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Targeting at implementing the next generation radio access networks (RANs) with virtualized network components, the open RAN (O-RAN) has been regarded as a novel paradigm towards fully open, virtualized and interoperable RANs. Through particularly introducing RAN intelligent controllers (RICs), machine learning (ML) can be unprecedentedly installed, adapting to various vertical applications and deployment environments without sophisticated planning efforts. However, the O-RAN also suffers two critical challenges of load balancing and frequent handovers in the massive base station (BS) deployment. In this paper, an intelligent user access control scheme with deep reinforcement learning (DRL) is proposed. To optimize the performance of distributed deep Q-networks (DQNs) trained by user equipments (UEs), a federated DRL-based scheme is proposed with a global model server installed in the RIC to update the DQN parameters. To further predictively train a global DQN with acceptable signaling overheads, the upper confidence bound (UCB) algorithm to select the optimal UE set and a dueling structure to decompose the DQN parameters are developed. With the proposed scheme, each UE effectively maximizes the long-term throughput and avoids frequent handovers. The simulation results well justify the outstanding performance of the proposed scheme over the-state-of-the-arts, to serve as references for the O-RAN standardization.

Multiple-input multiple-output (MIMO) has been used in wireless communications to increase data rates via multiplexing or improve performance via diversity. Additionally, in radar systems, MIMO promises improved parameter identifiability and target resolution. Frequency diverse (FD)-MIMO radar can effectively distinguish targets that are closely spaced in the same angle cell by exploiting its range-angle-dependent transmit beamform. Therefore, in this paper, we first propose an integrated waveform design by embedding weighted, phase-modulated communication signals in the FD-MIMO radar waveform. Then, we derive the Cramer-Rao lower bound (CRLB) of the location estimation and analyze the communication performance achieved when the communication receiver extracts information from the integrated signals. Next, transmit beamforming is optimized to achieve the best tradeoff between the radar and communication performances of the system. Due to the nonconvexity of the optimization problem, we apply sequential parametric convex approximation (SPCA) and semidefinite relaxation (SDR) methods to solve the problem. The simulation results reveal the effects of some parameters, such as the frequency interval, symbol period, and communication symbol sequence, on radar and communication performance and demonstrate the tradeoff between radar and communication obtained by optimizing the transmit beamforming vector.

The Internet of Things (IoT) has been extensively investigated as an enabling technology for situation awareness and data collection. Since power-limited or battery-free IoT devices may have limited transmission coverage, unmanned aerial vehicles (UAVs) can be employed as mobile edge computing (MEC) servers to collect and process data packets from multiple IoT devices due to their high mobility and low operating cost. However, data transmission becomes vulnerable when eavesdroppers exist, as those IoT devices may have limited computing power for encryption. Moreover, it is noteworthy that the identity and location information of IoT devices themselves are also crucial to particular implementations (e.g., regional situation awareness), especially when massive IoT devices are involved using random access schemes. In this article, a secure structure is proposed as a promising strategy to support UAV-aided IoT networks, where the identity and location information of multiple IoT devices are utilized as the key to encrypt data packets collected from IoT networks. In the new structure, the trajectory planning of UAVs is developed to minimize the energy consumption among multiple clusters of a UAV, while the deployment optimization is used to maximize the secrecy capacity in each cluster, and the beam hopping is carried out to encrypt multi-access IoT devices within a coverage of a UAV. After introducing the structure, some key technologies are presented, and then future works for the secure UAV-aided IoT network are discussed.

This letter studies a symbiotic radio network (SRN) with multiple backscatter devices (BDs) coexisting with a primary receiver. A hybrid peer-assisted non-orthogonal-multiple-access (NOMA)-plus-dynamic-time-division-multiple-access (TDMA) scheme is proposed. Under this scheme, BDs are categorized into different clusters, and only one cluster is allowed to transmit in each time slot. Within each cluster, NOMA is adopted. The latter cluster can harvest energy from previous clusters using peer assistance. We maximize the minimum throughput among all BDs by jointly optimizing the BS’s power allocation, BDs’ power reflection coefficients and the time allocation under practical constraints. Particularly, a practical energy causality constraint is considered, and the effect of imperfect successive interference cancellation (SIC) of NOMA is also studied. Numerical results show that the peer-assisted NOMA-plus-TDMA scheme achieves satisfactory throughput gain over conventional schemes. It is also shown that peer assistance does help in terms of improving the system throughput and fairness.

