joint optimization framework is delivered to realize the allocation of computing resources and communication channels in MEC. Simulation results show that the proposed algorithm provides effective solution for the problem of cloudlets allocation in MEC networks, and can significantly improve the computing and communication capabilities in the presence of user heterogeneity. [ABSTRACT FROM AUTHOR]

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two core services of the emerging fifth-generation (5G) systems. URLLC services require lower latency and high reliability, whereas eMBB services expect maximum data rates. Since 5G new radio (NR) aims to support heterogeneous services, the 3rd generation partnership project (3GPP) supports a highly flexible frame structure by introducing mixed numerology and mini-slot approaches. In these systems, the time slot is further divided into mini-slots. The coexistence of these two heterogeneous services leads to a challenging resource allocation problem. In this paper, we study the joint resource allocation problem of eMBB and URLLC schedulers to maximize the minimum expected achieved rate (MEAR) of eMBB users while satisfying URLLC users’ quality of service (QoS) constraints. The resource allocation of eMBB traffic occurs at time slot boundaries. In contrast, URLLC traffic is overlapped at the mini-slot timescale by adopting the non-orthogonal multiple access (NOMA) superposition technique. The suitable pairs of eMBB, URLLC users are evaluated using the one-one matching theory by inculcating fairness among eMBB users while satisfying the QoS parameters of URLCC users. The significance of the proposed approach over the baseline approaches is evaluated through numerical simulations in terms of the MEAR, mixed numerologies, and fairness scores among eMBB users. [ABSTRACT FROM AUTHOR]

In this paper, we propose a physical layer secure transmission technology based on multi-components for multiple-input single-output (MISO) systems. The scheme mainly realizes physical layer security (PLS) in General Multi-Fractional Fourier Transform (GMFRFT) signal domain, which is different from traditional schemes that rely on the spatial domain. Different GMFRFT components are transmitted by multiple antennas at the transmitter, then the legitimate receiver and the eavesdropper receive the superposition of multiple non-orthogonal components. The resulting mutual interference will reduce the signal-to-interference and noise (SINR) at the eavesdropper, but without affecting the legitimate receiver. Because the legitimate receiver receives the non-contaminated GMFRFT signal due to designing multi-components at the transmitter, while the eavesdropper receives the signal whose constraint relations in the GMFRFT signal domain are destroyed, which will cause the energy loss of the information bearing signal. Furthermore, in order to achieve the maximum secrecy capacity, the selection method of GMFRFT transform order is given to adjust the power allocation among GMFRFT components. Compared with the scheme based on artificial noise (AN), the advantages of our scheme are: 1) our scheme can further reduce the capacity of wiretap channel while not requiring the transmitter to use partial power to transmit meaningless artificial noise signals; 2) our scheme outperforms AN-based schemes when the available spatial degrees of freedom are limited. Simulation results are provided to demonstrate the secrecy performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a differential pricing scheme, named as Diff-Price, to deliver customized safety services to the users at an optimal price and maximizing the profit of Safety Service Provider (SSP) using the Safety-as-a-Service (Safe-aaS) platform. None of the existing pricing schemes addresses the dynamic variation of price as the type of users and decision parameters requested by them vary. We assume road transportation as the implementation scenario of Safe-aaS. In order to deal with these issues, we formulate a pricing scheme, which renders different prices for the same decision parameters. The amount charged from the users are based on their type, decision parameters selected by them, and the duration for which they request the service. Additionally, the SSP allows suitable discounted price to the users, which is determined from their mode of payment and history of services availed. Further, to model the interaction between the users and SSPs, we design our problem as a multiple leader multiple follower non-cooperative Stackelberg game. We calculate the utility of SSPs and formulate an optimization problem to compute the optimal value of the basic price charged from the users. The exhaustive mathematical analysis and simulation results reveal that the proposed pricing scheme, Diff-Price improves the financial gain of the service providers as the number of users increases by 13.51%, 15.75%, and 14.173% respectively, compared to the adaptive incremental spot pricing scheme [1], differentiated cloud virtual-network as-a-service scheme [2], and smart road pricing system [3]. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-part collaborative task offloading with multiple servers in mobile edge computing (MEC) systems by considering server overload and long-term system performance. Our goal is to achieve the optimal user experience within the range of the network operator’s affordable cost. To this end, we design a two-layer computation offloading framework based on the multi-part offloading mode and the collaborations among small cell base station (SBS) servers. Furthermore, a multi-object constrained optimization problem is formulated, which jointly optimizes user association, channel allocation, and multi-part collaborative task offloading. To solve this problem, we propose a Genetic and Deep Deterministic Policy Gradient (GADDPG)-based computation offloading scheme, where the long-term system performance is considered. Numerical and simulation results demonstrate that the proposed GADDPG-based computation offloading scheme outperforms other methods in effectively solving the server overload and guarantees long-term system performances while ensuring the best user experience. [ABSTRACT FROM AUTHOR]

