Asymptotic Analysis and Precoding Design of Integrated Access and Backhaul in Full-Duplex mmWave Networks

In this paper,we consider a full-duplex(FD)millimeter wave(mmWave)multiuser integrated access and backhaul(IAB)system with massive MIMO,and the system asymptotic performance and interference cancellation schemes are investigated.First,the asymptotic performance of the IAB system with massive MIMO is analyzed.As the number of macro base station(MBS)and small base station(SBS)antennas approaches infinity,the FD selfinterference(SI),inter-tier interference and noise can be eliminated,which means that only multiuser interference remains in the system.Then,multiuser interference can be suppressed by the base band(BB)precoders.Since all interference and noise are suppressed,the spectral efficiency of the SBS and users are infinite in theory.Then,two interference suppression precoding schemes are proposed.A block diagonalization(BD)-based interference cancellation scheme is designed based on the channel characteristics and null space projection.The FD SI,intertier interference and multiuser interference are eliminated by BB precoders.Instead of eliminating interference completely,a signal to leakage and noise ratio(SLNR)-based precoding scheme is derived to suppress both interference and noise.By utilizing the Rayleigh-Ritz theorem,the SLNRs of the SBS and users are optimized.Simulation results show that all the interference can be effectively eliminated by the BD-based scheme at the cost of spectral efficiency performance loss,while the SLNR-based scheme can balance interference and noise and achieve higher spectral efficiency with comparatively low interference level.Therefore,the BD-based scheme is more suitable for interference elimination cases,and the SLNRbased scheme can improve the system performance in low interference scenarios.

THE INNER-SYSTEM LABELING ALGORITHM AND ITS FAIRNESS ANALYSIS

On the basis of inner-system labeling signaling used in the integrated access system,a kind of inner-system labeling algorithm is introduced in this paper, and the fairness of the algorithm for each traffic stream in the integrated-services is analyzed. The base of this algorithm is Class of Services (CoS), and each packet entering the relative independent area (an autonomous system) would be labeled according to the service type or Quality of Service (QoS) in demand,and be scheduled and managed within the system (the system can be enlarged if conforming to the same protocol). The experimental results show that each of the stream rate in the integratedservices would converge to a stable value if the rates of transmitting converge to that of the receiving exponentially, that is, the effective traffic of each stream would be fair.

Joint User Association and Resource Allocation for mmWave Communication:A Neural Network Approach

With the rapid growth of wireless data demand and the shortage of global bandwidth,the use of millimeter-wave(mmWave)frequency band for wireless cellular networks has become the core content of the fifth generation cellular network.Because mmWave communication has different characteristics from microwave communication,using traditional optimization techniques to manage the resource of mmWave communication networks is inappropriate.In this paper,we propose a neural network-based algorithm to solve the joint user association and resource allocation for mmWave communication system with multi-connectivity(MC)and integrated access backhaul(IAB).The resource allocation problem is formulated as a mixed-integer quadratically constrained quadratic programming(MIQCQP),which is very difficult to solve.First,we decompose the MIQCQP into two sub-problems,i.e.,binary associated matrix sub-problem and continuous IAB ratio sub-problem.Then we propose a neural network to solve the binary associated matrix inference problem and a resource allocation algorithm to find the sub-optimal IAB ratio.Simulation results show that the proposed algorithm can achieve good performance with a fast inference speed.