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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]

In this paper, we investigate security and energy efficiency of multicast communication in cognitive satellite terrestrial networks. To achieve high spectrum efficiency and low interference power transmissions in the cognitive terrestrial network, the rate splitting multiple access scheme (RSMA) is employed to prompt the massive access of the secondary users. In particular, we derive analytical and asymptotic closed-form expressions for secrecy outage probability of the satellite terrestrial network with eavesdropping, interfering, and imperfect channel state information. Hence, a beamforming (BF) scheme based on RSMA for secondary network is proposed to suppress eavesdropping, mitigate interference, and enhance energy efficiency of the proposed system model. Then, we transform non-convex optimization constraints into resoluble problems utilizing Taylor series expansion and successive convex approximation. Finally, simulation results corroborate the above theoretical analysis and highlight the superiority of the designed BF scheme on security and energy efficiency. [ABSTRACT FROM AUTHOR]

As millimeter wave (mmWave) technology becomes more integrated into today's communication infrastructure, its application to mobile scenarios has attracted considerable interests. In particular, mmWave technology is one of the leading candidates in Vehicle-to-Everything (V2X) community to address the ever-rising data exchange demand from vehicles. However, the inherent directionality of the technology poses challenges to peer discovery and transmission scheduling in typical V2X scenarios, causing significant loss of network capacity. To mitigate this issue, we propose a novel sub-6 GHz V2X-assisted synchronous mmWave communication scheduler in this paper. The sub-6 GHz channel serves as control channel for mmWave scheduling. The scheduler carefully considers data importance and freshness in determining communication schedule, such that network utility across mmWave links is maximized. We formulate the scheduling problem into an integer linear programming (ILP) problem. However, ILP problem is NP-hard and has exponential computational complexity for the worst cases. We then develop a scheduling algorithm, maximum weight matching based iterative algorithm (MWMIA), that operates in polynomial time with performance guarantees for the NP-hard ILP, by leveraging structural properties of link conflicts. Comparative simulation studies verify the effectiveness of our scheduler against other solutions. [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]

The proliferation of IEEE 802.11 or WiFi networks, and the explosive growth in traffic demands call for solutions to maximize the capacity of WiFi networks. Hence, maximizing the spatial reuse of WiFi networks is critical as doing so allows multiple concurrent transmissions. In this respect, a critical network parameter, Clear Channel Assessment (CCA) threshold, plays a vital role as it dictates whether a node is allowed to transmit after sensing the channel. In this paper, we propose to use Deep Q-network (DQN) under two learning patterns to select the CCA threshold of devices. We further consider Transmit Power Control (TPC) in conjunction with CCA threshold selection to improve the capacity of a WiFi network. The simulation results show that our approach is capable of selecting the optimal CCA threshold for each device. As a result, the average throughput is 62.4% higher than that of a legacy Dynamic Sensitivity Control (DSC) algorithm. [ABSTRACT FROM AUTHOR]

Mission-critical communications (MCC) involve all communications between people in charge of the safety of the civil society. MCC have unique requirements that include improved reliability, security and group communication support. In this paper, we propose secure and robust Multiple-Input-Multiple-Output (MIMO) transceivers, designed for multiple Base Stations (BS) supporting multicast MCC in presence of multiple eavesdroppers. We formulate minimization problems with the Sum-Mean-Square-Error (SMSE) at legitimate users as an objective function, and a lower bound for the MSE at eavesdroppers as a constraint. Security is achieved thanks to physical layer security mechanisms, namely MIMO beamforming and Artificial Noise (AN). Reliability is achieved by designing a system which is robust to two types of channel state information errors: stochastic and norm-bounded. We propose a coordinate descent-based algorithm and a worst-case iterative algorithm to solve these problems. Numerical results at physical layer and system level reveal the crucial role of robust designs for reliable MCC. We show the interest of both robust design and AN to improve the security gap. We also show that full BS cooperation in preferred for highly secured and reliable MCC but dynamic clustering allows to trade-off security and reliability against capacity. [ABSTRACT FROM AUTHOR]

