Integrating wireless sensing capabilities into base stations (BSs) has become a widespread trend in the future beyond fifth-generation (B5G)/sixth-generation (6G) wireless networks. In this paper, we investigate intelligent reflecting surface (IRS) enabled wireless localization, in which an IRS is deployed to assist a BS in locating a target in its non-line-of-sight (NLoS) region. In particular, we consider the case where the BS-IRS channel state information (CSI) is unknown. Specifically, we first propose a separate BS-IRS channel estimation scheme in which the BS operates in full-duplex mode (FDM), i.e., a portion of the BS antennas send downlink pilot signals to the IRS, while the remaining BS antennas receive the uplink pilot signals reflected by the IRS. However, we can only obtain an incomplete BS-IRS channel matrix based on our developed iterative coordinate descent-based channel estimation algorithm due to the “sign ambiguity issue”. Then, we employ the multiple hypotheses testing framework to perform target localization based on the incomplete estimated channel, in which the probability of each hypothesis is updated using Bayesian inference at each cycle. Moreover, we formulate a joint BS transmit waveform and IRS phase shifts optimization problem to improve the target localization performance by maximizing the weighted sum distance between each two hypotheses. However, the objective function is essentially a quartic function of the IRS phase shift vector, thus motivating us to resort to the penalty-based method to tackle this challenge. Simulation results validate the effectiveness of our proposed target localization scheme and show that the scheme’s performance can be further improved by finely designing the BS transmit waveform and IRS phase shifts intending to maximize the weighted sum distance between different hypotheses.

The cell-free network (CFN) is a promising technology capable of delivering high-reliability, high-data rate wireless communication services for Metaverse communication. This paper studies a joint optimization problem of user scheduling (US) and beamforming (BF) in CFN, where constraints of per access point (AP) power and the limited number of the scheduled users per AP are considered. In order to reduce the interaction overhead, this problem is investigated using local channel state information (CSI). Since this problem is a mixed-integer nonlinear programming (MINP) program with non-convexity and high complexity, we propose an alternating optimization framework to solve this problem. Specifically, we first adopt the weighted$l_{1}$-norm approximation to transform the discrete variables into the continuous variables. Then, we solve the rest of the problem by fractional programming, and solve the subproblems alternatively. The analysis of complexity and convergence analysis validate the efficiency and accuracy of the proposed algorithm. Numerical results show that the cross-layer design of the US&BF scheme is superior to the separate design of US&BF schemes. In addition, the proposed algorithm with local CSI achieves a comparable data rate to the algorithms with global CSI.

In this paper, the cross-layer optimization problem of access point selection (APS) and beamforming (BF) in cell-free network (CFN) with local CSI is studied, where constraints of per AP power and the number of active APs are considered. Such a joint APS&BF optimization problem is modeled as a mixed-integer nonlinear programming (MINP) problem aiming at maximizing the sum rate of the whole system. Fractional programming (FP)-based and alternating optimization (AO)-based algorithms with weightedl1-norm approximation are proposed to solve this MINP problem. However, the latter performs better than the former, with higher complexity. A lightweight multi-head single-body graph neural network (MHSB-GNN) algorithm is proposed, where the nodes and structures are innovatively designed. The MHSB-GNN benefits from the different node updating modules for different user equipment (UE), which introduce extra prior information into the graph and mine specific information of different UEs. Moreover, the equivalence between GNN and FP-based algorithm is proved to provide interpretability and theoretical guarantees for MHSB-GNN. The analysis of convergence and complexity validates the accuracy and effectiveness of the FP and AO-based algorithms. Leveraging the existing APS and BF solver, it is shown that the three proposed algorithms guarantee comparable performance as the exhaustive search algorithm in performance and complexity.

In this paper, a cross-layer optimization problem of access point selection (APS) and beamforming (BF) in cell free network (CFN) has been studied, where constraints of per access point (AP) power and per user receiving data streams are considered. Such a cross-layer design of APS&BF problem is modeled as a mixed-integer nonlinear programming (MINP) program. Then, by adopting the weighted l1-norm approximation, the MINP problem is transformed into the sum logarithmic multiple-ratio form. To be specific, a novel and low-complexity mix-integer fractional programming (MIFP) algorithm is proposed to solve the transformed problem effectively. Convergence analysis validates that the proposed MIFP converges to a local optimal solution. Finally, numerical results show that cross-layer design of APS&BF scheme is superior to separate design of APS&BF schemes. In addition, the proposed MIFP has the approximate performance as partial exhaustive search algorithm.