Device-to-device (D2D) communication is a promising wireless communication technology which can effectively reduce the traffic load of the base station and improve the spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further improve the throughput, but the problem of interference suppression becomes more complex and challenging due to the introduction of new links. Therefore, how to perform effective and low-complexity optimal radio resource allocation is still a problem to be solved in IRS-assisted D2D communication systems. To this end, a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization is proposed in this paper. First, a multivariable joint optimization problem for the uplink cellular network with IRS-assisted D2D communication is established, where multiple DUEs are allowed to share a CUE’s sub-channel. However, the proposed problem considering the joint optimization of power and phase shift, with the objective of maximizing the system sum rate and the constraints of the minimum user signal-to-interference-plus-noise ratio (SINR), is a non-convex non-linear model and is hard to solve. Different from the existing work, instead of decomposing this optimization problem into two sub-problems and optimizing the two variables separately, we jointly optimize them based on Particle Swarm Optimization (PSO). Then, a fitness function with a penalty term is established, and a penalty value priority update scheme is designed for discrete phase shift optimization variables and continuous power optimization variables. Finally, the performance analysis and simulation results show that the proposed algorithm is close to the iterative algorithm in terms of sum rate, but lower in power consumption. In particular, when the number of D2D users is four, the power consumption is reduced by 20%. In addition, compared with PSO and distributed PSO, the sum rate of the proposed algorithm increases by about 10.2% and 38.3%, respectively, when the number of D2D users is four.

The intelligent reflective surface (IRS)-assisted communication is recognized as a promising technology to enhance capacity and coverage of the network by controlling propagation. However, in IRS-aided device-to-device (D2D) communication underlaying cellular networks, co-channel interference is more complicated and challenging than traditional D2D communication networks due to the addition of extra reflection paths. To alleviate interference and improve sum-rate, we establish a multivariable resource optimization function based on IRS-assisted D2D communication in this paper. Then, a hybrid sub-optimal algorithm is proposed, which includes channel allocation based on game theory, power allocation and phase shift optimization. Specifically, a sub-channel allocation algorithm is introduced to maximize system sum-rate, where the problem of sub-channel allocation is formulated as a two-stage combinatorial auction model, and the approximate optimal solution is obtained by bidding and adjustment. Then, a sub-optimal solution for transmission power and phase shifts is obtained through local search and iterative optimization. Simulation results show that the proposed hybrid sub-optimal algorithm is feasible and effective. Compared to the other 5 schemes, it has best sum-rate performance and lowest power consumption.

With the flourishing development of wireless communication, further challenges will be introduced by the future demands of emerging applications. However, in the face of more complex communication scenarios, favorable decoding results may not be yielded by conventional channel decoding schemes based on mathematical models. The remarkable contributions of deep neural networks (DNNs) in various fields have garnered widespread recognition, which has ignited our enthusiasm for their application in wireless communication systems. Therefore, a reliable DNN-based decoding scheme designed for wireless communication systems is proposed. This scheme comprises efficient local decoding using linear and nonlinear operations. To be specific, linear operations are carried out on the edges connecting neurons, while nonlinear operations are performed on each neuron. After forward propagation through the DNN, the loss value is estimated based on the output, and backward propagation is employed to update the weights and biases. This process is performed iteratively until a near-optimal message sequence is recovered. Various factors within the DNN are considered in the simulation and the potential impacts of each factor are analyzed. Simulation results indicate that our proposed DNN-based decoding scheme is superior to the conventional hard decision.

Object detection in remote sensing imagery is challenged by extreme variations in object scale and arbitrary orientations, which significantly degrade the accuracy of conventional detectors. At the same time, there is a growing demand for lightweight models to facilitate deployment in real-world scenarios. To address these issues, we propose DMSE-YOLO, a detection framework that jointly enhances multi-scale representation and orientation awareness while maintaining computational efficiency. The network integrates cross-scale feature fusion with adaptive channel reweighting to enhance multi-scale detection capability, and introduces direction-aware attention mechanisms to improve localization under orientation uncertainty. Furthermore, an efficient convolutional variant is incorporated to form a lightweight version, L-DMSE-YOLO, which preserves detection accuracy with reduced parameters and FLOPs. Experiments on the challenging DOTA-v1.0, DOTA-v1.5, and DIOR-R benchmarks demonstrate that DMSE-YOLO achieves mAP50 scores of 75.04, 69.9, and 77.6, respectively, while the lightweight L-DMSE-YOLO maintains comparable performance with reduced computational cost, making it ideal for resource-constrained applications.

This letter studies the transmit power allocation in downlink coordinated multi-cell systems with the battery-capacity-limited receivers, where the battery level of receivers is considered. The power allocation is formulated as an optimization problem to maximize the minimum signal-to-interference noise ratio of users under the per-base station power constraints and the feasible maximum received data rate constraints determined by the receiver battery level. The optimal solutions are derived by the proposed monotonic optimization technique-based algorithm. The proposed algorithm can extend the battery lifetime of the receivers with lower battery level. Simulation results illustrate the performance of the proposed algorithm.

This letter focuses on the pilot assignment problem in scalable cell-free systems and proposes a graph partitioning based scheme to improve the user fairness. Specifically, a novel metric to indicate the strength of potential pilot contamination between any two users is firstly defined, and a weighted graph is constructed by taking the novel defined metric as the edge weight to describe the potential interference among users. Then, we formulate the pilot assignment as a graph partitioning problem, and get the solution by the proposed degree sorting-based algorithm. Simulation results verify the effectiveness of the constructed metric to indicate the pilot contamination between users and the fairness advantage of the proposed algorithm.