Zhirui Hu

To address the dual demands of high-efficiency communication for multiple devices and high-precision positioning for multiple passive radio frequency identification (RFID) tags in the Internet of Things (IoT), this paper investigates an integrated sensing and backscatter communication (ISABC) system based on a uniform planar array (UPA). Focusing on resource-constrained IoT gateways, we aim to minimize the Cramér-Rao lower bound (CRLB) for high-precision two-dimensional (2D) direction-of-arrival (DoA) estimation of passive tags via precoding design, subject to the gateway power budget and communication quality-of-service (QoS) constraints. To balance high-precision positioning with real-time processing in dynamic IoT environments, we propose a configurable framework comprising three schemes: 1) joint sensing and communication precoding (JSCP) as a performance benchmark; 2) layered-precoding communication-optimized precoding (LP-COP) to effectively balance sensing accuracy and algorithmic latency; and 3) a low-complexity fixed-beam with dynamic power allocation precoding (FB-PDPA). Simulation results demonstrate that the proposed LP-COP scheme achieves near-optimal sensing accuracy comparable to the JSCP benchmark while reducing computation time by approximately 96% and maintaining strong robustness under strict signal-to-interference-plus-noise ratio (SINR) constraints. The proposed framework provides a flexibly configurable technical path for IoT ISABC deployments.

In non-orthogonal multiple access (NOMA) systems, the overall bit error rate (BER) is highly influenced by power allocation. This letter investigates the relationship between the optimal power allocation factor and user path losses in a downlink Rician fading channel, with the objective of minimizing the overall BER. To achieve this, the exact BER expressions for a downlink NOMA system under imperfect successive interference cancellation (SIC) are derived. An analytical relationship between the power allocation factor and path loss is then established by applying an exponential $Q$ -function approximation, performing analytical averaging over Rician fading, and employing a piecewise Taylor expansion. Numerical and Monte Carlo simulations validate the obtained relationship.

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 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.

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.

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.

As one of the most promising techniques in wireless communication systems, device-to-device (D2D) has drawn much attention due to its superior performance. Meanwhile, the interference between cellular users and D2D users is still a challenging problem and needs to be mitigated effectively. A large number of simulation experiments for D2D communications have been studied to reduce the impact of the interference in the existing literature. However, theoretical research is still lacking. Thus, in this paper, we use stochastic geometry to evaluate the uplink performance of users by considering the impact of the previous moment on the next moment, which captures the effect of temporal and the spatial correlation of the interference in D2D communication underlaying cellular networks. Using a Poisson point process to model locations of D2D users, we derive an analytic expression for conditional probability and unconditional probability of link success, and prove that the probability of link success is temporally correlated. Moreover, we provide a theoretical framework for interference mitigation in D2D underlaying cellular networks.

KSII Transactions on Internet and Information Systems TIIS Paper Details

This paper focuses on the fairness‐aware nonlinear joint transceiver design for the energy‐harvesting (EH)‐powered coordinated multi‐point (CoMP) systems. In the EH‐powered CoMP systems, each node harvests the energy independently first. Then, these nodes collaborate for joint signal transmission. Since there is no conventional grid to connect the nodes, the energy among nodes cannot be coordinated. Therefore, per‐node power constraints should be satisfied, which however has not been considered in existing schemes. In this paper, a fairness‐aware nonlinear transceiver scheme is developed under the per‐node power constraints. The nonlinear transceiver design is formulated as an optimization problem to maximize the minimum signal‐to‐interference noise ratio of data streams. A two‐step optimization algorithm is proposed to obtain the closed‐form solutions in the cases with perfect channel state information (CSI) and imperfect CSI, respectively. Simulation results verify the fairness of the proposed algorithm, and demonstrate its performance enhancement in sum‐rate and bit ratio error (BER). For sum‐rate, the proposed algorithm can achieve 12% gain over WS‐MSE and 40% gain over BD at SNR = 20 dB. For BER, it can achieve an approximately 4 dB gain over WS‐MSE and 8 dB gain over BD at BER = 10 ⁻³ .