ANALYSIS OF TRANSMIT BEAMFORMING PERFORMANCE ON MULTIPLE-TRANSMIT BINARY ZCZ WAVEFORMS

The Transmit BeamForming (TBF) technology, applied in a multiple-transmit radar system, is studied in this paper, where multiple elements of antenna array transmit binary Zero Correlation Zones Orthogonal Signals (ZCZ-OS) independently. For each Direction Of Arrival (DOA) with respect to the transmitting array, the analysis on the gain and sidelobe level of TBF output is presented. This paper focuses on the range sidelobes performance within the main beam (in angle domain). For the normal direction, due to the inherent phase property of ZCZ-OS, the TBF output has part zero sidelobes area, of which the distribution is discussed. For the other directions, a systematic search algorithm to optimize the transmission order of signals is proposed for an optimal relationship chart of DOA and transmission order. The range sidelobe performance within the main beam can be improved as the optimal transmission order is adopted.

Capacity analysis of transmit beamforming with variable feedback delay

The feedback delay can severely affect the quality of the channel state information at the transmitter （CSIT） which is fed back from the receiver. The outdated CSIT will cause large performance loss in the transmit beamforming systems. The effect of variable feedback delay on the capacity of transmit beamforming systems over Rayleigh fading channels is studied. First, the case of fixed feedback delay is considered and a closed-form expression of system capacity is derived. Based on the results of fixed delay, the delay following certain distributions in variable delay case is assumed and the closed-form expressions of capacities are derived. The closed-form expressions show that the capacity is significantly affected by the statistical characteristics of the feedback delay. The obtained results provide an analytical insight into the effects caused by variable delay on the system capacity.

A New Transmit Beamforming Method for Multi-User Communication in Dual-Function Radar-Communication

This paper develops a new transmit beamforming for an integrated mechanical and electrical scanning dual-function radar-communication(DFRC)system.Differing from the related some works using beampattern sidelobe level to communication,we exploit the fact that transmit beamforming weight vector u k in directionθand weight vector u*k in direction-θcan achieve the same spatial power distribution,and formulate a new transmit beamforming vector design problem accounting for some extra sidelobe level constraints.By doing so,the number of the transmit beamforming weight vectors and the computing demand in the multi-user communication(MUC)scenario can be reduced.Finally,the numerical examples are designed to verify the effectiveness of the proposed design strategy in comparison with the existing method.

Transmit Power Minimization for IRS-Assisted NOMA-UAV Networks

The flexibility of unmanned aerial vehicles(UAVs)allows them to be quickly deployed to support ground users.Intelligent reflecting surface(IRS)can reflect the incident signal and form passive beamforming to enhance the signal in the specific direction.Motivated by the promising benefits of both technologies,we consider a new scenario in this paper where a UAV uses non-orthogonal multiple access to serve multiple users with IRS.According to their distance to the UAV,the users are divided into the close users and remote users.The UAV hovers above the close users due to their higher rate requirement,while the IRS is deployed near the remote users to enhance their received power.We aim at minimizing the transmit power of UAV by jointly optimizing the beamforming of UAV and the phase shift of IRS while ensuring the decoding requirement.However,the problem is non-convex.Therefore,we decompose it into two sub-problems,including the transmit beamforming optimization and phase shift optimization,which are transformed into second-order cone programming and semidefinite programming,respectively.We propose an iterative algorithm to solve the two sub-problems alternatively.Simulation results prove the effectiveness of the proposed scheme in minimizing the transmit power of UAV.

Joint transmit beamforming and power control in multi-user MIMO downlink using a game theoretic approach

This study addresses the problem of jointly optimizing the transmit beamformers and power control in multi-user multiple-input multiple-output （MIMO） downlink. The objective is minimizing the total transmission power while satisfying the signal-to-noise plus interference ratio （SINR） requirement of each user. Before power control, it uses the maximum ratio transmission （MRT） scheme to determine the beamformers due to its attractive properties and the simplicity of handling. For power control it introduces a supermodular game approach and proposes an iterated strict dominance elimination algorithm. The algorithm is proved to converge to the Nash equilibrium. Simulation results indicate that this joint optimization method assures the improvement of performance.

Downlink scheduling of multiuser MIMO systems with transmit beamforming

This article deals with downlink scheduling for multiuser multiple-input multiple-output （MIMO） systems, where the base station communicates with multiple users simultaneously through transmit beamforming. Most of the existing transmission schemes for multiuser MIMO systems focus on optimizing sum rate performance of the system. The individual quality of service （QoS） requirements （such as packet delay and minimum transmission rate for the data traffic） are rarely considered. In this article, a novel scheduling strategy is proposed, where we try to optimize the global system performance under individual QoS constraints. By performing scheduling into two steps, namely successive user selection and power allocation, the scheduler can achieve efficient resource utilization while maintaining the QoS requirements of all users. Extensive simulations and analysis are given to show the effectiveness of the proposed scheduler.

