Edge computing is expected to enable the cloud computing services in proximity and leverage the computational capacities at the edge of a network. Driven by massive data-hungry and energy-hungry applications, future wireless networks will need to provide various cloud-based multimedia services in a "greener" manner. However, limited computing capacity, wireless resources, and power supply prevent edge computing from evolving and maturing in terms of high sustainability and energy efficiency. In this article, we attempt to further improve the energy-efficient communication in mobile edge computing by proposing a device-to-device (D2D) offloading architecture. The D2D technique, in line with mobile edge computing, is responsible for offloading wireless traffic as well as computational tasks. Cooperative relay-aided transmission is also used in this architecture to pursue further improvements in a multi-cell scenario. Finally, offloading and balancing technologies are introduced to increase the total energy efficiency, decrease the inter/ intra-cell interference, deal with traffic/computational congestion, and provide better services to end users.

In this article, we proposed a symbiotic localization and the ambient backscatter communication (SLABC) architecture, which uses existing ambient backscatter communication (AmBC) hardware and received signals to help Internet of Things (IoT) localize target objects. The SLABC can be viewed as a specific realization of integrated passive/symbiotic sensing and communication, which has two mutually beneficial stages: 1) sensing stage with generalized Prony with homologous matching (GPHM) method and 2) communication stage with multisource constant modulus algorithm (MSCMA) method. To formulate SLABC, we propose a unified TMSC signal model to characterize the complex multisource, multipath, and multireflection symbiotic communication (TMSC), and facilitate the key information extraction. Utilizing the frequency domain expression of the unified signal model and the similarity between multipath channel coefficients and tap coefficients of equalizers, our proposed GPHM and MSCMA methods are efficient to mitigate the TMSC interference and retrieve the interested information to locate targets and communicate. The proposed SLABC concept updates the symbiotic AmBC IoT with the least cost and reduces the dependence on hardware stacking. The simulation results show that: 1) information extracted by GPHM and MSCMA can help localization achieve high accuracy and reduce the complexity of multipath interference mitigation and 2) both of the two methods have a positive influence on each other.

The future integrated sensing and communication (ISAC) networks is expected to equip with sufficient computation resources. However, current research focuses on single-domain resource allocation in ISAC and computing force networks, leaving the joint optimization of sensing, communication, and computation resource allocation unexplored. In this paper, we propose a novel approach to this problem by deep incorporating computation resources, combined with a federated learning framework, while considering sensing precision and power consumption. Firstly, a multi-objective optimization is designed, involving Cramer-Rao Bound, sum rate of ISAC networks, and power consumption of computing force networks. Subsequently, the multi-objective optimization is transformed into a multi-task learning model. We aim to obtain joint optimization of sensing, communication, and computation resource allocation via deep learning techniques. Towards the multi-task learning model, the multiple-gradient descent algorithm is utilized to obtain the multi-objective optimization. Furthermore, a practical low-complexity the multiple-gradient descent algorithm is developed to reduce the computational cost. Finally, the effectiveness of the proposed deep learning algorithms is verified by simulations results.

Owing to environmental, financial and quality of service (QoS) considerations, energy efficient wireless communications have been paid increasing attention under the background of limited energy resource. This study considers a spectrum sharing cognitive radio network, where the primary system leases its radio spectrum to the secondary system for a fraction of time in exchange for secondary users served as relays to assist the transmission of the primary traffic. Based on the cooperative spectrum leasing protocol, the authors formulate a joint optimisation of relay selection and power allocation under QoS requirements to improve energy efficiency (EE). By employing a greedy spectrum sharing (GSS) algorithm, the optimal relay selection, power and sharing time allocation are readily obtained. Monte–Carlo simulations are performed to demonstrate that significant EE improvement is achieved by the proposed GSS algorithm, and that the network performance is enhanced.

One of the main challenges of popularizing the integration of sensing and communication (ISAC) network is mutual interference between the two functions. A viable solution is the time division scheme where communication and sensing are separated in time domain. This paper considers a multi-cluster ISAC network model, where the time-domain radio resources are allocated to sensing and communication. At the same time, the time resources can be reused in space-domain. Particularly, the terminals in different work phases can access radio resources of different or the same clusters simultaneously, depending on interference. In this way, the interference is isolated while the resources utilization is improved. Two different resource allocation approaches are proposed according to how interference is considered. The aim is to maximize the sensing detection probability under the constraint of network throughput. The performance improvement in terms of target detection probability brought by the proposed schemes is shown by numerical results compared with benchmark methods.

The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performance. Therefore, the precise UE location sensing and resource allocation issues are investigated in a spectrum sharing sixth generation network. An approach is proposed for joint subcarrier and power optimization based on UE location sensing, aiming to minimize system energy consumption. The joint allocation process is separated into two key phases of operation. In the radar location sensing phase, the multipath interference and Doppler effects are considered simultaneously, and the issues of UE’s location and channel state estimation are transformed into a convex optimization problem, which is then solved through gradient descent. In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed. To further minimize system energy consumption, a joint subcarrier and power allocation method is introduced, resolved via the Lagrange multiplier method for the non-convex resource allocation problem. Simulation analysis results indicate that the location sensing algorithm exhibits a prominent improvement in accuracy compared to benchmark algorithms. Simultaneously, the proposed resource allocation scheme also demonstrates a substantial enhancement in performance relative to baseline schemes.