Backscatter communication is an energy-efficient communication technique for the Internet of things (IoT) devices. It enables data transmission by reflecting the incident radio signals. In this paper, we propose a communication mode selection scheme for IoT devices that can communicate using either active transmission or backscattering. In the active transmission mode, the IoT devices can transmit data over narrowband subcarriers using power-domain non-orthogonal multiple access (NOMA). In the backscattering mode, which operates over shorter distance than active transmission, nearby user equipment (UE) devices are used as relays. The UEs receive the backscattered signals from the IoT devices and forward them to the base station. We formulate a connection density maximization problem to select the communication mode used by each IoT device. We determine the IoT device pairing for active transmission mode with NOMA and UE-IoT device association for backscattering mode. The formulated problem is a binary integer programming problem. Although it can be solved optimally, the optimal algorithm incurs exponential computational complexity. Hence, we propose a low-complexity suboptimal algorithm to solve this problem. Results show that our proposed algorithm can enhance the connection density of narrowband IoT systems by up to 64% when compared with using single communication mode. [ABSTRACT FROM AUTHOR]

In this paper, we give performance analysis in an unmanned-aerial-vehicle (UAV) assisted ground short-packet communication system, which adopts a UAV as a wireless relay to accomplish ultra-reliable and low-latency communications (URLLC) between a base station and a user equipment (UE). Both large-scale fading, which is modeled as the free space path loss or the classic 3-Dimensional channel, and small-scale fading, which follows Nakagami- $m$ distribution, are considered in our system model. We first derive the closed-form expressions for the average overall block error ratio (BLER) at the UE by adopting Gasssian-Legendre quadrature (GLQ) method. Then, under the reliability and latency constraints at the UE, we apply both bisection search and one-dimensional search algorithms to jointly optimize blocklength, transmit power and UAV flying height for maximizing the energy-efficiency (EE) of UAV. Finally, Monte-Carlo simulations are provided to verify our analytical results. [ABSTRACT FROM AUTHOR]

This paper studies a scheduling problem in decentralized coded caching considering sequential and random request arrivals. The asynchrony between the requests begets the question of when to launch network-coded multicast to serve the requests when both the energy efficiency and the delay penalty are of concern (one can be traded for the other). To attain an optimal energy-delay tradeoff, we formulate this scheduling problem as an optimal stopping problem. In particular, for either the hard deadline or the linear penalty functions, we derive the structure of an optimal stopping rule and propose an iterative method to approximate the optimal rule. Simulation results demonstrate the effectiveness of the derived optimal stopping rules under various conditions, comparing them with several baseline scheduling strategies. [ABSTRACT FROM AUTHOR]

M2M communication in the LTE network is gaining attention with a growing number of connected devices and adaptation of new emerging technologies. Usually, the traffic generated by M2M devices is heterogeneous in terms of packet size, intensity, strict delay, and throughput requirements. The M2M traffic generally flows in the uplink direction. It imposes several challenges in designing scheduling for uplink in the LTE network. The research community proposed various solutions regarding the QoS handling in M2M communications. Allocating the resources based on the QoS of machine type communications (MTCs) has a challenge in deciding on devices’ priority. This work applies game theory to control devices’ strategic behavior claiming a false priority. In this paper, a Quality-of-Service aware uplink packet scheduling scheme is proposed using a combinatorial game. The packet scheduling problem is modeled as an Auction game that handles the scheduling in both Time-Domain and Frequency-Domain. The packet scheduler uses the QoS requirement as an allocation metric, and a power control mechanism is applied to control the devices’ strategic behavior. Simulation is performed in MATLAB R2019b. The performance of the proposed schemes is evaluated in terms of the total system utility in QoS satisfaction against standard schedulers like Round Robin and Proportional Fair schedulers and some other schedulers proposed in the literature. [ABSTRACT FROM AUTHOR]

