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Wireless resources, such as spectrum, computation, infrastructures and so on, are critical in 6G and beyond. Dynamic Resource Sharing (DRS), which improves the resource utilization compared to the static and fixed resource allocation, thus needs to be further exploited. Blockchain and AI are two promising techniques for DRS in 6G and beyond. In this article, a blockchain and AI-empow-ered DRS architecture is proposed, where block-chain is used to achieve the functionalities in DRS with improved distribution, security and automation, and AI is implemented to improve the performance of pattern recognition and decision-making in DRS. Finally, a case study where dynamic spectrum sharing is implemented within the proposed architecture, and deep reinforcement learning is used and shown to optimize the profit ratio of the users.

Symbiotic radio (SR) is a new paradigm of spectrum sharing that allows passive Internet-of-Things (IoT) device to transmit messages over the ambient RF signal. Considering that the reader needs to collect information from multiple IoT devices in practice, and the traditional multiple access schemes that require coordination among users are unaffordable for the passive devices, we propose a random code assisted multiple access scheme, in which each backscatter device (BD) independently chooses a random code to spread its signal and the incident signal is backscattered. To avoid the need of the instantaneous information, including the specific codes and the instantaneous channel state information of the backscatter links, and to optimize the transmit power and the BD reflection coefficients, we derive the asymptotic BD signal to interference plus noise ratio (SINR) by employing the large-dimension random matrix theory. We formulate the joint optimization problem to maximize the minimum asymptotic SINR among BDs with guaranteeing the primary target rate. By investigating the property of the problem, the original problem is transformed and solved by the proposed iterative-based algorithm. Extensive simulations are executed to verify our theoretical analysis and demonstrate the outperformance of the proposed algorithm.

Equivalent models of antennas are useful for the fast simulation of vehicle-to-everything (V2X) communication. The existing antenna-equivalent models are inflexible because they assume rectangular antenna contour. This article presents a hybrid-equivalent surface-edge current model to overcome the limitation of the existing equivalent models. Based on Huygens’ principle, a V2X communication antenna is equivalent to a conducting plate excited by equivalent surface magnetic current (ESMC). The sources of fields reflected by the conducting plate and diffracted by its edges are modeled as the image of ESMC and equivalent edge current (EEC), respectively. A hybrid-equivalent surface-edge current model is thus established, and it consists of ESMC, the image of ESMC, and EEC. The unknown sources in the proposed model are solved from near fields of the antennas, and the proposed model is then used to simulate the radiation performance of antennas installed on vehicles. The transmission coefficient between V2X communication antennas can also be calculated by using the proposed model and the electromagnetic reaction theorem. Simulations and experiments are performed to demonstrate the effectiveness of the proposed method. It is shown that the proposed equivalent model not only accurately models antennas with arbitrarily shaped contour but also significantly accelerates the integrated simulation of antennas and vehicles.

This letter considers a symbiotic radio (SR) system enhanced by a reconfigurable intelligent surface (RIS). The active transmit beamforming of the base station (BS) and the passive beamforming of the RIS are jointly designed to minimize the BS’s transmission power, subject to the signal-to-interference-plus-noise ratio constraints for decoding backscatter signal, and the rate constraint for primary communication. To solve the non-convex problem, we propose an efficient iterative algorithm based on alternating optimization and semi-definite relaxation. The algorithm’s convergence and complexity are analyzed. Numerical results show that the RIS-enhanced SR achieves lower transmission power than SR without RIS.

To support massive access for future wireless networks, we propose a novel reconfigurable intelligent surface (RIS)-enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system. In the proposed system, each RIS not only enhances the primary transmission from the primary transmitter (PT) to the associated primary receiver (PR) nearby, but also acts as an Internet-of-Things (IoT) device to enable IoT transmissions to the same PR. Therefore, each PR needs to jointly decode the information from both the PT and its corresponding RIS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each RIS under the maximum transmit power constraint at the PT and various constraints on the reflection coefficients (RCs), which include the ideal, continuous-phase and the discrete-phase cases. The formulated problem is non-convex, which cannot be solved directly. Thus, fractional programming (FP) method and alternating optimization (AO) technique are adopted to tackle the problem. In particular, three low-complexity algorithms are proposed to trade off between computational complexity and convergence rate. Compared to different benchmark schemes, simulation results demonstrate that with the aid of the RISs, the PRs can benefit from the enhanced primary transmission from the PT, and receive information from the associated RISs via IoT transmission.

The Internet-of-Things (IoT) paradigm holds the promise to revolutionize the way we live and work through connecting various machine-type communication terminals. In this work, we investigate a symbiotic network from a cross-layer perspective, where passive IoT devices coexist in symbiosis with an ambient network that uses carrier sense multiple access with collision notifications (CSMA/CNs) MAC protocol. In the ambient network, each full-duplex mobile user (MU) aims to transmit its own packets to the common access point (AP) while receiving the signal backscattered from its associated backscatter device (BD). Considering the imperfectness of carrier sensing of the MU, two key parameters, i.e., the probability of detection and the probability of false alarm, are quantified. Then we derive the PHY-layer outage probabilities and analyze the corresponding diversity orders for both the CSMA/CN and the BD system. By incorporating the outage probabilities and the carrier sensing metrics into the MAC-layer analysis, the cross-layer outage capacities of the CSMA/CN and the BD system are derived. Simulation results demonstrate that the system performance can be improved by appropriately setting the PHY-layer parameters, such as the BD reflection coefficient $\alpha $ and the number of samples for carrier sensing $K$ , as well as the MAC-layer parameters, such as the sensing duration and the initial contention window. With the BD reflection coefficient being 0.05, the outage capacity of the overall system has improved by 41.85% compared with carrier sense multiple access with collision avoidance protocol.

