Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA

Opportunistic beamforming (OBF) is an effective technique to improve the spectrum efficiencies (SEs) of multiple-input-multiple-output (MIMO) systems, which can obtain multiuser diversity gains with both low computation complexity and feedback information. To serve multiple users simultaneously, many multiple-access schemes have been researched in OBF. However, for most of the multiple-access schemes, the SEs are not satisfactory. To further improve the SE, this paper proposes a downlink multiuser OBF system, where both orthogonal frequency division multiplexing (OFDM) and non-orthogonal multiple-access (NOMA) methods are applied. The closed-form expressions of the equivalent channels and SE are derived in frequency selective fading channels. Then, an optimization problem is formulated to maximize the SE, although the optimization problem is non-convex and hard to solve. To obtain the solution, we divide the optimization problem into two suboptimal issues, and then a joint iterative algorithm is applied. In the proposed optimization scheme, the subcarrier mapping ϑ, user pairing knc and allocated power Pknc are determined to maximize spectrum efficiency (SE) and reduce bit error ratio (BER). According to numerical results, the proposed method achieves approximately 5 dB gain on both SE and BER, compared to the existing beamforming methods with low feedback information. Moreover, the SE of the proposed method is approximately 2 (bps/Hz) higher than sparse code multiple-access (SCMA), when the number of waiting users and the ratio of transmit power to noise variance are respectively 10 and 20 dB. It is indicated that the proposed scheme can achieve high and low BER with the limited feedback and computation complexity, regardless of the transmit power and the number of waiting users.     

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.     

New generalized zero forcing beamforming for serving more users in energy-harvesting enabled networks

This paper considers a multi-user wireless communication network supported by a multiple-antenna base station (BS), where the users who are located sufficiently close to the BS employ wireless energy harvesting (EH) to replenish their energy needs. The objective of this work is to design an efficient beamforming to maximize the minimum throughput among all the information users (IUs), subject to EH constraints. In this regard, transmit time-switching approach is employed, where energy and information are transmitted over different fractions of a time-slot. To achieve efficient EH, a conjugate beamforming (matched filtering) is applied. To design efficient information beamforming for max–min throughput optimization, conventional zero-forcing (ZF) beamforming can be adopted, however, it will not suppress multi-user interference if the number of users is greater than the number of antennas at the BS. To this end, different from the existing works which employ regularized zero-forcing (RZF) beamforming, this work proposes a new generalized zero-forcing (GZF) beamforming, which promises better max–min throughput compared to that achieved by the RZF beamforming. A new path-following algorithm is developed to achieve max–min throughput optimization by GZF beamforming, which is based on a simple convex quadratic program over each iteration.     

System throughput maximization in IRS-assisted phase cooperative NOMA networks

This work investigates performance of system throughput in intelligent reflecting surfaces (IRSs)-enabled phase cooperative non-orthogonal multiple access (NOMA) framework. By exploiting heterogeneous cognitive radio networks concept the aim is to maximize the sum rate of secondary users in the proposed phase cooperative downlink network configuration via optimization solutions. However, the optimization problem comes out to be NP-hard and precludes direct solution. Hence, an alternating optimization is applied at the primary network to solve the maximization problem by exploiting the transmit beamforming (BF) at the power station (PS) and phase shift optimization at the IRS. Later, sum rate maximization for secondary network is performed by utilizing phase shifts of primary network via phase cooperation. In order to find global optimal solutions for active beamformers at both PSs, a branch-reduce-and-bound (BRnB) method is used whereas, passive phase shift optimization at the primary PS is performed via a simple iterative solution, i.e., the element-wise block coordinate descent method. For the proposed framework, Monte-Carlo simulations are performed where the optimality of the global solution is compared with heuristic BF methods including minimum-mean-square-error/regularized zero-forcing-beamforming (ZFBF) and ZFBF. The BRnB algorithm sets an upper performance bound by improving the sum rate of users in comparison with the conventional heuristic BF schemes. This work signifies the utilization of phase cooperation in IRS-assisted NOMA networks for a multi-user environment.     