Traditional D2D power control methods require instantaneous interference information and so are difficult to implement in a real network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core design ideas are described as follows. First, we design linear filters with various sizes termed IFEs to extract the different local interference patterns from outdated channel information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, so as to provide more effective power allocation strategies. Second, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network. Third, an input reduction process is also designed to reduce the input size from $O(N^{2})$ to $O(1)$ , which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

This article investigates a full-duplex orthogonal-frequency-division multiple access (OFDMA)-based multiple unmanned-aerial-vehicles (UAVs)-enabled wireless-powered Internet-of-Things (IoT) networks. In this paper, a swarm of UAVs is first deployed in 3-D to simultaneously charge all devices, i.e., a downlink (DL) charging period, and then flies to new locations within this area to collect information from scheduled devices in several epochs via OFDMA due to potential limited number of channels available in IoT during an uplink (UL) communication period. To maximize the UL throughput of IoT devices, we jointly optimize the UL-and-DL 3-D deployment of the UAV swarm, including the device-UAV association, the scheduling order, and the UL-DL time allocation. In particular, the DL energy harvesting threshold of devices and the UL signal decoding threshold of UAVs are taken into consideration when studying the problem. Besides, both line-of-sight and non-line-of-sight channel models are studied depending on the position of sensors and UAVs. The influence of potential limited number of channels in IoT is also considered. Guidelines on the 3-D placement of UAVs in the DL charging and the UL communications are also given. Finally, simulation results show that the proposed optimal time allocation OFDMA-UAV scheme achieves significant throughput gains compared with conventional schemes.

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.

With the recent advances in wireless communications, the automotive industry is leading to evolution. To succeed in this emerging era of technology, the Internet of Connected Vehicles (IoCV) has emerged as one of the potential applications of the Internet of Things (IoT). It refers to the dynamic mobile communication systems that communicate between vehicles and public networks to enhance the connectivity between cars via technology. By offering a wide variety of infotainment services, fleet operations, and in-vehicle applications, IoCV has gained the tremendous capacity to provide a safer and sustainable transportation system to the society. According to Gartner Inc., “the connected car is already a reality, and in-vehicle wireless connectivity is expanding rapidly.” As a result, the evolution of cars into the IoT will keep on accelerating the global market which is expected to grow by 270% by 2022. Furthermore, the increasing deployment of sensors and ever-evolving cognitive technology opens up new opportunities for IoCV. Due to these significant developments, connected vehicles are receiving widespread attention from the major automotive giants such as Tesla, BMW, Waymo (Google), Uber, Volvo, and so on. Despite all the opportunities offered by the IoCV, their highly dynamic topology and the increasing number of vehicles pose challenges regarding delivering low-latency vehicle-to-everything (V2X) communications.

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.

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment. We are interested in the joint design of active transmit beamformer at the PTx and passive reflecting beamformer at the STx to minimize the total transmit power at the PTx, subject to the signal-to-noise-ratio (SNR) constraint for the secondary transmission and the rate constraint for the primary transmission. Due to the non-convexity of the formulated problem, we decouple the original problem into a series of subproblems using the alternating optimization method and then iteratively solve them. The convergence performance and computational complexity of the proposed algorithm are analyzed. Furthermore, we develop a low-complexity algorithm to design the reflecting beamformer by solving a backscatter link enhancement problem through the semi-definite relaxation (SDR) technique. Then, theoretical analysis is performed to reveal the insights of the proposed system. Finally, simulation results are presented to validate the effectiveness of the proposed algorithms and the superiority of the proposed system.

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.