We consider a typical heterogeneous network (HetNet), in which multiple access points (APs) are deployed to serve users by reusing the same spectrum band. Since different APs and users may cause severe interference to each other, advanced power control techniques are needed to manage the interference and enhance the sum-rate of the whole network. Conventional power control techniques first collect instantaneous global channel state information (CSI) and then calculate sub-optimal solutions. Nevertheless, it is challenging to collect instantaneous global CSI in the HetNet, in which global CSI typically changes fast. In this article, we exploit deep reinforcement learning (DRL) to design a multi-agent power control algorithm, which has a centralized-training-distributed-execution framework. To be specific, each AP acts as an agent with a local deep neural network (DNN) and we propose a multiple-actor-shared-critic (MASC) method to train the local DNNs separately in an online trial-and-error manner. With the proposed algorithm, each AP can independently use the local DNN to control the transmit power with only local observations. Simulations results show that the proposed algorithm outperforms the conventional power control algorithms in terms of both the converged average sum-rate and the computational complexity.

In this letter, the joint power control of D2D users and the passive beamforming of reconfigurable intelligent surfaces (RIS) for a RIS-aided device-to-device (D2D) communication network is investigated to maximize energy efficiency. This non-convex optimization problem is divided into two subproblems, which are passive beamforming and power control. The two subproblems are optimized alternately. We first decouple the passive beamforming at RIS based on the Lagrangian dual transform. This problem is solved by using fractional programming. Then we optimize the power control by using the Dinkelbach method. By iteratively solving the two subproblems, we obtain a suboptimal solution for the joint optimization problem. Numerical results have verified the effectiveness of the proposed algorithm, which can significantly improve the energy efficiency of the D2D network., Comment: 4 pages, 4 figures

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services.

Non-Terrestrial Networks (NTNs) composed of space-borne (e.g., satellites) and airborne vehicles (e.g., drones and blimps) have recently been proposed by 3GPP as a new paradigm of infrastructures to enhance the capacity and coverage of existing terrestrial wireless networks. The mobility of non-terrestrial base stations (NT-BSs) however leads to a dynamic environment, which imposes unique challenges for handover and throughput optimization particularly in multi-user access control for NTNs. To achieve performance optimization, each terrestrial user equipment (UE) should autonomously estimate the dynamics of moving NT-BSs, which is different from the existing user access control schemes in terrestrial wireless networks. Consequently, new learning schemes for optimum multi-user access control are desired. In this article, we therefore propose a UE-driven deep reinforcement learning (DRL) based scheme, in which a centralized agent deployed at the backhaul side of NT-BSs is responsible for training the parameter of a deep Q-network (DQN), and each UE independently makes its own access decisions based on the parameter from the trained DQN. With the proposed scheme, each UE is able to access a proper NT-BS intelligently to enhance the long-term system throughput and avoid frequent handovers among NT-BSs. Through comprehensive simulation studies, we justify the performance of the proposed scheme, and show its effectiveness in addressing the fundamental issues in the NTNs deployment.

This letter investigates an energy-limited unmanned aerial vehicle (UAV)-enabled wireless-powered Internet-of-Things (IoT) networks. Herein, we propose an energy-constrained/limited fly-and-hover energy harvest (EFH-EH) scheme. The UAV charges the IoT sensors during its flying time, while the hovering time of the UAV above each sensor is divided into two parts, one for energy transfer and the other for information transmission. In particular, a maximum available energy constraint is imposed on the UAV to capture UAV’s real-world energy-limited feature. In addition, a practical UAV energy consumption model is adopted, which differentiates the hovering, flying, and charging energy consumption of the UAV. Under these practical constraints, we study the optimal time allocation and hovering time ratio to maximize the sum throughput. Numerical results show that the proposed EFH-EH scheme outperforms existing schemes, and the maximum available energy constraint limits the system performance when the transmit power is high.

