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 the 5G system, different units probably do cooperation to satisfy the communication requirements. However, when network configuration is limited, these units may be difficult to adopt the globally cooperative style. In this case, local cooperation is considered. This study focuses on local cooperation, and mainly aims at interference mitigation between different units and receiving performance improvement for each unit. Generally, a two‐step scheme is proposed. First, channel decomposition is acted on interference mitigation through the design of first‐step transmitters and receivers. Second, channel diagonalisation with symbol processing and selection principle is executed for receiving performance improvement, and the second‐step transmitters and receivers are generated. The authors' contributions are: (i) they present the general scenario of local cooperation, and propose the corresponding strategies of interference mitigation and receiving performance improvement; (ii) they do not apply any iterative algorithm, and can adjust the quantity of information interaction between multiple units; (iii) they can make the symbol processing rule, modulation mode and signal‐to‐noise ratio metric flexible for each data stream. Numerical results illustrate the proposed scheme efficiently reduces the bit error rate, which means the proposed scheme can achieve the satisfactory effect in the locally cooperative network.

To meet the immensely diverse service requirements, heterogeneous cloud radio access network (H-CRAN) architecture and D2D communication is embraced. Consequently, the resource allocation between D2D pairs and current users is a challenge. In this paper, a joint power control and sub-channel allocation scheme is proposed. The original mixed-integer nonlinear programming problem is decomposed into power and sub-channel allocation. Geometric Vertex Search approach and 3-dimensional (3-D) matching method are used to solve them. Finally, numerical results verify the proposed scheme has about 35% and 60% improvement in total throughput comparing with other approaches.

Energy efficiency is essential for massive machine-type communication (mMTC), because of the limited energy in internet of things (IoT) devices. We consider a two-hop amplify-and-forward (AF) relay communication, and allow IoT devices with inferior channel conditions to connect relays by using device-to-device (D2D) technology. This paper proposes to jointly optimize relay selection, channel allocation and power control, so that the total energy efficiency is maximized while guaranteeing the signal to interference plus noise ratio (SINR) requirements of relays and BSs. The formulated joint optimization problem involves a nonlinear fractional programming (NFP) problem and a user-relay-channel matching problem which is NP-hard. Therefore, we propose a two-stage approach composed of the Dinkelbach method and a hypergraph-based 3D matching (HGM). Simulation results show that the total energy efficiency under the HGM is 8.41% and 59.85% higher than the iterative Hungarian method (IHM) and the minimum zero surface prioritized allocation (MZPA), respectively.

This paper proposes a novel Gain-Aware Uplink-Downlink(GAUD) jointly resource allocation scheme to maximize the Device-to-Device(D2D) throughput while guaranteeing Quality of Service (QoS) of cellular users. We formulate the global optimization problem as a mixed integer nonlinear programming problem and decompose it into three sub-problems. Firstly, a method of jointly uplink and downlink reuse mode selection is proposed. Based on the throughout gain, each D2D pair is appropriately assigned by either downlink or uplink frequency resource to reuse. Then a heuristic scheduling is designed for fairness channel allocation in order to form D2D users as much as possible. At last, the Lagrangian dual algorithm is developed to solve the optimal power allocation. The simulation results show that our proposed jointly downlink-uplink resource reusing scheme can make the system throughput increased by about 35% and 50% higher than the scheme based on Only Downlink and Only Uplink resource reusing.