Service caching in mobile edge computing (MEC) networks involves pre-storing computation programs on MEC servers to efficiently handle users’ computational tasks. By pre-loading these programs, service caching can significantly reduce both computation and transmission delays, addressing the diverse computational requirements of users. However, optimal caching placement is essential due to limited caching capacity, which directly impacts the efficiency of computation offloading. Proper cache placement ensures that relevant programs are readily available, thereby maximizing offloading performance and minimizing delays. This paper investigates a multi-tier MEC network consisting of caching-enabled edge servers, a cloud server, and multiple users. Users offload computational tasks to proximate edge servers, where locally cached programs facilitate immediate processing, thereby mitigating delays. When programs are absent from the cache, tasks are offloaded to the cloud, leading to additional latency. We formulate an optimization problem to minimize the overall communication and computation delay by jointly optimizing caching placement, transmission power, bandwidth allocation, and computation capacity. To tackle the complexity of this mixed-integer nonlinear programming (MINLP) problem, we propose two novel algorithms. The first is a Dinkelbach and big-M-based algorithm that reformulates the problem into a mixed-integer second-order cone programming (MI-SOCP) problem, approximating a near-optimal solution. Recognizing the computational demands of MI-SOCP, we also develop a low-complexity algorithm based on successive convex approximation (SCA) and alternating methods, which efficiently yields a high-quality sub-optimal solution. Simulation results confirm the effectiveness of the proposed algorithms in reducing network delays and emphasize the critical role of caching in improving network performance.

This paper investigates the joint optimal allocation of radio and computational resources aiming to minimize global average task offloading age (TOA) over all time slots and mobile devices (MDs) for long-term multi-cell MEC systems with continuous arrival of MDs. TOA represents the total number of offloading time slots, including both transmission and computation. The joint resource allocation problem cannot be solved online because its objective function is long-term average of TOA over all time slots. We transform the long-term resource allocation problem into an online one by the Lyapunov method, then an iterative algorithm is proposed to solve the online problem. The idea of this algorithm is computing iteratively the two sub-problems which optimize sub-channel allocation and offloading power and computational resources joint allocation based on an initial resource allocation scheme. The alternating direction method of multipliers (ADMM) method is employed to solve the first sub-problem. For the second sub-problem, a closed-form expression of optimal power is deduced by solving a convex optimization problem using the Lagrange multiplier method, then the sub-problem is simplified into a linear programming (LP) problem about computational resource allocation. The improved iterative greedy (IIG) algorithm is applied to solve the LP problem. Simulation results demonstrate that the proposed algorithm approaches the performance of the optimal branch-and-bound (BnB) algorithm in the MEC systems with one-time arrival of MDs, and outperforms two benchmark schemes such as first in first out (FIFO) and Chang’s algorithm.

This paper studies the joint on-ground precoding and on-board beamforming design problem in multi-gateway multibeam satellite communications with feeder link interference. Compared with pure on-ground systems, the hybrid on-ground on-board architecture provides a good trade-off between the total throughput and the bandwidth requirements of the feeder link. Moreover, we propose a two-timescale design strategy which significantly reduces the computational burden on the satellite payload. To be specific, the long-term on-board beamforming network (BFN) is designed based on the statistical channel state information (S-CSI), while the short-term on-ground precoding matrices are designed to cater to the instantaneous CSI (I-CSI) with optimized on-board BFN. We formulate an average sum rate maximization problem which incorporates the lossless constraint of the BFN, as well as the average power constraints at the satellite. To tackle this two-timescale non-convex stochastic optimization problem, we propose an algorithm called two-timescale joint on-ground and on-board beamforming (TJGBB). At each iteration, the TJGBB algorithm first solves the short-term on-ground precoding subproblem based on the weighted minimum mean square error (WMMSE) method. Then, it constructs convex surrogate functions for the objective and constraints of the long-term on-board beamforming primary problem, creating an approximate version. Finally, the long-term variables are updated by solving this convex approximation problem. The proposed algorithm is proven to almost surely converge to a stationary solution of the original problem within an acceptable error margin. Simulations show that our proposed method can significantly improve the overall system throughput compared to existing schemes.

This paper aims at designing a joint on-ground and on-board beamforming scheme for multi-gateway multibeam satellite systems. The proposed scheme exploits the degrees of freedom of on-board processing to mitigate feeder link interference and enhance satellite resource flexibility without requiring additional feeder link bandwidth, thereby improving the overall throughput. Based on the practical consideration of not escalating the complexity and real-time computational burden of the satellite payload, the two-timescale design is employed. Specifically, the long-term on-board beamforming network (BFN) is designed based on the statistical channel state information (S-CSI) of all links, while the short-term on-ground precoding is designed based on the instantaneous CSI (I-CSI) of the effective channel with optimized BFN. We formulate an average sum rate maximization problem that incorporates the lossless constraint of the BFN, as well as the average power constraints at the satellite. We propose an algorithm called two-timescale joint on-ground and on-board beamforming (TJGBB) to tackle this mixed-timescale non-convex stochastic optimization problem. Simulation results demonstrate the effectiveness of the proposed algorithm and its advantages over the benchmark schemes.

We consider scalable cell-free massive multiple-input multiple-output networks under an open radio access network paradigm comprising user equipments (UEs), radio units (RUs), and decentralized processing units (DUs). UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors (implementing the physical layer for each user) are hosted by the DUs as software-defined virtual network functions. Unlike the current literature, mainly focused on the characterization of the user rates under unrestricted fronthaul communication and computation, in this work we explicitly take into account the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. In particular, we systematically address the new problem of joint fronthaul load balancing and allocation of the computation resource. As a consequence of our new optimization framework, we present representative numerical results highlighting the existence of an optimal number of quantization bits in the analog-to-digital conversion at the RUs.