Device-to-device (D2D) technology, which allows direct communications between proximal devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic explosion problem. In this paper, we consider an overlay D2D network, in which multiple D2D pairs coexist on several orthogonal spectrum bands, i.e., channels. Due to spectrum scarcity, the number of D2D pairs is typically more than that of available channels, and thus multiple D2D pairs may use a single channel simultaneously. This may lead to severe co-channel interference and degrade network performance. To deal with this issue, we formulate a joint channel selection and power control optimization problem, with the aim to maximize the weighted-sum-rate (WSR) of the D2D network. Unfortunately, this problem is non-convex and NP-hard. To solve this problem, we first adopt the state-of-art fractional programming (FP) technique and develop an FP-based algorithm to obtain a near-optimal solution. However, the FP-based algorithm requires instantaneous global channel state information (CSI) for centralized processing, resulting in poor scalability and prohibitively high signalling overheads. Therefore, we further propose a distributed deep reinforcement learning (DRL)-based scheme, with which D2D pairs can autonomously optimize channel selection and transmit power by only exploiting local information and outdated nonlocal information. Compared with the FP-based algorithm, the DRL-based scheme can achieve better scalability and reduce signalling overheads significantly. Simulation results demonstrate that even without instantaneous global CSI, the performance of the DRL-based scheme can approach closely to that of the FP-based algorithm.

Backscatter communication which enables wireless-powered backscatter devices (BDs) to transmit information by reflecting incident signals, is an energy- and cost-efficient communication technology for Internet-of-Things. This paper considers an unmanned aerial vehicle (UAV)-assisted backscatter communication network (UBCN) consisting of multiple BDs and carrier emitters (CEs) on the ground as well as a UAV. A communicate-while-fly scheme is first designed, in which the BDs illuminated by their associated CEs transmit information to the flying UAV in a time-division-multiple-access manner. Considering the critical issue of the UAV's limited on-board energy and the CEs' transmission energy, we maximize the energy efficiency (EE) of the UBCN by jointly optimizing the UAV's trajectory, the BDs' scheduling, and the CEs' transmission power, subject to the BDs' throughput constraints and harvested energy constraints, as well as other practical constraints. Furthermore, we propose an iterative algorithm based on the block coordinated decent method to solve the formulated mixed-integer non-convex problem, in each iteration of which the variables are alternatively optimized by leveraging the cutting-plane technique, the Dinkelbach's method and the successive convex approximation technique. Also, the convergence and complexity of the proposed algorithm are analyzed. Finally, simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared with the benchmark hover-and-fly scheme. Useful insights on the optimal trajectory design and resource allocation are also obtained., Comment: This paper has 31 pages and 10 figures. It is submitted for possible journal publications

The heterogenous wireless services and exponentially growing traffic call for novel spectrum- and energy-efficient wireless communication technologies. Recently, a new technique, called symbiotic radio (SR), is proposed to exploit the benefits and address the drawbacks of cognitive radio (CR) and ambient backscattering communications (AmBC), leading to mutualism spectrum sharing and highly reliable backscattering communications. In particular, the secondary transmitter (STx) in SR transmits messages to the secondary receiver (SRx) over the RF signals originating from the primary transmitter (PTx) based on cognitive backscattering communications, thus the secondary system shares not only the radio spectrum, but also the power, and infrastructure with the primary system. In return, the secondary transmission provides beneficial multipath diversity to the primary system, therefore the two systems form mutualism spectrum sharing. More importantly, joint decoding is exploited at SRx to achieve highly reliable backscattering communications. In this article, to exploit the full potential of SR, we provide a systematic view for SR and address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx’s RF signals using reconfigurable intelligent surfaces. Emerging applications, design challenges and open research problems will also be discussed.

In this letter, we consider a multi-user cognitive backscatter network. Backscatter devices (BDs) not only share the spectrum with the primary user, but also harvest energy from and transmit their own information over the primary signal. The multi-BD system adopts non-orthogonal multiple access (NOMA) to further improve the spectral efficiency. Moreover, BDs involved are assumed to have energy storage capability so that the energy harvested in the current block can be stored for future use. Under this assumption, a multi-slot energy causality constraint is proposed. The transmit power of the primary user and the reflection coefficients of BDs are jointly optimized to maximize the sum-rate of the secondary system over multiple slots. Based on the concave-convex procedure (CCCP), we obtain a near-optimal solution by iteratively maximizing the sum-rate. Besides, the effect of imperfect SIC on our proposed resource allocation schemes is also studied. Numerical results show that the multi-slot operation can achieve a remarkable performance gain over the single-slot operation.

Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-the-art solutions.

In this letter, we consider the intelligent-reflecting-surface (IRS)-assisted single-user multiple-input single-output downlink communication system, in which multiple IRSs are deployed to improve the propagation condition. Our objective is to configure the phase shift matrices of all the IRSs by exploiting the statistical channel state information (CSI). In particular, two effective algorithms are proposed, which learns the statistical CSI from the historical channel observations. Numerical results show that significant performance gain is achieved especially when the channel randomness is low.

Cellular traffic prediction plays an important role in network management and resource utilization. However, due to the high nonlinearity and dynamic spatial-temporal correlation, it is challenging to obtain the traffic prediction accurately. In this letter, a spatial-temporal attention-convolution network is proposed to predict the citywide cellular traffic. Considering the temporal correlation of the cellular traffic, the traffic data is modeled by the hourly, daily and weekly traffic components independently. In each traffic component, the spatial-temporal attention module is designed to capture the dynamic spatial-temporal correlation of cellular traffic; the spatial-temporal convolution module utilizes the graph convolution and standard convolution to obtain the spatial and temporal features simultaneously. By considering the external factors, the prediction of final mobile traffic is obtained. Experiment results indicate that the proposed approach outperforms the existing prediction methods on the real-world cellular traffic datasets.