Dynamic topology, fast-changing channels and the time sensitivity of safety-related services present challenges to the status quo of resource allocation for cellular-underlaying vehicle-to-vehicle (V2V) communications. In this paper, we investigate a novel federated multi-agent deep reinforcement learning (FedMARL) approach for the decentralized joint optimization of channel selection and power control for V2V communication. The approach takes advantage of both deep reinforcement learning (DRL) and federated learning (FL), satisfying the reliability and delay requirements of V2V communication while maximizing the transmit rates of cellular links. Specifically, we elaborately construct individual V2V agent implement by the dueling double deep Q-network (D3QN), and design the reward function to train V2V agents collaboratively. As a result, each agent individually optimizes channel selection and power level based on its local observations, including the instantaneous channel state information (CSI) of corresponding V2V link, the instantaneous co-channel interference from the cellular link, the previous channels selections of nearby V2V pairs, and the queue backlog at the V2V transmitter. Another important aspect is that we incorporate FL to alleviate the training instability problem induced by cooperative multi-agent environment. The local DRL models of different V2V agents are federated periodically, addressing the limitations of partial observability on the entire network status for individual agent, and accelerating the training process of multi-agent learning. Validated via simulations, the proposed FedMARL scheme shows superiority to the baselines in terms of the cellular sum-rate and the V2V packet delivery rate. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are widely deployed to enhance the wireless network capacity and to provide communication services to mobile users beyond the infrastructure coverage. Recently, with the help of a promising technology called network virtualization, multiple service providers (SPs) can share the infrastructures and wireless resources owned by the mobile network operators (MNOs). Then, they provide specific services to their mobile users using the resources obtained from MNOs. However, wireless resource sharing among SPs is challenging as each SP wants to maximize their utility/profit selfishly while satisfying the QoS requirement of their mobile users. Therefore, in this paper, we propose joint users association and wireless resource sharing problem in the cell-free UAVs-assisted wireless networks with the objective of maximizing the total network utility of the SPs while ensuring QoS constraints of their mobile users and the resource constraints of the UAVs deployed by MNOs. To solve the proposed mixed-integer non-convex problem, we decompose the proposed problem into two subproblems: users association, and resource sharing problems. Then, a two-sided matching algorithm is deployed in order to solve users association problem. We further deploy the whale optimization and Lagrangian relaxation algorithms to solve the resource sharing problem. Finally, extensive numerical results are provided in order to show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of an intelligent reflective surface (IRS) assisted non-orthogonal multiple access (NOMA) system, where the IRS is mounted on an unmanned aerial vehicle (UAV) to assist the transmissions from a base station (BS). The BS utilizes the UAV as a relay to serve multiple user equipments (UEs) on the ground. In addition to the IRS relaying links, we also incorporate direct non line-of-sight links between the BS and UEs. We derive the outage probability for this system configuration. Moreover, we obtain a bound of the ergodic spectral efficiency to extract various useful insights. Furthermore, we also compare the performance of the proposed system design against the baseline scheme. Finally, we present numerical results to highlight the benefits of the proposed IRS-aided UAV relaying system and the accuracy of the derived analytical results. [ABSTRACT FROM AUTHOR]

Efficient spectrum management has always been an important issue due to the scarcity of satellite spectral resource, especially with the ever-increasing broadband demand. This paper proposes a market-driven technique to improve spectrum efficiency. In practice, spectrum resources are typically allocated in bulk to terrestrial agents which in turn will resell the bandwidth to end users of satellite communications. This paper explores the important role of terrestrial agents which serve as spectrum sales agents in satellite communications systems. The proposed approach aims to provide an incentive scheme for the agents to participate in the spectrum optimization process so as to result in maximizing the benefit of the agents as well as the satellite systems. We propose a dynamically optimal cooperation scheme between terrestrial agents and satellite systems, which is based on a stochastic process and optimal contract principle. By taking into consideration the satellite system's marginal cost related to transmission capacity in the given bandwidth, terrestrial agent's effort and the impact of market volatility, we designed an optimal incentive model which allows the satellite systems to determine a threshold value for paying sales commission to terrestrial agents or triggering contract termination if spectrum utilization is inefficient. Numerical results are presented to evaluate the performances of satellite systems’ profits in changing spectrum market and agency cost undertaken by satellite systems. [ABSTRACT FROM AUTHOR]

