Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge servers’ (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESs’ processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of users’ completion times.

A cellular network is usually modeled and analyzed as a Poisson point process (PPP). However, small cells are ordinarily clustered around hotspots in urban areas, which cannot be accurately modeled with a PPP. This letter presents an analysis of the downlink coverage of small cells that are modeled with a Matern cluster process (MCP). A new accurate formula for the probability generating function of an MCP is derived. We also propose a new distribution function for the desired distance (i.e., between the user and serving small cell) based on the spherical contact distribution function of the Boolean model. The above two formulas can be used to obtain a closed-form expression for the downlink average coverage probability of small cells. Simulation results confirm the accuracy of our derived formulas. Our derived MCP-based coverage is accurate and can be used to guide the small cell configuration of a cellular network.

Owing to the increasing popularity of online social media, Web 3.0, and Metaverse, mobile users can access intelligent and self-sustained services more conveniently than before. However, the enormous computation burden and data flow associated with these services pose tremendous challenges for future 6G networks that strive to enable diversified applications with varying user requirements. To address these grave challenges, wireless edge caching and computing are widely acknowledged as the two most successful enablers. Meanwhile, the trend of composing large services/applications as a suite of small and independent microservices (MSs) is leading to the cases where functional units can be distributed over edges, accelerating the transition of telecom architecture towards distributed MS-based paradigm. This article conceives an overarching perspective towards such distributed MS-aware cellular networks (DMCNs), which embrace the conventional service caching (and computing) system as a special case. The studies are expanded based on two aspects. One is the fundamental analysis of network architecture and the other is the leading design principles, including the user’s behavior modeling, MS placement, MS migration, and the joint consideration. These key issues are compelling problems in practices, yet have been largely ignored by existing works. Several potential solutions to the aforementioned issues are presented, and by addressing the challenges and exploring the potential enablers, we can pave the way for the successful implementation and adoption of DMCNs in the future. The article also includes a pair of case studies to validate the effectiveness of the developed framework. Finally, research opportunities and future trends are discussed.

We exploit a behavior-shaping proactive mechanism, namely, recommendation, in cache-assisted non-orthogonal multiple access (NOMA) networks, aiming at minimizing the average system’s latency. Thereof, the considered latency consists of two parts, i.e., the backhaul link transmission delay and the content delivery latency. Towards this end, we first examine the expression of system latency, demonstrating how it is critically determined by content cache placement, personalized recommendation, and delivery associated NOMA user pairing and power control strategies. Thereafter, we formulate the minimization problem mathematically taking into account the cache capacity budget, the recommendation-oriented requirements, and the total transmit power constraint, which is a non-convex, multi-timescale, and mixed-integer programming problem. To facilitate the process, we put forth an entirely new paradigm named divide-and-rule . Specifically, we first solve the short-term optimization problem regarding user pairing as well as power allocation and the long-term decision-making problem with respect to recommendation and caching, respectively. On this basis, an iterative algorithm is developed to optimize all the optimization variables alternately. Particularly, for solving the short-timescale problem, graph theory enabled NOMA user grouping and efficient inter-group power control manners are invoked. Meanwhile, a dynamic programming approach and a complexity-controllable swap-then-compare method with convergence insurance are designed to derive the caching and recommendation policies, respectively. From Monte-Carlo simulation, we show the superiority of the proposed joint optimization method in terms of both system latency and cache hit ratio when compared to extensive benchmark strategies.

Integrated sensing and communication (ISAC) has been identified as a potential key 6G technology that empowers cellular systems with wireless sensing capabilities. It is challenging for an individual ISAC node to meet the increased sensing demands of 6G Internet of Everything (IoE) applications, such as larger coverage and higher accuracy. To address this issue, multi-node cooperative ISAC (MNC-ISAC) schemes have recently been explored to improve sensing performances for 6G IoE applications. This paper innovatively proposes a distributed multinode cooperative ISAC (DMNC-ISAC) scheme, which differs from the conventional centralized multi-node cooperative ISAC (CMNC-ISAC) scheme where all cooperative ISAC nodes form a single sensing coalition. In the proposed DMNC-ISAC scheme, multiple ISAC nodes form a satisfied number of cooperative groups by coordinating their sensing abilities and resources in a distributed way. The interactions between multiple ISAC nodes, characterized by collaboration in sensing and competition for resources, are modeled as a local cooperation-based game. Then, to maximize the overall sensing and communication (S&C) performance of the proposed DMNC-ISAC scheme, the cooperation grouping and transmission beamforming of multiple ISAC nodes are jointly optimized. The stochastic learning automata (SLA) and particle swarm optimization algorithms are utilized to obtain the equilibrium grouping results and design the beamforming of multiple ISAC nodes. Finally, simulation results show that the distributed cooperative ISAC scheme enhances sensing performance by at least 5%, leading to improved systemic performance in most ISAC scenarios. This improvement is particularly notable in challenging sensing environments and when the possible status of the target is ambiguous.