We consider a homogeneous cellular network where a multi-antenna base station (BS) in each cell transmits messages to its intended user over a common frequency band. To improve the system capacity of this multi-cell multi-input single-output (MISO) interference channel, one of the state-of-the-art algorithms, namely, downlink-beamforming coordination, allows all BSs to cooperate with one another to mitigate the effect of inter-cell interference. However, most existing algorithms are suboptimal and impractical in a dynamic wireless environment, due to the high computational complexity and the overhead involved in collecting global channel state information (CSI). In this study, we exploit deep reinforcement learning (DRL) and propose a distributed dynamic downlink-beamforming coordination (DDBC) method with partial observability of the CSI. Each BS is able to train its own deep Q-network and employs appropriate beamformer depending on its environment, which is observed through a designed limited-information exchange protocol. The simulation results show that the proposed DRL-based DDBC method, with a considerably lower system overhead, achieves a system capacity that is very close to that of the fractional programming algorithm with global and instantaneous CSI measurements. In addition, this work demonstrates the potential of utilizing DRL to solve DDBC problems in a more practical manner.

In this letter, a machine learning approach is proposed to cancel the interference from multiple sources in the concurrent spectrum access (CSA) model of the cognitive Internet of Things (C-IoT). We assume that the C-IoT system is non-cooperative with the primary user (PU) system, and has little knowledge on the interference caused by PU transmitters. In order to recover the C-IoT signal under power strong multi-interference, we employ an iterative receiver consisting of a linear estimation module, and a demodulation-and-decoding module, as well as a clustering module by following the idea of iterative detection and interference cancellation. Conventional clustering algorithms, such as the K-means algorithm and the Gaussian mixture model (GMM) based expectation maximization (EM) algorithm, can potentially be used to realize the clustering module. However, their performance is poor under the existence of multiple interferers since the performance of these algorithms heavily relies on the initialization of cluster centroids. To address this problem, we use the affinity propagation (AP) algorithm, which works well without the initialization of cluster centroids, to realize the clustering module. Numerical results demonstrate that the AP algorithm has much better performance than the K-means and GMM-EM algorithms in handling multi-interference.

Wireless network optimization has been becoming very challenging as the problem size and complexity increase tremendously, due to close couplings among network entities with heterogeneous service and resource requirements. By continuously interacting with the environment, deep reinforcement learning (DRL) provides a mechanism for different network entities to build knowledge and make autonomous decisions to improve network performance. In this article, we first review typical DRL approaches and recent enhancements. We then discuss the applications of DRL for mobile edge computing (MEC), which can be used for the low-power IoT devices, e.g., wireless sensors in healthcare monitoring, to offload computation workload to nearby MEC servers. To balance power consumption in offloading and computation, we propose a novel hybrid offloading model that exploits the complement operations of RF communications and low-power backscatter communications. The DRL framework is then customized to optimize the transmission scheduling and workload allocation in two communications technologies, which is shown to enhance the offloading performance significantly compared with existing schemes., 15 pages, 6 figures, 15 references

This paper proposes a peer-assisted power supply approach for a wireless powered Internet of Things (IoT) non-orthogonal multiple access (NOMA) network in which passive user equipments (UEs) without battery harvest energy from active UEs equipped with power supply. Specifically, passive UEs harvest energy from active UEs during their uplink transmission using NOMA. They then upload information along with the active UEs. Particularly, considering the combination of time division multiple access (TDMA) and NOMA, under the assumption that the power of active UEs is fixed, we study different transmission modes (non-stand-alone/stand-alone) and different operations (NOMA/NOMA-plus-TDMA). Taking into account the practical applications, we re-investigate the above schemes in the scenario where active UEs’ energy is limited, i.e., the power of active UEs is not fixed and is affected by time allocation. We maximize the sum-throughput of each proposed model. We prove that the optimization problems for all cases are convex, and we obtain closed-form solutions for most cases. Finally, we show by simulations that, in all cases, the transmit power of active UEs and the number of UEs have a positive effect on the sum-throughput. Besides, in terms of maximizing the sum-throughput, the NOMA-plus-TDMA operation outperforms the NOMA operation. If active UEs’ power is fixed, the non-stand-alone transmission outperforms the stand-alone transmission, and vice versa.

Ambient backscatter communication (AmBC) has great potential to provide Internet of Things (IoT) services with low energy and spectrum cost. In this letter, we study an AmBC system where multiple full-duplex mobile users (MUs) attempt to send packets to the access point (AP) according to the CSMA/CA protocol while receiving the information from their respective associated backscatter devices (BDs). We notice that the transmission of the BD not only decreases the PHY-layer SINR of the CSMA/CA system, but also increases its failure probability of the packet transmission in the MAC-layer. In return, the BD transmission probability is affected by the MAC-layer activity of the CSMA/CA system. To this end, we analyze the cross-layer performance of the AmBC system and quantify the cross-layer outage capacities of the BD and the CSMA/CA systems. Simulation results are provided to verify our theoretical analysis, and demonstrate the interaction between the BD and the ambient CSMA/CA systems.