From the fourth generation (4G) of cellular systems onward, wireless personal devices (WPDs) support multi-input multi-output (MIMO) communication. However, the impact of MIMO communication on the electromagnetic field (EMF) of WPD users has yet to be fully understood and analyzed at the system level. In this paper, we first provide a generic model for assessing the individual exposure dose of multi-antenna WPD users in a multi-user multi-carrier communication system. An optimization framework for minimizing this exposure dose is then developed based on our exposure model. This framework helps us to identify a new criterion, i.e., the ratio between the normalized exposure dose and the channel to noise ratio (CNR), as the main system level criterion for minimizing the individual exposure dose of multi-antenna WPD users. This criterion is further integrated in the design of two novel centralized resource allocation schemes that take advantage of the multiple antennas at the WPD to minimize the per-user exposure dose, when full or limited knowledge of each user channel is available. Our new schemes can significantly reduce the individual exposure dose of WPD users (by approximately 80%) in comparison with the most relevant existing resource allocation schemes. Our results also provide insights into the logarithmic relationship between the per-user exposure dose and the number of receive antennas (or the number of time slots), and how such a parameter can be exploited to further reduce the exposure and/or provide a higher SE while maintaining a low exposure dose. [ABSTRACT FROM AUTHOR]

This paper proposes a massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system based on cognitive radio (CR) networks. To reduce the users interference, we propose an efficient user clustering algorithm. Within a cluster, we consider the user with the weak channel gain as a primary user (PU) and the user with the strong channel gain as a secondary user (SU). Given the quality of service for both PUs and SUs, we propose a power allocation (PA) method that maximizes the sum rate. The PA optimization problem is discussed into three scenarios which depend on the available transmit power. Thereafter, we derive a robust solution for the PA issue under imperfect channel state information (CSI) assumptions. Simulation results demonstrate that our proposed NOMA-CR scheme can significantly improve the spectral efficiency (SE). In addition, the proposed robust PA scheme for the imperfect CSI case reduces the performance loss due to the channel uncertainty. [ABSTRACT FROM AUTHOR]

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them. Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects. [ABSTRACT FROM AUTHOR]

Due to the forecasted fast increasing cellular traffic and the already highly congested licensed spectrum, it is critical to exploit and utilize the unlicensed spectrum resources for the fifth-generation (5G) and beyond networks. A challenging problem is the coexistence of 5G and other networks with fair, reliable, and efficient sharing of the unlicensed spectrum. In this paper, we propose a blockchain-enhanced distributed spectrum sharing scheme for coexisting multiple operators and multiple WiFi APs. We design a novel lightweight and efficient consensus mechanism, named Proof of Strategy (PoG). In this consensus mechanism, the problem of spectrum sharing is used as a consensus puzzle, and the part of the unlicensed spectrum is used as the ‘fee’ of miners. With such a design, the computing overhead of the consensus process is expected to be reduced significantly. We develop a non-cooperative game to analyze the behavior of the miners and obtain a symmetric Bayesian Nash equilibrium under the uniform distribution of mining cost estimation. It can be found mathematically and experimentally that the strategy of the winner tends to maximize the system revenue by sharing the unlicensed spectrum resource. Furthermore, to reduce the impact of heavy interactions on system throughput, the operation of WiFi APs in the proposed scheme can be adaptively switched between ‘contention mode’ and ‘blockchain mode’ according to the network traffic load. The dynamic behavior is constructed as an evolutionary game, and the existence and uniqueness of equilibrium points are proved by theoretical analysis. Simulations demonstrated the fairness and effectiveness of the proposed blockchain-based scheme and the mode switching method for distributed spectrum sharing by heterogeneous wireless networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure computation offloading under the eavesdropping-attack, in which a malicious node overhears the edge-computing user's (EU’s) offloading transmission to the edge-computing server (ES), and NOMA is used for the EU's offloading and meanwhile for providing artificial jamming to the eavesdropper. Since multi-user simultaneous transmission over a same frequency channel can be enabled by NOMA, a wireless user (WU) can form a NOMA pair with the EU to provide cooperative jamming to the eavesdropper while also gaining an opportunity of sending its data. Focusing on the EU-WU pair with the fixed WU's energy-provisioning, we exploit the physical layer security to quantify the EU's offloading throughput with the help of WU's jamming. We then study the joint optimization of the EU's computation offloading and the EU-WU's NOMA transmission for minimizing the EU's total energy consumption subject to its latency-requirement in completing the computation-task. By utilizing the feature of analytical solution of the WU's transmission, we then investigate the WU's optimal energy-provisioning for the EU-WU pair, such that both the EU and WU can benefit from the cooperative jamming in a fairness manner. Specifically, we formulate the EU-WU's cooperation as a Nash bargaining game. By identifying the monotonic feature of Nash bargaining problem, we propose a polyblock approximation based algorithm for determining the WU's optimal energy-provisioning to achieve the win-win solution for the paired EU and WU. Finally, we investigate the scenario of multiple EUs and WUs, and aim at finding the stable pairing between the EUs and WUs, such that no individual EU (or WU) would like to change its partner. An efficient algorithm, which is based on the Gale-Shapley theory while exploiting the quantitative feature of EUs’ and WUs’ net-rewards, is proposed to achieve the stable EU-WU pairings. Numerical results are provided to validate our proposed algorithms and demonstrate the advantage of our proposed NOMA assisted computation offloading via cooperative jamming. [ABSTRACT FROM AUTHOR]