To mitigate the impact of the dramatic growth of wireless video traffic and provide users with heterogeneous video services, a collaborative caching-transmission method in cache-enabled terahertz (THz) heterogeneous networks is studied in this paper. We propose a novel scalable video coding (SVC)-based two-tier cooperative caching strategy that can provide video services with different perceptual qualities. In addition, we propose a hierarchical hybrid transmission scheme where users obtain the requested layer files through the cooperative cellular multicast-D2D and THz transmissions. A base layer-based multicast group aggregation process is presented to improve resource utilization, while an efficient multicast transmission resource scheduling scheme can make better use of the spatial reusability. We derive the expressions for the average offloading capacity gain and average energy consumption, and formulate an efficiency function optimization problem. The problem is efficiently solved by the greedy strategy-gradient descent based caching solution and long tail distribution based heuristic caching solution. Simulation results validate the two solutions and demonstrate that the proposed caching-transmission method can achieve quasi-optimal performance compared with other contrasting schemes. [ABSTRACT FROM AUTHOR]

The emergence of new radios (NRs) including mmWave in 5G necessitates multi-connectivity to enable a robust control plane as well as a faster user plane to satisfy stringent mobile service requirements. In multi-connectivity, a mobile user equipment (UE) communicates with multiple base stations simultaneously, where each base station provides a cell group as radio resources to the UE. In this paper, we design an online control policy for downlink transmission in a multi-connectivity environment. Our control policy aims to split incoming traffic of packets into multiple base stations and jointly determine the state of each cell group. Our design constraints include periodical traffic splitting and distributed cell group state decisions subject to limited cell group decision opportunities. Designing control policies that meet these constraints poses significant challenges. By applying the Lyapunov optimization method, we develop a joint control algorithm for real-time traffic splitting and cell group state decisions to satisfy the imposed constraints. Our proposed algorithms are robust and simple enough to be implemented in a real network. We confirm through analysis and simulation that our algorithm achieves optimal performance and outperforms other competitive schemes in terms of the balance between power consumption and queue length. [ABSTRACT FROM AUTHOR]

This paper studies joint cache placement and cooperative multicast beamforming to provide content-centric data services for mobile users in the integrated satellite-terrestrial network (ISTN). Specifically, in the ISTN, users requesting the same content are arranged into a multicast group and served by the cache-enabled base stations (BSs) and low earth orbit (LEO) satellite via cooperative beamforming. To maximize the network utility that takes network throughput and backhaul traffic into consideration, the cache placement, LEO satellite and BS clustering, and multicast beamforming are jointly designed and formulated as a two-timescale optimization problem. However, the original problem is anti-causal since the cache placement strategy and content delivery policy are coupled in different timescales. By utilizing historical information, we propose a two-step scheme to decompose the problem into a short-term content delivery subproblem and a long-term cache placement subproblem. As the former subproblem is nonconvex with mixed-integer variables and coupling constraints, we transform it into an equivalent problem and propose a penalty concave-convex procedure based algorithm to solve it. To address the latter subproblem, a centralized iterative algorithm and a distributed alternating algorithm with low complexity are developed, respectively. Simulation results validate that the proposed schemes can effectively enhance the network throughput and reduce the backhaul traffic compared with benchmark scheme. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is deemed a promising technique for future wireless and mobile technologies due to its ability to reconfigure the radio propagation environment. In this paper, we analyze the physical layer security (PLS) of a downlink non-orthogonal multiple access (NOMA) network through an IRS intended for assisting the wireless transmission, taking into account the external and internal eavesdropping scenarios. Considering the hardware limitations, a 1-bit coding scheme is utilized to investigate the secrecy performance of the IRS-aided NOMA (IRS-NOMA) network. Exact and asymptotic expressions in terms of the secrecy outage probability (SOP) and the effective secrecy throughput (EST) are presented, which are in consideration of the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC). Based on the analytical results, the secrecy diversity orders are related to the number of reflecting elements and EST ceilings are observed in the high SNR regime. Monte Carlo simulations are presented to corroborate the correctness of the analyses and illustrate that the IRS-NOMA scheme is superior in the secrecy outage performance to the IRS-aided orthogonal multiple access scheme. In addition, the SOP performance enhancement through the increased number of reflecting elements and the improvement of EST through proper target secrecy rate are also evidenced by the simulations. [ABSTRACT FROM AUTHOR]