Particle swarm optimization based network selection in heterogeneous wireless environment

Deployment of heterogeneous wireless networks is spreading throughout the world as users want to be connected anytime, anywhere, and anyhow. Meanwhile, users are increasingly interested in multimedia applications such as audio, video streaming and Voice over IP (VoIP), which require strict Quality of Service (QoS) support. Provisioning of Always Best Connected (ABC) network with such constraints is a challenging task. Considering the availability of various access technologies, it is difficult for a network operator to find reliable criteria to select the best network that ensures user satisfaction while reducing multiple network selection. Designing an efficient Network selection algorithm, in this type of environment, is an important research problem. In this paper, we propose a novel network selection algorithm utilizing signal strength, available bit rate, signal to noise ratio, achievable throughput, bit error rate and outage probability metrics as criteria for network selection. The selection metrics are combined with PSO for relative dynamic weight optimization. The proposed algorithm is implemented in a typical heterogeneous environment of EDGE (2.5G) and UMTS (3G). Switching rate of the user between available networks has been used as the performance metric. Moreover, a utility function is used to maintain desired QoS during transition between networks, which is measured in terms of the throughput. It is shown here that PSO based approach yields optimal network selection in heterogeneous wireless environment.     

On spectrum sensing in cognitive radio CDMA networks with beamforming

In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%).     

3D beamforming in Intelligent Reflecting Surface (IRS)-assisted multi-user cognitive radio networks

In this paper, we consider a multi-user cognitive radio network (CRN) equipped with an intelligent reflecting surface (IRS). We examine the network performance by evaluating the fairness of the secondary system, which is satisfying the minimum required signal to interference and noise ratio (SINR) for each secondary user (SU). The minimum SINR of the SUs is maximized by joint optimization of the beamforming vector and three-dimensional beamforming (3DBF) angles at the secondary base station (SBS) and also the phase shifts of the IRS elements. This optimization problem is highly non-convex. To solve this problem, we utilize Dinkelbach’s algorithm along with an alternating optimization (AO) approach to achieve some sub-problems. Accordingly, by further applying a semi-definite relaxation method, we convert these sub-problems to equivalent convex forms and find a solution. Furthermore, analytically we propose an algorithm for optimizing 3DBF angles at the SBS. Through numerical results, the improvement of the sum SINR of the secondary system using the proposed method is illustrated. Moreover, it is shown that as the number of reflecting elements of IRS increases, the sum SINR significantly augments while satisfying fairness. Also, the convergence of the proposed algorithm is verified utilizing numerical results.     

Intelligent resource allocation for transmission security on IRS-assisted spectrum sharing systems with OFDM

In this paper, intelligent reflecting surface (IRS) technology is employed to enhance physical layer security (PLS) for spectrum sharing communication systems with orthogonal frequency division multiplexing (OFDM). Aiming to improve the secondary users’ secrecy rates, a design problem for jointly optimizing the transmission beamforming of secondary base station (SBS), the IRS’s reflecting coefficient and the channel allocation is formulated under the constraints of the requirements of minimum data rates of primary users and the interference between users. As the scenario is highly complex, it is quite challenging to address the non-convexity of the optimization problem. Thus, a deep reinforcement learning (DRL) based approach is taken into consideration. Specifically, we use dueling double deep Q networks (D3QN) and soft Actor–Critic (SAC) to solve the discrete and continuous action space optimization problems, respectively, taking full advantage of the maximum entropy RL algorithm to explore all possible optimal paths. Finally, simulation results show that our proposed approach has a great improvement in security transmission rate compared with the scheme without IRS and OFDM, and our proposed D3QN-SAC approach is more effective than other approaches in terms of maximum security transmission rate.     