Dynamic spectrum access (DSA) is crucial to improve the utilization efficiency of the limited and precious radio spectrum resources. Recently, the application of blockchain is proposed to improve the security, distribution and transparency of DSA. However, in opportunistic spectrum access (OSA), the implementation of blockchain consumes considerable amount time in each time slot so that the time left for spectrum sensing and access will be decreased. Therefore, in this letter, we aim to optimize the frame structure regarding the sensing time and mining time so that the average achievable throughput is maximized. We first decouple the original optimization problem into two sub-optimization problems with respect to sensing time and mining time, respectively, and then prove that there exists a unique maximum point for both the two sub-optimization problems. After that, an alternating algorithm is proposed for the optimization. Using the simulations, the sensing-mining-access tradeoff and effectiveness of our proposed algorithm to optimize such a tradeoff are illustrated.

As a revolutionary hardware technology that can reconfigure the propagation environment, reconfigurable intelligent surfaces (RISs) have been regarded as a promising solution to enhance wireless networks. In this paper, we consider a multiuser multiple-input single-output (MISO) wireless power transfer (WPT) system, which is assisted by several RISs. In order to improve energy efficiency and reduce hardware cost, we consider that the energy transmitter (ET) in the WPT system is equipped with a constant-envelope analog beamformer, instead of a digital beamformer. Focusing on user fairness, we study a minimum received power maximization problem by jointly optimizing the ET beamforming and the RIS phase shifts, subject to the constant-envelope constraints. We iteratively solve this non-convex max-min problem by leveraging both the successive convex approximation (SCA) method and the alternating direction method of multipliers (ADMM) algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm and show attractive performance gain brought by RISs.

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.

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.

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.

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.

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 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.

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.

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.

Publisher Copyright: IEEE Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation,Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks.

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.

Large-dimensional (LD) random matrix theory, RMT for short, which originates from the research field of quantum physics, has shown tremendous capability in providing deep insights into large-dimensional systems. With the fact that we have entered an unprecedented era full of massive amounts of data and large complex systems, RMT is expected to play more important roles in the analysis and design of modern systems. In this paper, we review the key results of RMT and its applications in two emerging fields: wireless communications and deep learning. In wireless communications, we show that RMT can be exploited to design the spectrum sensing algorithms for cognitive radio systems and to perform the design and asymptotic analysis for large communication systems. In deep learning, RMT can be utilized to analyze the Hessian, input–output Jacobian and data covariance matrix of the deep neural networks, thereby to understand and improve the convergence and the learning speed of the neural networks. Finally, we highlight some challenges and opportunities in applying RMT to the practical large-dimensional systems.

To support massive access for future wireless communications, we propose a novel intelligent reflecting surface (IRS) enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system, where each IRS, acting as a reflecting Internet-of-Things (IoT) device, transmits its message to a nearby primary receiver (PR) by reflecting the RF signals from the primary transmitter (PT), and simultaneously enhances the transmission from the PT to the associated PR. Thus, each PR jointly decodes its own message as well as the one from the corresponding IRS. 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 IRS, subject to the maximum transmit power constraint at PT. Besides, as the passive elements at IRS can only reflect the incident signal with discrete phase shifts in practice, the discrete reflection coefficient (RC) constraint is further considered at the IRSs. Due to the non-convexity of the formulated problems, we solve them with fractional programming (FP) technique and alternating optimization (AO) method. Simulation results have verified the effectiveness of the proposed algorithms compared to different benchmark schemes.

The Open Radio Access Network (O-RAN) introducing a particular unit known as RAN Intelligent Controllers (RICs) has been regarded as revolutionary paradigms to support multiclass wireless services required in the fifth and sixth generation (5G/6G) networks. Through unprecedentedly installing various machine learning (ML) algorithms to RICs, a RAN is able to intelligently configure resources/communications to support any vertical applications over any operating scenarios. However, to practically deploy this RAN paradigm, the O-RAN still suffers two critical issues of load balance and handover control, and therefore the very first ML algorithm for the O-RAN should effectively address these issues. In this paper, inspired by the superior performance of deep reinforcement learning (DRL) in tackling sequential decision-making tasks, we therefore develop an intelligent user access control scheme with the facilitation of deep Q-networks (DQNs). A federated DRL-based scheme is further proposed to train the parameters of multiple DQNs in the O-RAN, so as to maximize the long-term throughput and meanwhile avoid frequent user handovers with a limited amount of signaling overheads in the O-RAN. The simulation results have fully demonstrated the outstanding performance over the state-of-the-arts, to service the urgent needs in the standardization of the O-RAN.