This paper investigates the rotary-wing unmanned aerial vehicle (UAV)-enabled full-duplex wireless-powered Internet-of-Things (IoT) networks, in which a rotary-wing UAV equipped with a full-duplex hybrid access point (HAP) serves multiple sparsely-distributed energy-constrained IoT sensors. The UAV broadcasts energy when flying and hovering, and collects information only when hovering. It is assumed that the transmission range of the UAV is limited and the sensors are sparsely distributed in the IoT network. Under these practical assumptions, we formulate three optimization problems: a sum-throughput maximization (STM) problem, a total-time minimization (TTM) problem, and a total-energy minimization (TEM) problem. For the TEM problem, we further take into consideration that the power needed for hovering, flying, and transmitting are different. For the STM, TTM and TEM problems, optimal solutions are obtained. Finally, numerical results show that the performance achieved by the proposed optimal time allocation schemes outperform existing time allocation schemes. It is also observed that i) the time allocation between hovering and flying time has different trends for different goals; ii) there is an optimal UAV transmit power range that minimizes the energy consumed by the UAV during the entire cycle.

Ambient backscatter technology utilizes ambient radio frequency (RF) signals to enable battery-free devices (tags and readers) to communicate. Most existing studies assume single-antenna tags. However, in this paper, we consider tags with multiple antennas, which are exploited to provide transmit diversity. Channel state information (CSI) estimation is then a fundamental challenge because the tags can transmit few or no training symbols. To overcome it, we require detectors that operate without CSI. Thus, we propose and design three detectors based on the chi-squared test, F-test and Bartlett’s test. The latter two are blind detectors because they require neither CSI nor the knowledge of RF source power and noise variance. We derive the detection probability bounds for the first two detectors. We also propose optimal tag antenna selection schemes to maximize the detection probabilities. Finally, simulation results are provided to corroborate our theoretical studies.

In intelligent transportation system (ITS), the interworking of vehicular networks (VN) and cellular networks (CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading (MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process (SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio. Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.

Operating LTE networks in unlicensed bands together with legacy WiFi systems is deemed as a promising technique to support explosively growing mobile traffic. In conventional LTE/WiFi spectrum sharing schemes, LTE systems need to know WiFi traffic demands for optimizing system parameters to protect WiFi systems, for which the two systems are required to cooperate with each other via signallings exchanges. However, it is difficult to establish a dedicated channel among the two independent systems for exchanging signallings. Hence, in this paper, we propose an intelligent duty-cycle medium access control protocol to realize the effective and fair spectrum sharing between LTE and WiFi systems without requiring signalling exchanges. Specifically, we first design a duty-cycle spectrum sharing framework, which allows an LTE system to share the spectrum with a WiFi system by using time sharing. After that, we develop deep reinforcement learning (DRL)-based algorithms to learn WiFi traffic demands by analyzing WiFi channel activity, e.g., the idleness/business of WiFi channels, which can be observed by the LTE system via monitoring WiFi channels. Based on the learnt knowledge, the LTE system can adaptively optimize LTE transmission time to maximize its own throughput and meanwhile to provide sufficient protection to the WiFi system. Simulation results show that, in terms of LTE throughput and WiFi protection, the performance of the proposed intelligent scheme can approach that of the genie-aided exhaustive search algorithm, which needs the perfect knowledge of WiFi traffic demands through massive signalling exchanges and is of high computational complexity.

Since 5G new radio comes with non-standalone (NSA) and standalone (SA) versions in 3GPP, research on 6G has been on schedule by academics and industries. Though 6G is supposed to have much higher capabilities than 5G, yet there is no clear description about what 6G is. In this article, a comprehensive discussion of 6G is given based on the review of 5G developments, covering visions and requirements, technology trends and challenges, aiming at tackling the challenge of coverage, capacity, the user data rate and movement speed of mobile communication system. The vision of 6G is to fully support the development of a Ubiquitous Intelligent Mobile Society with intelligent life and industries. Finally, the roadmap of the 6G standard is suggested for the future.

In this article, a symbiotic radio (SR) system is proposed to support passive Internet of Things (IoT), in which a backscatter device (BD), also called IoT device, is parasitic in a primary transmission. The primary transmitter (PT) is designed to assist both the primary and BD transmissions, and the primary receiver (PR) is used to decode the information from the PT as well as the BD. The symbol period for BD transmission is assumed to be either equal to or much greater than that of the primary one, resulting in parasitic SR (PSR) or commensal SR (CSR) setup. We consider a basic SR system which consists of three nodes: 1) a multiantenna PT; 2) a single-antenna BD; and 3) a single-antenna PR. We first derive the achievable rates for the primary and BD transmissions for each setup. Then, we formulate two transmit beamforming optimization problems, i.e., the weighted sum-rate maximization (WSRM) problem and the transmit power minimization (TPM) problem, and solve these nonconvex problems by applying the semidefinite relaxation (SDR) technique. In addition, a novel transmit beamforming structure is proposed to reduce the computational complexity of the solutions. The simulation results show that for CSR setup, the proposed solution enables the opportunistic transmission for the BD via energy-efficient passive backscattering without any loss in spectral efficiency, by properly exploiting the additional signal path from the BD.