A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems

The future 6G mobile communication network will support an unprecedented amount of Internet of Things (IoT) devices, which will boost the demand for low cost terminals under the principle of green communication. One of the critical issues for low cost terminals is the sampling rate of analog-to-digital converters (ADCs) at the receivers. A high sampling rate of the ADC gives rise to a high energy consumption and high hardware cost for the terminal. In the conventional multi-user OFDM systems, all users have to sample the received signal with a sampling rate that is larger than or equal to the Nyquist rate, despite only a small fraction of the bandwidth (number of subcarriers) is allocated to each user. This paper proposes a low sampling rate receiver design for multi-antenna multi-user OFDM systems. With the aid of zero-forcing precoding, the sampling rate of the receiver can be reduced to 1/K of the Nyquist rate, where K is the number of users. The simulation results show that with a significant reduction in sampling rate, performance loss is insignificant and acceptable in terms of bit error rate, mutual information and peak-to-average power ratio.     

Energy-efficient beamforming optimization for MISO communication based on reconfigurable intelligent surface

Reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiency (EE) by reconfiguring the wireless propagation environment. In this paper, we propose a base station (BS) beamforming and RIS phase shift optimization technique that maximizes the EE of a RIS-aided multiple-input–single-output system. In particular, considering the system circuits’ energy consumption, an EE maximization problem is formulated by jointly optimizing the active beamforming at the BS and the passive beamforming at the RIS, under the constraints of each user’ rate requirement, the BS’s maximal transmit power budget and unit-modulus constraint of the RIS phase shifts. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. To overcome this obstacle, we divide the problem into active and passive beamforming optimization subproblems. For the first subproblem, the active beamforming is given by the maximum ratio transmission optimal strategy. For the second subproblem, the optimal phase shift matrix at the RIS is obtained by exploiting sine cosine algorithm (SCA). Moreover, for this case where each reflection element’s working state is controlled by a circuit switch, each reflection element’s switch value is optimized with the aid of particle swarm optimization algorithm. Finally, numerical results verify the effectiveness of our proposed algorithm compared to other algorithms.     

Performance of multi-user transmitter preprocessing assisted MIMO system over correlated frequency-selective channels

In this contribution we present the performance of a multi-user transmitter preprocessing (MUTP) assisted multiple-input multiple-output (MIMO) space division multiple access (SDMA) system, aided by double space time transmit diversity (DSTTD) and space time block code (STBC) processing for downlink (DL) and uplink (UL) transmissions respectively. The MUTP is invoked by singular value decomposition (SVD) which exploits the channel state information (CSI) of all the users at the base station (BS) and only an individual user’s CSI at the mobile station (MS). Specifically, in this contribution, we investigate the performance of multi-user MIMO cellular systems in frequency-selective channels from a transmitter signal processing perspective, where multiple access interference (MAI) is the dominant channel impairment. In particular, the effects of three types of delay spread distributions on MUTP assisted MIMO SDMA systems pertaining to the Long Term Evolution (LTE) channel model are analyzed. The simulation results demonstrate that MUTP can perfectly eliminate MAI in addition to obviating the need for complex multi-user detectors (MUDs) both at the BS and MS. Further, SVD-based MUTP results in better achievable symbol error rate (SER) compared to popularly known precoding schemes such as block diagonalization (BD), dirty paper coding (DPC), Tomlinson–Harashima precoding (THP) and geometric mean decomposition (GMD). Furthermore, when turbo coding is invoked, coded SVD aided MUTP results in better achievable SER than an uncoded system.     