ROBUST OPTIMIZATION OF TRANSMIT/RECEIVE BEAMFORMER FOR MIMO DOWNLINK WITH IMPERFECT CSI AT BASE STATION

A robust scheme is proposed to jointly optimize transmit/receive beamformers for Mul-tiple Input Multiple Output(MIMO) downlinks where the available Channel State Information(CSI) at Base Station(BS)(CSIBS) is imperfect.The criterion is to minimize the sum Mean Square Error(sum-MSE) over all users under a constraint on the total transmit power,which is a non-convex and non-linear problem.Observing from the first order optimization condition that the optimal trans-mit/receive beamformers are mutually dependent,the transmit/receive beamformers for each user are updated iteratively until the sum-MSE is minimized.Simulation results indicate that the proposed scheme can effectively mitigate the system performance loss induced by imperfect CSIBS.

Discrete Phase Shifts Control and Beam Selection in RIS-Aided MISO System via Deep Reinforcement Learning

Reconfigurable intelligent surface(RIS)for wireless networks have drawn lots of attention in both academic and industry communities.RIS can dynamically control the phases of the reflection elements to send the signal in the desired direction,thus it provides supplementary links for wireless networks.Most of prior works on RIS-aided wireless communication systems consider continuous phase shifts,but phase shifts of RIS are discrete in practical hardware.Thus we focus on the actual discrete phase shifts on RIS in this paper.Using the advanced deep reinforcement learning(DRL),we jointly optimize the transmit beamforming matrix from the discrete Fourier transform(DFT)codebook at the base station(BS)and the discrete phase shifts at the RIS to maximize the received signal-to-interference plus noise ratio(SINR).Unlike the traditional schemes usually using alternate optimization methods to solve the transmit beamforming and phase shifts,the DRL algorithm proposed in the paper can jointly design the transmit beamforming and phase shifts as the output of the DRL neural network.Numerical results indicate that the DRL proposed can dispose the complicated optimization problem with low computational complexity.

Secure Multigroup Multicast Communication Systems via Intelligent Reflecting Surface

This paper considers a secure multigroup multicast multiple-input single-output(MISO)communication system aided by an intelligent reflecting surface(IRS).Specifically,we aim to minimize the transmit power at Alice via jointly optimizing the transmit beamformer,artificial noise(AN)vector and phase shifts at the IRS subject to the secrecy rate constraints as well as the unit modulus constraints of IRS phase shifts.To tackle the optimization problem,we first transform it into a semidefinite relaxation(SDR)problem,and then alternately update the transmit beamformer and AN matrix as well as the phase shifts at the IRS.In order to reduce the high computational complexity,we further propose a low-complexity algorithm based on second-order cone programming(SOCP).We decouple the optimization problem into two sub-problems and optimize the transmit beamformer,AN vector and the phase shifts alternately by solving two corresponding SOCP subproblem.Simulation results show that the proposed SDR and SOCP schemes require half or less transmit power than the scheme without IRS,which demonstrates the advantages of introducing IRS and the effectiveness of the proposed methods.

Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks

Owing to the inherent central information processing and resource management ability,the cloud radio access network(C-RAN)is a promising network structure for an intelligent and simplified sixth-generation(6G)wireless network.Nevertheless,to further enhance the capacity and coverage,more radio remote heads(RRHs)as well as high-fidelity and low-latency fronthaul links are required,which may lead to high implementation cost.To address this issue,we propose to exploit the intelligent reflecting surface(IRS)as an alternative way to enhance the C-RAN,which is a low-cost and energy-efficient option.Specifically,we consider the uplink transmission where multi-antenna users communicate with the baseband unit(BBU)pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs.RRHs can conduct either point-to-point(P2P)compression or Wyner-Ziv coding to compress the received signals,which are then forwarded to the BBU pool through fronthaul links.We investigate the joint design and optimization of user transmit beamformers,IRS passive beamformers,and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding.By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation(SDR),we propose a successive convex approximation approach to solve non-convex problems,and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided.Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

Statistical assessment of selection-based dual-hop semi-blind amplify-and-forward cooperative networks

For multiple-relay cooperative networks with multiple antennas deployed at source and destination nodes,we investigate the outage performance of selection based semi-blind amplify-and-forward(AF) relaying,where transmit beamforming(TB) is conducted at source transmission and maximum ratio combining(MRC) at destination reception.Based on the Kolmogorov-Smirnov test,we analyze the impact of the configuration of destination antennas on the outage performance under arbitrary Nakagami-m fading channels.Results reveal that increasing the number of destination antennas is not necessary for an improvement of outage performance with any Nakagami-m parameter.Inspired by this fact,an approximation is proposed for the optimal selection.Simulation results show that the approximation is an efficient selection method.