As the Internet of Things(IoT) and its intelligent applications continue to proliferate, forthcoming 6G networks will confront the dual challenge of heightened communication and computing capacity demands. To address this, D2D collaborative computing is being explored. However, the current D2D collaborative computing ignores the integrity of computing and communication. For a single-task device, offloading operations intertwine computing and communication, internal coupling causes due to parallel executed between local and D2D offloading. In addition, external coupling arises among devices competing for limited radio and computing resources. Worse, internal coupling and external coupling interact, exacerbating the situation. To address these challenges, a novel D2D offloading framework is proposed based on hyper-graph matching in this paper. Our goal is to minimize both delay and energy costs while ensuring service quality for all users by jointly optimizing task scheduling, offload policies and resource allocation. The original problem is formulated as a nonlinear integer programming problem. Then, by three-stage strategy optimization decomposition, it is separated into several sub-problems. In the first stage, a polynomial-time algorithm has been developed to optimize the task offloading ratio, taking into account both its upper and lower bounds. In the second stage, a geometric programming algorithm has been created to address power allocation. In the third stage, a three-dimensional hyper-graph matching model is employed to derive the optimal offloading and channel allocation policies. This is based on analyzing the conflict graph and applying the claw theorem. Simulation results demonstrate that the proposed scheme outperforms other algorithms by approximately 12%, 20%, 28%, 40%, respectively. Moreover, it enhances both spectral efficiency and computational efficiency.

In recent years, considerable studies have been devoted to physical‐layer network coding (PLNC) in a two‐way relay channel (TWRC) network with the symmetric channel. However, it may not be appropriate for the asymmetric channel in which the source‐to‐relay channels are not equally strong and the two sources may utilise different modulation. In this study, the authors studied the heterogeneous PLNC design in the asymmetric TWRC network with high‐order modulation and proposed a relay denoising mapping approach to reduce the decoding error probability. Firstly, they put forward a cluster maximisation algorithm to enlarge one cluster by sucking the neighbour points, which satisfy the exclusive law. Then, based on this algorithm, they designed the heuristic nearest‐neighbour denoising mapping (HNDM) approach. The HNDM algorithm divides the nearest neighbour superimposed constellation points into the same cluster as far as possible. Points in the same cluster are coded into a new symbol by a specific mapping that achieves a many‐to‐one mapping. Performance results are provided in terms of symbol error rate. Under diversity channel conditions, the performances of 16‐quadrature amplitude modulator (16‐QAM)‐8‐phase‐shift keying and 16‐QAM‐quadrature phase‐shift keying are evaluated.

Hybrid precoding, combining the digital and analogue precoding, is a key enabler to reach a compromise between system performance and hardware complexity in millimetre wave (mmWave) massive multiple‐input multiple‐output (MIMO) systems. Most previous studies on hybrid precoding considered narrowband channels. However, mmWave systems are expected to operate on wideband channels with frequency selectivity. In this study, the authors focus on the hybrid precoding design in mmWave massive MIMO systems using orthogonal frequency division multiplexing (OFDM). By transforming the hybrid precoding problem into a series of sub‐problems, they propose a low‐complexity algorithm to design the digital precoder and analogue precoder alternately. In particular, a phase pursuit method is introduced to seek this solution of analogue precoding matrix with the constant amplitude constraint. Simulation results and complexity evaluations reveal that the proposed algorithm can obtain better performance and lower complexity than some existing solutions in mmWave OFDM systems.

Millimetre wave (mmWave) multiple‐input multiple‐output (MIMO) systems have been proposed to enable Gbps communication for next‐generation cellular systems and local area networks. To compensate for the severe propagation loss of the mmWave channel, a cost‐effective hybrid precoding architecture, combining a digital precoder and an analogue precoder, is widely used in mmWave MIMO systems. In this study, two hybrid precoding algorithms based on particle swarm optimisation (PSO) algorithm will be proposed for two different hybrid precoding structures, i.e. the fully connected and partially connected structures. First, the authors use the phase of the analogue precoding variable to replace the corresponding variable with the constraints of the unit norm to solve the non‐convex constraints skillfully. Then they design analogue precoding by using PSO algorithm and compute the digital precoding based on the least squares solution. Finally, the proposed algorithms are compared with the existing advanced algorithms. Simulation results demonstrate that the proposed algorithms can approach the optimal performance under the corresponding structure.