Two-level-enforced security with hybrid precoding for multicast massive MIMO wiretap systems

Multicast hybrid precoding reaches a compromise among hardware complexity, transmission performance and wireless resource efficiency in massive MIMO systems. However, system security is extremely challenging with the appearance of eavesdroppers. Physical layer security (PLS) is a relatively effective approach to improve transmission and security performance for multicast massive MIMO wiretap systems. In this paper, we consider a transmitter with massive antennas transmits the secret signal to many legitimate users with multiple-antenna, while eavesdroppers attempt to eavesdrop the information. A fractional problem aims at maximizing sum secrecy rate is proposed to optimize secure hybrid precoding in multicast massive MIMO wiretap system. Because the proposed optimized model is an intractable non-convex problem, we equivalently transform the original problem into two suboptimal problems to separately optimize the secure analog precoding and secure digital precoding. Secure analog precoding is achieved by applying singular value decomposition (SVD) of secure channel. Then, employing semidefinite program (SDP), secure digital precoding with fixed secure analog precoding is obtained to ensure quality of service (QoS) of legitimate users and limit QoS of eavesdroppers. Complexity of the proposed SVD-SDP algorithm related to the number of transmitting antennas squared is lower compared with that of constant modulus precoding algorithm (CMPA) which is in connection with that number cubed. Simulation results illustrate that SVD-SDP algorithm brings higher sum secrecy rate than those of CMPA and SVD-SVD algorithm.     

Robust secrecy rate maximization in IRS-assisted cognitive radio networks

In this paper, we focus on the secrecy rate maximization problem in intelligent reflecting surface (IRS)-assisted cognitive radio (CR) networks. In order to improve the security, there is a common scheme to add artificial noise (AN) to the transmitted signal, which is also applied in this paper. Further, in CR networks, the secondary users always cannot obtain accurate channel state information (CSI) about the primary user and eavesdropper. By taking jointly design for the IRS phase shift matrix, the transmitted beamforming of the secondary base station (BS), and the covariance matrix of AN, our objective is to maximize the minimal secrecy rate of all secondary users. Due to the serious coupling among the designed variables, it cannot be solved by conventional methods. We propose an alternating optimization (AO) algorithm. In simulation results, we apply primary users and secondary users randomly distributed in the communication area, which numerically demonstrate the superiority of our proposed scheme.     

Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications

We have studied massive MIMO hybrid beamforming (HBF) for millimeter-wave (mmWave) communications, where the transceivers only have a few radio frequency chain (RFC) numbers compared to the number of antenna elements. We propose a hybrid beamforming design to improve the system’s spectral, hardware, and computational efficiencies, where finding the precoding and combining matrices are formulated as optimization problems with practical constraints. The series of analog phase shifters creates a unit modulus constraint, making this problem non-convex and subsequently incurring unaffordable computational complexity. Advanced deep reinforcement learning techniques effectively handle non-convex problems in many domains; therefore, we have transformed this non-convex hybrid beamforming optimization problem using a reinforcement learning framework. These frameworks are solved using advanced deep reinforcement learning techniques implemented with experience replay schemes to maximize the spectral and learning efficiencies in highly uncertain wireless environments. We developed a twin-delayed deep deterministic (TD3) policy gradient-based hybrid beamforming scheme to overcome Q-learning’s substantial overestimation. We assumed a complete channel state information (CSI) to design our beamformers and then challenged this assumption by proposing a deep reinforcement learning-based channel estimation method. We reduced hybrid beamforming complexity using soft target double deep Q-learning to exploit mmWave channel sparsity. This method allowed us to construct the analog precoder by selecting channel dominant paths. We have demonstrated that the proposed approaches improve the system’s spectral and learning efficiencies compared to prior studies. We have also demonstrated that deep reinforcement learning is a versatile technique that can unleash the power of massive MIMO hybrid beamforming in mmWave systems for next-generation wireless communication.     