In this paper, we develop centralized and distributed dynamic resource allocation schemes for an intelligent reflecting surface (IRS) aided energy harvesting (EH) system that optimize transmit power of a source node and phase-shift of passive reflecting elements of IRS. The source node randomly harvests renewable energy from the surrounding environment and performs data transmission with the harvested energy while satisfying statistical packet delay constraints in terms of maximum acceptable delay-outage probability. Our developed schemes do not require the statistical distributions of channel and energy profiles to be known and apply deep reinforcement learning (DRL) algorithm. Simulation results demonstrate the effectiveness of the proposed resource control scheme for IRS-aided EH system in different channel and energy conditions. [ABSTRACT FROM AUTHOR]

Massive Machine-Type Communication (mMTC) is proposed by ITU to provide a large scale of connectivity services for billions of machine-type communications devices (MTCDs) via cellular networks. However, MTCDs are generally energy-constrained, security capability limited and short-packet transmission dominated. It has great challenges for massive MTCDs to perform short-packet transmission simultaneously while guaranteeing the confidentiality and energy efficiency of the information transmission. In this paper, we investigate the energy-efficient secure short-packet transmission of Non-Orthogonal Multiple Access (NOMA) assisted mMTC networks, in which MTCDs aim to transmit secrecy messages to the destination base station via trusted relays with a passive eavesdropper present. To realize secure, reliable and energy-efficient transmission, we maximize the secrecy energy efficiency (SEE) by implementing joint relay and power level selection. Furthermore, we formulate a decentralized stateless Q-learning-based multi-agent reinforcement learning (MARL) algorithm for the dynamic and complex mMTC systems. The simulation results show that adopting the proposed decentralized MARL algorithm and relaying with NOMA can improve the performance of SEE while reducing information exchange overhead. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communications is an important technology in vehicular ad hoc network (VANET) to support autonomous data exchange among vehicles. Multiple V2V communications modes have been investigated for VANET, including dedicated short-range communications (DSRC) based on IEEE 802.11p and LTE-V2X, which are suitable for different packet transmission cases. In order to fully exploit the strengths of various modes, hybrid V2V communications strategies are designed in this paper, where each vehicle is allowed to choose different modes for different kinds of transmissions in separate frequency bands for transmission throughput improvement. Furthermore, since it is usually impractical to decide all the vehicles’ communications strategies globally due to high computational complexity and heavy overhead to broadcast the decisions, we model the selection of hybrid V2V communications strategies for vehicles as an evolutionary game. A strategy selection algorithm is then proposed, where each vehicle can select its hybrid V2V communications strategy locally based on its evaluation of the payoffs for different strategies and limited signalling from the base station. Simulation results demonstrate that the proposed algorithm can converge to an asymptotic stable state, which can improve the transmission throughput of vehicles, and is robust to the slight evaluation errors of payoffs and the strategy mutations of a few vehicles. [ABSTRACT FROM AUTHOR]

The integrated satellite-terrestrial network (ISN) is an emerging framework that complements the ground 5G network with global network access. The satellite layer consists of Low Earth Orbit (LEO) and Geosynchronous (GEO) satellites for long-distance and high-throughput data transmission with low latency. However, traditional ground routing strategies are not appropriate in ISN, considering the dynamic motion and different capabilities of satellites. Moreover, multi-type tasks need to be allocated with optimal inter-satellite transmission links (ISL) to ensure both the load balance of ISN and the quality of experience (QoE) of subscribers. In this paper, we combine the Software Defined Networking (SDN), AI technique, and fuzzy logic to optimize multi-task routing in ISN. The GEO satellites and the ground computing center (GCC) are set as the joint control plane. The load information of ISN at different times is collected by GEO controllers and made into a multi-D matrix. The GCC collects the historical traffic data from GEO controllers for convolutional neural network (CNN) model training and updating. The GEO satellites utilize the trained CNN model to make path allocation decisions and issue flow tables to LEO satellites. Further, considering the CNN judgment may contradict the user QoE, we use fuzzy logic to evaluate task requirements to improve the CNN output for optimal ISN path allocation. The simulation results demonstrate that the fuzzy-CNN based multi-task routing (FCMR) method has better traffic control performance and path finding flexibility under different ISN conditions. [ABSTRACT FROM AUTHOR]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

