Federated learning (FL) is a privacy-preserving technique that enables local model training on devices without raw data sharing. However, a critical challenge in FL lies in the communication requirement of uploading the trained models to servers, which can be hindered by interference from ambient devices, particularly in unreliable wireless environments. To address this, hierarchical FL (HFL) introduces an additional intermediate layer where the edge server performs work aggregation from the devices nearby, aiming at reducing the communication load and improving the efficiency of model training. However, existing approaches suffer from two critical limitations. First, they fail to fully quantify the impact of device competition-induced interference on transmission performance, which leads to unacceptably high upload latency and low success upload probability (SUP). Second, they lack a targeted optimization strategy to balance model accuracy and transmission efficiency under dynamic interference conditions. To address these critical limitations and mitigate their adverse impacts on FL performance, we take these gaps as the core motivation of our work and propose a targeted solution. Specifically, we first model the network as a two-layer binomial point process (BPP), which allows us to analyze the network-layer performance and calculate the SUP for the trained model. Based on this model, we propose optimizing cluster selection to balance accuracy and latency, thereby enhancing overall FL performance. We formulate this optimization as a Markov decision process (MDP) and solve it using a twin-delayed deep deterministic policy gradient (TD3)-based cluster selection algorithm (CS-TD3). In addition, to guarantee network-layer performance and enhance the efficiency of HFL, we employ an experimental exhaustive search algorithm to find the best solution within a limited range. The experimental results show that our algorithm overperforms other commonly-used algorithms in terms of HFL accuracy and model transmission latency, achieving a 10.95% improvement over the other methods.

Unmanned aerial vehicle (UAV) is expected to bring transformative improvements to the integrated sensing and communication (ISAC) systems, due to its high flexibility, high autonomy, large coverage and strong adaptability to various terrains. Sensory data is gathered by sensing UAVs (SUs) from the coverage area and then relayed to the corresponding fusion center UAVs (FCUs). Afterwards, terrestrial base stations receive the sensory data from FCUs in such air-ground networks. However, due to complex task execution environment and transmission environment, it is challenging to capture the network-layer performance of the sensory data transmission and evaluate the trade-off relationship between sensing and communication. In this work, we model and analyze the network-layer delay violation for an ISAC UAV network to address this challenge. Specifically, the UAV formation is distributed according to a Poisson cluster process (PCP). Then, the successful sensing probability is derived, with which the sensory data traffic can be captured. Under the sensory data flow, the delay violation probability is calculated for the two-stage sensory data transmission queue by exploiting stochastic network calculus (SNC). Furthermore, a delay minimization problem is proposed to reveal the trade-off relationship between sensing and communication under the power allocation strategy. Based on the long-term network-layer queue backlog evaluated, we are devoted to analyze the delay violation probability under an emergency that results in the antenna misalignment for one typical sensing UAV during a certain period. The steady-state and transient analysis for the ISAC UAV network not only illustrate the trade-off relationship between sensing and communication for the network, but also provide insights for on-demand power allocation, network deployment, control module provisioning and sensory data flow control under certain performance requirements.

This paper investigates the secure hybrid beamforming (HB) design in an intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) system, where an IRS is deployed to help the legitimate transmission from Alice to Bob under the eavesdropping of Eve. To protect the legitimate transmission, Alice employs HB to send both the information signal and the artificial noise, while the IRS employs passive beamforming (PB) to reconstruct the wireless environment. Aiming at the secrecy capacity (SC) maximization, the joint optimization of HB and PB is formulated as a non-convex problem with constant-modulus constraints. To efficiently solve such a challenging problem, the original problem is decomposed into a PB subproblem and an HB subproblem, then these subproblems are sequentially solved by the proposed algorithms. Particularly, for the PB subproblem, we propose a channel information aided PB algorithm, which is proved to converge at a stationary point. With the solution of PB subproblem, two algorithms are proposed for the HB subproblem: 1) near-optimal SC approaching HB algorithm that achieves a near-optimal solution; 2) low-complexity HB algorithm that achieves a slight lower SC with less computational complexity. Simulation results demonstrate the superior performance of proposed algorithms in comparison with the state-of-the-art works.