This paper presents a literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS's phase control, combined with the conventional transmission control, can potentially bring performance gain compared to wireless networks without IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g.,~rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases. Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless networks in beyond 5G communications., 31 pages, 10 figures, 6 tables, 203 references

This paper studies a full-duplex symbiotic radio network, where a full-duplex access point (FAP) transmits downlink orthogonal frequency division multiplexing signals to a legacy user (LU) and simultaneously receives signals backscattered from multiple passive backscatter devices (BDs) with capability of radio-frequency energy harvesting. A non-orthogonal-multiple-access enhanced dynamic-time-division-multiple-access (NOMA-DTDMA) transmission scheme is proposed to exploit the channel dynamics and further improve the spectrum efficiency. In order to maximize the throughput performance and ensure BD fairness, we maximize the minimum throughput among all BDs by jointly optimizing the FAP's subcarrier power allocation, the BDs' backscatter time allocation and power reflection coefficients, subject to the LU's throughput requirement, the BDs' harvested energy requirements, and other practical constraints. An efficient iterative algorithm is proposed to solve the formulated non-convex problem, by utilizing the block coordinated descent and successive convex optimization techniques. The convergence and complexity of the proposed algorithm are also analyzed. Numerical results show that the proposed NOMA-DTDMA scheme significantly outperforms the benchmark scheme of dynamic TDMA in terms of both throughput performance and BD fairness. Also, the trade-off performances between the BDs' throughput and the LU's throughput requirement as well as the BDs' harvested energy requirements are numerically verified.

This letter considers a non-orthogonal-multiple-access (NOMA)-enhanced bistatic backscatter communication network where multiple backscatter devices (BDs) transmit to a backscatter receiver using a protocol integrating NOMA with dynamic time-division-multiple-access. We maximize the minimum throughput among all BDs by jointly optimizing the BDs’ backscatter time and power reflection coefficients, subject to the BDs’ harvested energy constraints and other constraints. By applying the block coordinated decent and successive convex optimization techniques, an efficient iterative algorithm is proposed to find a sub-optimal solution to the formulated non-convex problem. Numerical results show that the proposed scheme achieves significant throughput gain compared to the benchmark scheme.

Recently, classifying the modulation schemes of signals using deep neural network has received much attention. In this paper, we introduce a general model of deep neural network (DNN)-based modulation classifiers for single-input single-output (SISO) systems. Its feasibility is analyzed using maximum a posteriori probability (MAP) criterion and its robustness to uncertain noise conditions is compared to that of the conventional maximum likelihood (ML)-based classifiers. To reduce the design and training cost of DNN classifiers, a simple but effective pre-processing method is introduced and adopted. Furthermore, featuring multiple recurrent neural network (RNN) layers, the DNN modulation classifier is realized. Simulation results show that the proposed RNN-based classifier is robust to the uncertain noise conditions, and the performance of it approaches to that of the ideal ML classifier with perfect channel and noise information. Moreover, with a much lower complexity, it outperforms the existing ML-based classifiers, specifically, expectation maximization (EM) and expectation conditional maximization (ECM) classifiers which iteratively estimate channel and noise parameters. In addition, the proposed classifier is shown to be invariant to the signal distortion such as frequency offset. Furthermore, the adopted pre-processing method is shown to accelerate the training process of our proposed classifier, thus reducing the training cost. Lastly, the computational complexity of our proposed classifier is analyzed and compared to other traditional ones, which further demonstrates its overall advantage.

Ambient Backscattering Communication (AmBC) is regarded as a promising approach for green Internet-of-Things (IoT) networks. However, the noise nature of the ambient radio-frequency (RF) signal poses great difficulty for the signal processing at the receiver, resulting in an unsatisfied Bit Error Rate (BER) performance at a long communication distance. Aiming at improving AmBC performance in practical noisy environments, this letter introduces the concept of nonlinear dynamics to AmBC signal processing and thereby designs a novel Stochastic Resonance (SR) based nonlinear AmBC receiver architecture. As such, the received Signal-to-Noise Ratio (SNR) can be greatly improved, as the analog nonlinear filtering module constructively utilizes useless noise to enhance the target signal. This further allows us to realise sub-symbol level sampling with low rate, i.e. avoiding high speed Analog-to-Digital Convertor (ADC) with high power expenditure. Signal detection is finally accomplished via simple averaging and automatic thresholding, which hence greatly facilitates the efficient hardware design. Simulation results demonstrate the superiority of our nonlinear AmBC signal receiving system.

With increasingly sophisticated electromagnetic environment of combat platforms, radar communication integration has attracted wide interests. In this letter, instead of designing a new integrated signal, we employ multiple-phase shift keying direct sequence spread spectrum (MPSK-DSSS) signal as radar communication integrated signal due to its advantage of outstanding correlation. On the one hand, the closed form expression of average ambiguity function is obtained, by which we can analyze the relationship between parameters of integrated signal and radar range resolution. On the other hand, to improve radar peak sidelobe level (PSL) influenced by random communication signals, we propose a joint optimization method based on sequence mapping at the transmitter and sidelobe suppression at the radar receiver. Simulation results show that the sidelobe levels of the optimized integrated signal is effectively suppressed at the cost of slight communication rate loss.