This paper investigates rate splitting multiple access (RSMA) for multigroup multicast beamforming in a cache-enabled cloud radio access network (C $^2$ -RAN). To maximize the minimum weighted rate among all users and ensure the base station (BS) transmit power and backhaul capacity constraints, we jointly optimize the dynamic BS clustering, message splitting, and beamforming vectors. More specifically, users requesting the same content are collected to a multicast group and served by a cluster of BSs. Each BS has limited storage space and connects to the central processor (CP) via a backhaul link. However, the resulting optimization problem is discrete and nonconvex and has a non-smooth objective function. To alleviate these problems, we construct two surrogates to approximate the objective function locally. These surrogates are strictly lower bounds and concave. Also, we build an upper-bound convex surrogate function to handle the backhaul cost constraint and derive a minorization-maximization (MM)-based iterative algorithm. Besides, we adopt a quadratic transform method to recast the achievable rate and binary BS selection problems to a more tractable form to reduce time cost. We transform the resulting problem into a two-tier alternating optimization problem; the outer-tier problem is handled with closed-form expressions while the inner-tier one is solved by a convex framework. We thus develop a quadratic transform-based alternating (QTA) algorithm. Extensive simulation results confirm the superiority of our proposed transmit strategy and algorithms. [ABSTRACT FROM AUTHOR]

Massive Machine-type Communications (mMTC) is one of the principal features of the 5th Generation and beyond (5G+) mobile network services. Due to sparse but synchronous MTC nature, a large number of devices tend to access a base station simultaneously for transmitting data, leading to congestion. To accommodate a large number of simultaneous arrivals in mMTC, efficient congestion control techniques like access class barring (ACB) are incorporated in LTE-A random access. ACB introduces access delay which may not be acceptable in delay-constrained scenarios, such as, eHealth, self-driven vehicles, and smart grid applications. In such scenarios, MTC devices may be forced to drop packets that exceed their delay budget, leading to a decreased system throughput. To this end, in this paper a novel delay-aware priority access classification (DPAC) based ACB is proposed, where the MTC devices having packets with lesser leftover delay budget are given higher priority in ACB. A reinforcement learning (RL) aided framework, called DPAC-RL, is also proposed for online learning of DPAC model parameters. Simulation studies show that the proposed scheme increases successful preamble transmissions by up to $75 \%$ while ensuring that the access delay is well within the delay budget. [ABSTRACT FROM AUTHOR]

Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the design and analysis of the Analog Fountain Code (AFC) for short packet communications. We first propose a density evolution (DE) based framework, which tracks the evolution of the probability density function of the messages exchanged between variable and check nodes of AFC in the belief propagation decoder. Using the proposed DE framework, we formulate an optimisation problem to find the optimal AFC code parameters, including the weight-set, which minimises the bit error rate at a given signal-to-noise ratio (SNR). Our results show the superiority of our AFC code design compared to existing designs of AFC in the literature and thus the validity of the proposed DE framework in the asymptotically long block length regime. We then focus on selecting the precoder to improve the performance of AFC at short block lengths. Simulation results show that lower precode rates obtain better realised rates over a wide SNR range for short information block lengths. We also discuss the complexity of the AFC decoder and propose a threshold-based decoder to reduce the complexity. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV)-based 5G networks deliver specific services to a end-users through the creation of Service Function Chains (SFCs), which are composed of a number of Virtualized Network Functions (VNFs) interconnected via a set of virtual links (vLinks). VNFs consume computational resources of the network's servers, while vLinks utilize the communicational resources of the network. The efficient utilization of network resources remains a challenge in NFV-based networks. To this end, in this paper, we propose an analytical framework for the Admission Ratio (AR) calculation in NFV-based converged optical-wireless 5G networks. The proposed methodology employs a network slicing architecture in which different network slices form end-to-end logically isolated networks and each slice delivers a specific service type to the users through its SFC(s). In the proposed analysis, we not only take into account the occupancy distribution in both the network's computational and communicational domains (servers and fiber links), but we also consider the SFC establishment AR by taking into account different sub-service-classes belonging to different slices. The accuracy of the model is evaluated through the comparison of analytical and simulation results and was found satisfactory. Furthermore, the proposed model is employed for the determination of the optimal (minimum) capacity for all SFCs elements (VNFs and vLinks), in a way that users belonging to a specific slice experience a predefined value of AR as minimum. Additionally, our calculations deploy recursive formulas, which have a low computational complexity, as opposed to time-consuming simulations, without requiring the application of complex optimization algorithms. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UW-ASNs) have received increasing attention in the civil and military fields. However, due to the unique features of the underwater environment, designing a suitable media access control (MAC) protocol for UW-ASNs is an immense challenge. While orthogonal frequency division multiple access (OFDMA) is a multiple access technology based on orthogonal frequency division multiplexing (OFDM), allocating different subchannels to different users for parallel transmission. In this paper, we propose a traffic load-aware OFDMA-based MAC protocol for UW-ASNs, called TLAO-MAC, that can suit the operational requirements of distributed underwater sensor networks. It uses the exchange of control packets to collect data transmission requests and update the channel resource list. Neighboring nodes rely on receiving nodes to conduct channel negotiation and resource allocation in a distributed manner. In addition, we define the channel busyness index to sense the dynamic traffic load in distributed networks. The proposed TLAO-MAC includes two key schemes: an adaptive channel grouping algorithm based on channel busyness, and a distributed subcarrier and power allocation algorithm. By adaptively adjusting the channel grouping in consideration of the dynamics of the traffic load, TLAO-MAC allows simultaneous transmission between neighboring nodes and avoids conflict in the data channel. Under the premise of meeting the transmission rate requirement of the sender, the transmission power is saved to the greatest extent. The simulation results show that TLAO-MAC can improve network goodput and reduce end-to-end delay, showing better adaptability in distributed networks. [ABSTRACT FROM AUTHOR]