The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the DFRC signals to sense the vehicles’ position state information (PSI), and communicates with the vehicles based on PSI. The objective of this paper is to minimize the maximum communication delay among all vehicles by considering the estimation accuracy constraint of the vehicles’ PSI and the transmit power constraint of RSU. By leveraging convex optimization theory, two iterative power allocation algorithms are proposed with different complexities and applicable scenarios. Simulation results indicate that the proposed power allocation algorithm converges and can significantly reduce the maximum transmit delay among vehicles compared with other schemes. [ABSTRACT FROM AUTHOR]

Vehicular networks are envisioned to deliver data transmission services ubiquitously, especially in the upcoming autonomous driving era. Accordingly, the high data traffic load poses a heavy burden to the terrestrial network infrastructure. Unmanned Aerial Vehicles (UAVs) show enormous potential to assist vehicular networks in providing services. In a dense UAV-assisted vehicular network with a large number of users, spectrum sharing is leveraged for alleviating the spectrum scarcity. However, the increasing data traffic still leads to the UAV energy consumption problem. This paper considers a specific network scenario where a UAV transmits its cached content files to vehicular users over UAV-to-vehicle (U2V) links while vehicle-to-vehicle (V2V) links reuse the U2V spectrum for safety-critical message exchanges. To improve the UAV's energy efficiency while guaranteeing users’ quality of service (QoS), we jointly optimize content placement, spectrum allocation, co-channel link pairing, and power control, which are the key factors affecting energy efficiency and QoS. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by combining Hungarian and DDQN. We perform system performance evaluations, demonstrating that our approach can not only improve the UAV's energy efficiency while satisfying the users’ QoS requirements but also increase the timeliness of making decisions. [ABSTRACT FROM AUTHOR]

In this paper, we propose one joint interference alignment and subchannel allocation (JIARA) interference management scheme, aiming at maximizing the number of satisfactory users with different degrees of freedom (DoF) demand in ultra-dense wireless networks, while the existing works only consider the same DoF demands. The original JIARA scheme is equivalent to the maximum $K$ -colorable subgraph (MKCS) problem in graph theory for a given $K$ -colors and the multicoloring demand at each vertex, hence NP-hard. Therefore, we propose one graph theory based low complexity sub-optimal JIARA scheme. Firstly, we propose one virtual allocation of subchannel based scheme to construct transformed the conflict graph, which is applicable for the different DoF demands and different size of the feasible IA group. Furthermore, we propose one greedy multi-coloring based algorithm to determine the maximum number of satisfactory users. Simulation results show that the proposed JIARA scheme outperforms the subchannel resource allocation only scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient resource allocation problem in device-to-device (D2D) communications underlaying unmanned aerial vehicle (UAV)-enabled networks. The UAV is deployed as a flying base station to communicate with wireless users in the presence of an eavesdropper in the cell. We consider two types of users: the ground users (GUs) served by the UAV and the D2D users that communicate directly with one another. Our aim is to maximize the total energy efficiency (TEE) of all D2D pairs while guaranteeing the quality of service (QoS) requirements and secrecy rates of all GUs and D2D users via joint power control and channel allocation. The considered TEE maximization problem is a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve. Therefore, we propose a method that consists of outer and inner loops. In the outer loop, Dinkelbach's algorithm is utilized to transform the original fractional programming problem into a subtractive form. In the inner loop, we employ the alternating optimization method and divide the equivalent optimization problem into two sub-problems: power allocation and channel allocation. Solving the two sub-problems directly using standard convex optimization software may have high complexity. Therefore, we also propose a low-complexity algorithm using the Lagrangian dual and Kuhn—Munkres algorithm to obtain the optimal power allocation in closed-form and the optimal channel allocation, respectively. Simulation results show that the proposed algorithm converges in a small number of iterations. Furthermore, the proposed approach shows its superior performance compared with other benchmark methods. [ABSTRACT FROM AUTHOR]