Recently proposed Bluetooth low energy (BLE)-backscatter allows interference-free transmission by locally generating a sub-carrier for frequency shifting (FS). Motivated by the BLE backscatter, in this paper, we investigate the price-based bandwidth allocation for multi-user backscatter communication (BackCom), where multiple BackCom users are allocated to different non-overlapping sub-channels and all users share the whole spectrum in a frequency division multiple access (FDMA) manner. Each BackCom user is charged by the primary user (PU) in the same sub-channel for a price, which is proportional to the allocated bandwidth and can be viewed as the cost for bandwidth sharing. A Stackelberg game is formulated to study joint maximization of the revenue of PUs and the utility function of each BackCom user subject to the total bandwidth constraint on the PUs and the individual bandwidth constraints on each BackCom user. Stackelberg Equilibriums (SEs) for two proposed schemes are investigated in closed-form expressions. Simulation results confirm the effectiveness of proposed schemes in improving the revenue performance of PUs and the sum capacity performance of BackCom users.

In this paper, we study time-varying orthogonal frequency division multiplexing (OFDM) systems, and propose a joint channel-and-signal estimation receiver based on turbo message passing (TMP) to efficiently suppress inter-carrier interference (ICI). We establish a factor graph representation of the problem and divide the whole factor graph into two parts, one for channel estimation and the other for signal detection. For the first part, we use Gaussian message passing (GMP) for channel estimation; for the second part, a discrete state space (DSS) model is employed to describe the transition of signal states, and a forward-backward algorithm is adopted for message passing over the transition trellis in signal detection. The resulting algorithm is referred to as DSS-GMP. The complexity of DSS-GMP quickly becomes the bottleneck as the increase of the signal constellation size and the ICI width. To address this issue, we further develop a continuous-state-space (CSS) model based turbo message passing algorithm, where the messages of modulated signals are approximated as continuous Gaussian messages. Numerical results demonstrate that the TMP based scheme significantly outperforms the state-of-the-art schemes.

In this letter, a novel spectrum sharing system referred to as cognitive backscatter network (CBN) is proposed to support passive Internet of Things (IoT) transmission via backscatter modulation, in which the primary system transmits a sinusoidal carrier in addition to its own signal. In contrast to existing ambient backscatter communications, the proposed CBN does not require perfect symbol synchronization between the IoT and the primary transmissions. We maximize the data rate of the IoT transmission subject to the minimum rate requirement of the primary system by jointly optimizing the time sharing and power allocation parameters. Numerical results have demonstrated the superiority of the proposed CBN over conventional TDMA scheme.

In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.

One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues. However, these features could not accurately characterize the real environment. Motivated by this, in this paper, we use a deep neural network (DNN) to intelligently explore the data-driven test statistic. Firstly, we introduce a DNN-based detection framework, where a DNN-based likelihood ratio test (DNN-LRT) is derived to guarantee the optimality of the designed test statistic. As a realization of the developed DNN-based framework, we use the sample covariance matrix as the input of a convolutional neural network (CNN), and propose a covariance matrix-aware CNN (CM-CNN)-based spectrum sensing algorithm, which further improves the performance. In addition, we also provide the theoretical analysis of the proposed method. To the best of our knowledge, it’s the first time to analyze the theoretical performance of CNN-based methods. Finally, simulation results demonstrate that the performance of the proposed method is close to that of the optimal detector. Particularly, the proposed method could achieve a detection probability of 96.7% with a false alarm probability of 1.9% at SNR = −18dB, which significantly outperforms the conventional methods.

Massive wireless connections are emerging in Internet of Things (IoT) and will lead to a severe spectrum scarcity issue. To deal with this issue, we introduce the cognitive radio technology into the IoT, namely, cognitive IoT. Different from a conventional cognitive network, the cognitive IoT is dominated by short-packet transmissions, which suffer from a significant packet error rate even when the transmission rate is smaller than the Shannon capacity. In this paper, we jointly optimize the spectrum sensing time and packet error rate to maximize the cognitive effective-throughput, which is defined as the effective transmission rate by considering the packet error rate. First, we formulate an instantaneous effective-throughput maximization problem with the instantaneous channel state information (CSI) between cognitive transceivers, and develop a successive optimization algorithm. Second, we formulate an average effective-throughput maximization problem with the statistical CSI between cognitive transceivers. Due to the complicated expression of the average effective-throughput, we analyze its closed-form expression and adopt an exhaustive search method to obtain the optimal solution. Numerical and simulation results reveal that, the packet length has a significant impact on the optimal design. Meanwhile, the proposed algorithms can almost maximize the instantaneous/average effective-throughput.