Due to the inherent openness of wireless channels, data transmission in user-centric ultra-dense networks (UUDNs) is vulnerable to eavesdropping and malicious jamming attacks (EJA). This paper addresses the user-centric clustering, beamforming, and artificial noise design to combat with the EJA, and an accelerated deep reinforcement learning (DRL) based approach has been proposed for the downlink of UUDNs from a security, reliability and energy efficiency (SREE) perspective. In contrast to the existing approaches, the proposed approach divides the UE's communication behavior into association phase and transmission phase to perform a fast and adaptive response to the EJA. For the association phase, the proposed approach aims at achieving a fast response to EJA by optimization algorithms from a short-term perspective. For the transmission phase, we propose a DRL based algorithm to achieve optimal adaptive response by dealing with the dynamic and persistent effects of EJA from a long-term perspective. To accelerate the training process, fast response achieved in the association phase will be regarded as the initial experience to update the parameters of the deep neural networks adopted by the DRL based algorithm for transmission phase. Comparing with the existing methods, simulation results show that our proposed accelerated DRL approach achieves better performance in terms of SREE and convergence speed. [ABSTRACT FROM AUTHOR]

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible pilot assignment method to jointly optimize the uplink and downlink data transmission in multi-cell Massive multiple input multiple output (MIMO) systems with correlated Rayleigh fading channels. By utilizing a closed-form expression of the ergodic spectral efficiency (SE) achieved with maximum ratio processing, we formulate an optimization problem for maximizing the minimum weighted sum of the uplink and downlink SEs subject to the transmit powers and pilot assignment sets. This combinatiorial optimization problem is solved by two sequential algorithms: a heuristic pilot assignment is first proposed to obtain a good pilot reuse set and the data power control is then implemented. Numerical results manifest that the proposed algorithm converges fast to a better minimum sum SE per user than the algorithms in previous works. [ABSTRACT FROM AUTHOR]

Vehicular social networks (VSNs) are considered as an emerging class of vehicular ad hoc networks (VANETs) with social attributes. In contrast to the traditional randomly distributed human social networks, vehicles normally only move along fıxed paths. As such, information sharing and forwarding become challenging because of the high speed of vehicles, complicated propagation environment, and quickly changing network topologies. The social characteristics and human behavior features are used to improve the effıciency of information delivery among vehicles in VANETs. In this paper, we propose a dynamic periodic location encounter network (DPLEN) model that considering both time-varying and location-varying characteristics of vehicles. We calculate the line similarity for each encountered pair of vehicles based on the periodic time and location information of the encounter event, and analyze social attributes based on the network model, such as the Community structure, Betweenness Centrality and K-shell. We also use the similarity to design two routing protocols for unicast and multicast patterns. After testing the proposed model on empirically collected datasets of mobility traces, the results show that these two routing protocols are able to greatly improve the efficiency of information forwarding in the VSNs. [ABSTRACT FROM AUTHOR]