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.

As the era of Internet of Things is approaching, huge number of devices need to be included in the network, and the long standby time of devices should also be satisfied. To solve these problems, in this paper, a relay-aided multiple access (RAMA) transmission scheme based on network coding is considered in joint transmission with two base stations (BSs). In the scheme, the near user acts as a two-way relay node between the BS and the far user based on machine-to-machine communications for both downlink and uplink transmissions. At first for two users, non-orthogonal multiple access (NOMA) is employed to compare with the proposed RAMA scheme in terms of the achievable sum rates. From the analysis, we can show that for NOMA and RAMA, one outperforms the other under different conditions. Then, we consider the case of multiple user pairs, for which an adaptive RAMA-NOMA transmission scenario is developed to make full use of their respective advantages. In the scenario, an optimal power allocation scheme based on the branch and bound (BB) algorithm and a low-complexity user pairing scheme based on a greedy algorithm are proposed. Numerical results show that our proposed adaptive RAMA-NOMA transmission scenario can achieve a higher sum rate compared with RAMA or NOMA only scheme, and under the optimal power allocation based on BB, the performance of our proposed user pairing scheme is close to that of the optimal pairing method using an exhaustive search, and significantly outperforms the random selection method.

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.

Ambient backscatter communications (AmBC) enable wireless communications riding on ambient radio frequency (RF) signals instead of self-generated RF signals. Therefore, it has been considered as a promising candidate for the future Internet-of-Things with stringent energy and spectrum constraints. In this paper, we investigate a full-duplex-enabled cognitive backscatter network, in which an AmBC system underlays a primary cellular system, and the primary access point can transmit primary signals and receive backscatter signals simultaneously via full-duplex communications. We aim to maximize the throughput of the AmBC system while guaranteeing the minimum rate requirements of the primary system via joint time scheduling, transmit power allocation, and reflection coefficient (RC) adjustment. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into the time scheduling variable and the joint transmit power allocation and RC adjustment variable. For the time scheduling problem, we first prove its convexity and then utilize the interior-point method to solve it. For the joint power allocation and RC adjustment problem, we resort to the concave-convex procedure to transform it into a sequence of convex optimization problems, and then adopt Lagrange dual decomposition to tackle these convex optimization problems. The simulation results demonstrate that the proposed method can significantly increase the throughput of the AmBC system with a fast convergence speed.

The licensed-assisted access-based long term evolution (LAA-LTE) is a promising solution to provide enhanced LTE services by sharing unlicensed bands with WiFi systems. However, the intense contention with the incumbent WiFi system makes it challenging for the LAA-LTE system to support guaranteed quality-of-service (QoS) for the users. This paper is interested in the QoS-aware LAA-LTE/WiFi coexistence system. We first propose a flexible coexistence framework using the listen-before-talk mechanism, based on which the QoS metrics of LAA-LTE and WiFi systems are quantified. Then, a joint user association and resource allocation problem is formulated, which aims to maximize the number of QoS-preferred users supported by LAA-LTE, while protecting the WiFi users. The considered optimization problem is equivalently decomposed into two subproblems, the sum-power minimization problem and the user association problem. For the first subproblem, the deep-cut ellipsoid method is adopted to optimize the LAA-LTE transmission time, subcarrier assignment, and power allocation. For the latter one, an efficient algorithm called successive user removal is proposed. The simulation results have demonstrated the effectiveness of the proposed scheme, based on which the tradeoff among different QoS metrics in the coexistence system is observed.