International audience; Reconfigurable intelligent surface (RIS), one of the key enablers for the sixth-generation (6G) mobile communication networks, is considered by designers to smartly reconfigure the wireless propagation environment in a controllable and programmable manner. Specifically, an RIS consists of a large number of low-cost and passive reflective elements (REs) without radio frequency chains. The system gain of RIS wireless systems can be achieved by adjusting the phase shifts and amplitudes of the REs so that the desired signals can be added constructively at the receiver. However, an RIS typically has limited signal processing capability and cannot perform active transmitting/receiving in general, which leads to new challenges in the physical layer design of RIS wireless systems. In this paper, we provide an overview of the RIS-aided wireless systems, including the reflection principle, channel estimation, and system design. In particular, two types of emerging RIS systems are considered: RIS-aided wireless communications (RAWC) and RIS-based information transmission (RBIT), where the RIS plays the role of the reflector and the transmitter, respectively. We also envision the potential applications of RIS in 6G networks.

This paper considers a reconfigurable intelligent surface (RIS) enhanced symbiotic radio (SR) multigroup multicast system, where the RIS passively transmits information to an Internet-of-Things receiver (IR) by modulating the incident multicasting signals, and also enhances the downlink multicast transmission from a base station (BS) to multiple primary receivers (PRs). We aim to minimize the BS’s transmit power by jointly optimizing the active transmit beamforming at the BS and the passive phase shifts at the RIS, subject to the constraints on the PRs’ signal-to-interference-plus-noise-ratio, the IR’s successive-interference-cancellation and signal-to-noise-ratio, as well as the RIS’s phase shifts. An efficient iterative algorithm is proposed to solve the non-convex problem, by employing alternating optimization, quadratic transform and semidefinite relaxation. The algorithm’s convergence and complexity are also analysed. Numerical results show that the proposed scheme consumes less power than the traditional multicast scheme without RIS.

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator.

This paper is concerned with a machine learning approach to cancel the interference for cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model, where the C-IoT system is noncooperative and has very limited knowledge on the interference. Our transceiver design uses an iterative processing structure, which consists of a linear estimator, a demodulation-and-decoding module, and a clustering module. In the clustering module, we employ modified expectation–maximization (EM)-based algorithms to estimate the interference under the knowledge of the modulation constraint (MC) of the interference. We show that this modified EM algorithm-based receiver outperforms the original EM-based receiver, since the former is able to generate a more accurate clustering result by reducing the dimension of the parameter space. We further improve the performance of the iterative receiver by introducing the extrinsic information technique, with the resulting algorithm referred to as the extrinsic modulation constrained EM (Ext-MC-EM) algorithm. We show that the Ext-MC-EM algorithm-based receiver considerably outperforms the counterpart iterative receivers, including the MC-EM algorithm.

With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation (6G) mobile communications be on the eve of the fifth-generation (5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things (IoT), and artificial intelligence (AI). Then, we review key technologies to realize each aspect. In particular, teraherz (THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.

Cooperative ambient backscatter system is a novel symbiotic radio paradigm, in which the passive backscatter transmission shares not only the same spectrum but also the same radio-frequency source with the active primary transmission. By considering the unique features of the ambient backscatter system, three practical symbiotic transmission schemes are proposed, in which the relationships between the primary and backscatter transmissions are commensal, parasitic, and competitive, respectively. The achievable rates for both primary and backscatter transmissions are derived for particular fading state. Then, optimal power allocation is obtained for each transmission scheme under the average power constraint. Simulation results are provided to corroborate theoretical studies.

Dynamic spectrum management (DSM) plays an increasingly important role in wireless communication networks for improving spectral efficiency. Conventionally, DSM is realized with the support of accurate information or with the dependence on assumptions about the network, which could be challenging and impractical in the Internet of Things where a large number of users need to be served. The application of machine learning into DSM is promising to address these issues, and many investigations have focused on this application. This article aims to survey the state-of-the-art research results along this direction. We devise a taxonomy to categorize the literature based on the operation modes, learning paradigms, enabling functions, and design objectives. Moreover, the key challenges are outlined to facilitate the application of machine learning for DSM. Finally, we present several open issues as the future research direction.

In cognitive radio, most spectrum sensing algorithms are model-based and their detection performance relies heavily on the accuracy of the assumed statistical model. In this letter, we propose a convolutional neural network-based deep learning algorithm for spectrum sensing. Compared with model-based spectrum sensing algorithms, our proposed deep learning approach is data-driven and requires neither signal-noise probability model nor primary user (PU) activity pattern model. The proposed algorithm simultaneously takes in the present sensing data and historical sensing data, with which the inherent PU activity pattern can be learned to benefit the detection of PU activity. With extensive numerical simulations, results show that the proposed algorithm outperforms the estimator-correlator detector and the hidden Markov model-based detector in terms of correct detection probability.

We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by the MCS switchings, we further introduce a switching cost factor in the proposed algorithm. The simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90% ~ 100% of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, is 30% higher than that of the upper confidence bandit (UCB) algorithm, and is 100% higher than that of the signal-to-noise ratio (SNR)-based algorithm. Meanwhile, the proposed algorithm with the switching cost factor can achieve a higher primary transmission rate than those of the benchmark algorithms without increasing system overheads.