Billions of mobile sensing devices in Internet of Things (IoT) collect data from surrounding environment in which the Medium Access protocol is employed. Quorum-based protocols are most important medium access control protocol (MAC) for wake-up based devices. However, existing quorum-based protocols cannot achieve both low latency and long lifetime at the same time. In this paper, a novel Element Shift Quorum (ESQ) based MAC is proposed in the sensor based systems. Data transmission is usually launched from the periphery of the network through multi hop routing to the data collector (DC), thus the data operation time of devices are different based on the distance from the DC. In the ESQ protocol, the Quorum time slot (QTS) of sensing devices is scheduled in the time when the devices just have data to process, then the latency and energy consumption can be reduced. In this way, sufficient QTS are allocated for nodes when data operation is needed, and nodes can stay at sleeping state when no data forwarding is required, thus enhancing the energy efficiency and lowering the network delay. Theoretical analysis and experimental results show that, the lifetime is increased by 16% using the proposed method. Meanwhile, the latency is reduced by a factor between 9.08%-29.32%. [ABSTRACT FROM AUTHOR]

Network slicing is emerging as a promising solution for end-to-end resource management and orchestration together with Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. In this paper, a comprehensive network slicing framework is presented to achieve end-to-end (E2E) QoS provisioning among customized services in 5G-driven VANETs. The proposed scheme manages the cooperation of both RAN and Core Network (CN), using SDN, NFV and Edge Computing technologies. Furthermore, a dynamic radio resource slice optimization scheme is formulated mathematically, that handles a mixture of mission-critical and best effort traffic, by delivering the QoS provisioning of Ultra-reliability and low latency. The proposed scheme adjusts the optimal bandwidth slicing and dynamically adapts to instantaneous network load conditions in a way that a targeted performance is guaranteed. The problem is solved using a Genetic Algorithm (GA) and results are compared with the previously proposed 5 G VANET architecture. Simulation reveal that the proposed slicing framework is able to optimize resources and deliver on the key performance metrics for mission critical communication. [ABSTRACT FROM AUTHOR]

This paper focuses on the way to protect privacy of clients requesting datasets stored in data servers while keeping communication efficiency. To this end, we introduce a novel communication-efficient and privacy protecting framework termed crowded information acquisition (CIA), well suited to a large number of clients scenario. We investigate the CIA under various conditions addressing possible communication scenarios. Contrary to the conventional belief, the results claim that a large number of clients demanding private services can enhance the privacy protection while providing low latency services and generating a small amount of traffic. [ABSTRACT FROM AUTHOR]

Radio resource slicing is critical to customize service provisioning in fifth-generation (5G) uplink radio access networks (RANs). Using drone-small-cells (DSCs) as aerial support for terrestrial base stations can enhance the flexibility for resource provisioning in response to traffic distribution variations. In this paper, we study a multi-DSC-assisted radio resource slicing problem for 5G uplink RANs, with the objective of minimizing the total uplink resource consumption under differentiated quality-of-service (QoS) constraints for both human-type and machine-type communication services. We begin with an interference-aware graph model to formulate the joint DSC three-dimension (3D) placement and device-DSC association problem for uplink radio resource slicing and prove that the proposed problem is NP-hard. A complexity-adjustable problem approximation is presented via screening candidate DSC deployment positions, which incorporates flight height adaptation to balance the uplink communication coverage and resource utilization. A lightweight approximation using a fixed DSC flight altitude is also provided with reduced complexity. For mathematical traceability, the DSC placement and device-DSC associations in each approximation are transformed as a special weight clique problem. An upgraded clique algorithm is then developed to determine how to deploy DSCs for a given number of DSCs. Simulation results demonstrate the proposed scheme's effectiveness in terms of resource utilization, network coverage, and drone dispatching cost. [ABSTRACT FROM AUTHOR]

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [ABSTRACT FROM AUTHOR]