虚拟时间反转镜啁啾率键控的水声多用户通信

针对频率选择性衰落严重的浅海信道多用户通信问题,提出了一种虚拟时间反转镜(VTRM,Virtual Time Reversal Mirror)啁啾率键控(CRK,Chirp-Rate Keying)的水声多用户通信方式。该算法采用啁啾信号作为载波,接收端利用啁啾信号中心频率与分数阶傅里叶变换域的关系还原出原始信息,并使用虚拟时间反转镜技术减少了多径环境对系统的影响,采用啁啾调频率分集作为多址接入方式。在10 kHz的带宽内,可支持4个用户进行可靠通信,每用户通信速率425 bps。水池及海洋实验结果表明:该算法能够有效地传输数据,并且误码率在10-3数量级以下。      

On the ergodic sum rate for multiuser satellite–aerial–terrestrial networks

In this paper, we investigate the ergodic sum rate (ESR) for the downlink of a multi-user satellite–aerial–terrestrial network (SATN) with decode-and-forward (DF) protocol, where a multi-antenna unmanned aerial vehicle (UAV) acts as an aerial relay to assist the signal convey from satellite to multiple terrestrial users, which is also corrupted by co-channel interference. To maximize the ESR of the considered system, a beamforming (BF) scheme based on statistical channel state information is proposed to conduct space division multiple access (SDMA) in the UAV–terrestrial links. Then, by assuming that the satellite–UAV link and the UAV–terrestrial links undergo correlated Shadowed-Rician fading and correlated Rayleigh fading, respectively, we derive the analytical expression of ESR for our considered system with proposed BF scheme. Finally, numerical results are provided to verify the correctness of the theoretical analysis and demonstrate the superiority of our proposed BF scheme.     

Resource allocation in relay-assisted MIMO MAC systems with statistical CSI

In this work, the issue of non-cooperative resource allocation in the uplink of a relay-assisted MIMO MAC (multiple input multiple output multiple access channel) system with statistical CSI (channel state information) is considered. The mobile transmitters pursue individual achievable ergodic rate maximization, whereas the relay aims at optimizing the global performance of the system. The problem is formulated as a Stackelberg game with the relay as the leader, and the multiple access users as the followers. Moreover, necessary and sufficient conditions for beamforming optimality at the relay are derived, which simplifies the resource allocation process. Finally, numerical results corroborate the theoretical findings.     

Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.     

Joint optimization of component carrier selection and resource allocation in 5G carrier aggregation system

In this paper, we consider joint optimization of Component Carrier (CC) selection and resource allocation in 5G Carrier Aggregation (CA) system. Firstly, the upper-bound system throughput with determined number of CCs is derived and it is proved by using graph theory that the throughput optimization problem is NP hard. Then we propose a greedy based algorithm to solve this problem and prove that the proposed algorithm can achieve at least 1/2 of the optimal performance in the worst case. At last, we evaluate the throughput and computational complexity performance through a variety of simulations. Simulation results show that the proposed algorithm can obtain better performance comparing with existing schemes while keeping the computation complexity at an acceptable level.     

Low complexity optimization for user centric cellular networks via large dimensional analysis

Users near cell edges suffer from severe interference in traditional cellular networks. In this paper, we consider the scenario that multiple nearby base stations (BSs) cooperatively serve a group of users which is referred to as the cell free networks. A low complexity optimization method based on the large dimensional analysis is proposed. The advantage of the cell free networks is that the interference caused in the cell edge users can be converted into intended signal. It is not easy to obtain the optimal solution to the network due to coupled relations among the users’ rates. To obtain a suboptimal solution, a precoder that balances signal and interference is adopted to maximize the network capacity. In traditional optimization, it requires instantaneous channel state information. We try to optimize the network sum rate based on the large dimensional analysis. In this way, the optimization can be transformed into another problem that merely depend on the large scale channel statistics. Large dimensional analysis is leveraged to derive the asymptotic signal to interference plus noise ratio that only depends on large scale channel statistics. Based on this result, the power allocation problem does not need to adapt as frequently as the instantaneous channel state information. By this means, signal exchange overhead can be greatly reduced. Numerical results are provided to validate the efficacy of the proposed optimization method.