This paper presents an efficient quadrature amplitude modulation (QAM) signal detector for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM). The content of the paper is summarized as follows: first, we formulate QAM-MIMO detection as a maximum-likelihood optimization problem with bound relaxation constraints. Decomposing QAM signals into a sum of multiple binary variables and exploiting introduced binary variables as penalty functions, we transform the detection optimization model to a non-convex sharing problem; second, a customized ADMM algorithm is presented to solve the formulated non-convex optimization problem. In the implementation, all variables can be solved analytically and in parallel; third, it is proved that the proposed PS-ADMM algorithm converges under mild conditions. Simulation results demonstrate the effectiveness of the proposed approach.

In this paper, we design two efficient quadrature amplitude modulation (QAM) signal detectors for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM). The content of the paper is summarized as follows: first, we transform the maximum-likelihood detection model to a non-convex sharing optimization problem for massive MIMO-QAM systems, where a high-order QAM constellation is decomposed to a sum of multiple binary variables, integer constraints are relaxed to box constraints, and quadratic penalty functions are added to the objective function to result in a favorable integer solution; second, a customized ADMM algorithm, called PS-ADMM, is presented to solve the formulated non-convex optimization problem. In the implementation, all variables in each vector can be solved analytically and in parallel; and third, in order to solve the penalty-sharing distributively, we improve the proposed PS-ADMM algorithm to a distributed one, named DPS-ADMM. In the end, performance analyses of the proposed two algorithms, including convergence properties and computational cost, are provided. Simulation results demonstrate the effectiveness of the proposed approaches.

Unimodular sequences with good correlation properties are desired in wireless communication and radar applications. In this paper, we focus on designing these kinds of sequences and the main content is as follows: first, we formulate the design problem as a quartic polynomial minimization problem with constant modulus constraints. Then, by introducing auxiliary variables, the polynomial minimization problem is equivalent to a nonconvex consensus problem. Second, we develop a low-complexity consensus parallel direction method of multipliers (consensus-PDMM) algorithm, in which all subproblems can be performed in parallel with analytical solutions. Moreover, we prove that consensus-PDMM's output is some stationary point of the original nonconvex problem if it is convergent. Third, two variant PDMM algorithms, based on stochastic block coordinate descent and accelerated gradient descent, are proposed to reduce the computational complexity and speed up the convergence rate. Numerical simulation results show that the proposed algorithm offers better performance than the state-of-the-art approaches.

In this paper, we design constant modulus probing waveforms with low correlation sidelobes for colocated multi-input multi-output (MIMO) radar. Through exploiting the structure of the problem, we formulate it as a non-convex consensus minimization problem. Then a customized alternating direction method of multipliers (ADMM) algorithm is proposed to solve the problem, which is guaranteed convergent to its stationary point. Numerical examples show that the proposed approach offers better performance than the state-of-the-art approaches. Moreover, parallel implementation structure indicates that the proposed ADMM algorithm is suitable for applications involving large dimensionality.

Each user inside the cell of massive multiple- input multiple-output (MIMO) system suffers from severe pilot contamination (PC), which directly decreases the quality of service. To mitigate the PC, this paper proposes an improved soft pilot reuse scheme combined with time-shifted pilot arrangement, named by TS-SPR. First, we divide the users inside the cell into two parts: the center users and the edge users. For the center users, we divide all cells into three groups and implement the time- shifted pilot transmissions in uplink training stage. For the edge users, a filtering approach based on fast Fourier transform (FFT) operation is proposed to extract desired signals from interfering signals by the non-overlapping angle- of-arrivals (AOAs). Simulation results show that the proposed TS-SPR scheme can effectively improve the overall performance of the system without extra cost of pilot resources.

In this paper, a multi-cell joint optimization scheme based on pilot allocation is proposed in order to mitigate pilot contamination for multi- cell massive MIMO systems. This method measures interference of each pilot sequence caused by users multiplexing from adjacent cells exploiting the massive MIMO characteristics of fading channels. The scheme takes jointly optimizing a plurality of cells into account to ensure users in poor channel conditions suffering from less interference after the assignment, and improves the system performance with low computational complexity. Simulation results demonstrate the effectiveness of the proposed scheme.