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.

Mobile edge computing (MEC) in the next generation networks will provide computation services at the network edge to enrich the capabilities of mobile devices and lengthen their battery lives. However, the performance of MEC cannot be guaranteed, when large size local tasks are uploaded to the server simultaneously causing network congestion. As a new paradigm that focuses on transmitting the meaning of messages, semantic communications reveals the significant potential to reduce the network traffic. In this paper, we propose a semantic-aware joint communication and computation resource allocation framework for MEC systems. In the considered system, random tasks arrive at each terminal device (TD), which needs to be computed locally or offloaded to the MEC server. To further release the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the original large-size raw data. An optimization problem of joint semantic-aware division factor, communication and computation resource management is formulated. The problem aims to minimize the energy consumption of the whole system, while satisfying long-term delay and processing rate constraints. To solve this problem, an online low-complexity algorithm is proposed. In particular, Lyapunov optimization is utilized to decompose the original coupled long-term problem into a series of decoupled deterministic problems without requiring the realizations of future task arrivals and channel gains. Then, the block coordinate descent method and successive convex approximation algorithm are adopted to solve the current time slot deterministic problem by observing the current system states. Moreover, the closed-form optimal solution of each optimization variable is provided. Simulation results show that the proposed algorithm yields up to 41.8% energy reduction compared to its counterpart without semantic-aware allocation.

With the dramatic increase in the diverse service requirements and mobile devices, the application-specific data tends to span multiple consecutive timeslots to complete the transmission, while the demand for spectrum resources is further exacerbated. Recent works have suggested that integrating spatial frequency reuse with multi-cell networks can enhance the spectral efficiency and alleviate the scarcity of spectrum. Hence this paper considers a downlink multi-cell multi-timeslot orthogonal frequency division multiple access (OFDMA) cellular system where the users keep downloading data from the base stations (BS) until reaching a predetermined cache size. Specifically, we aim to minimize the transmission delay by jointly optimizing the BS selection, subcarrier assignment, and transmit power allocation, taking into account the current cache size. Due to inter-cell interference and multi-timeslot coupling, this problem is challenging to solve directly. We prove that this problem can be transformed into sequential online sum rate maximization subproblems under causal channel state information (CSI). To solve the subproblems, we first develop a centralized dynamic resource allocation algorithm based on the parameter transformation and the majorization-minimization (MM). In view of the trade-off between performance and complexity, we further propose a distributed algorithm by a designed BS selection scheme and the MM approach. Simulation results demonstrate that the distributed algorithm achieves comparable performance to the centralized algorithm, while they both outperform the benchmark schemes in terms of transmission delay.

Channel estimation is one of the fundamental topics in practical multi-antenna systems. With the progress of artificial intelligence, deep learning (DL)-based schemes have presented enormous the potential for performance and efficiency. In this paper, we propose an attention-aided approach to achieve channel estimation for multi-user single input multiple output (SIMO) system. Specifically, the multi-attention graph learning network (MAGLN) is conducted to estimate the uplink channel, which concentrates on the partial more important information in the different dimensions. The channel attention and graph attention mechanisms are adopted to enhance the quality of extracted features and finally output the estimated channel information. Numerical results show that the proposed scheme has better estimation performance compared with the traditional algorithms and other candidate DL-based architectures.

In Vehicle-to-Everything (V2X), cellular Device-to-Device (D2D) communication can improve spectrum efficiency, and non-orthogonal multiple access (NOMA) can further strengthen system capacity. However, the execution for NOMA may be affected by the additional interference introduced by cellular links in V2X. In this paper, we study the energy-efficient optimization problem in cellular D2D-aided V2X networks with NOMA. To efficiently meet the quality of service (QoS) requirements while accounting for the maximum performance from the user’s perspective, we attempt to maximize the minimum energy efficiency (EE) of each matching link by performing power allocation and spectrum sharing. Since the formulated problem belongs to a non-convex mixed integer non-linear programming (MINLP), a novel resource allocation scheme is proposed to solve this complex coupling problem. Finally, simulation results verify the proposed algorithm’s effectiveness as compared to different benchmark schemes.

The multi-task federated learning (FL) problem in the wireless communication system is investigated in this paper. The base station (BS) and wireless users cooperatively perform a two-task FL algorithm in the established model. Users use their local datasets to train two local models of two different tasks. The trained local model of only one task is transmitted to the BS at each time and the BS aggregates the obtained models to calculate a global model, which will be sent back to all users. Since the resources for wireless transmission, such as transmit power and number of subcarriers are limited, the BS have to allocate resources reasonably to minimize the time consumption of the FL procedure while meeting the required learning performance. On the other hand, users are dynamically arranged to participate in different tasks in each iteration. This resource allocation and users arrangement problem is formulated as an optimization problem which aims to minimize time consumption of the two-task FL procedure. To address this nonconvex problem, we first decompose it into two convex sub-problems. Then we propose an iterative algorithm to solve this problem via iteratively obtaining the optimal solution of the joint power control and communication round optimization subproblem, and user arrangement subproblem. Simulation results of this multi-task FL system show that the proposed algorithm can reduce 7.02% and 9.67% completion time compared to the uniform and random user selection schemes respectively.

This paper investigates the deployment of federated learning (FL) over an over-the-air computation (AirComp) and intelligent reflecting surface (IRS) based wireless network. In the considered system, devices transmit locally trained machine learning (ML) models to the base station (BS) which aggregates the received ML models and generates a shared global ML model. The devices can directly transmit ML models to the BS or using IRS. Meanwhile, AirComp is used to aggregate ML models that are transmitted from the devices to the BS. To minimize the energy consumption of devices, an energy minimization problem is formulated, which jointly optimizes the device selection, phase shift matrix, decoding vector, and power control. To seek the solution, the original optimization problem is divided into four sub-problems. Then the fractional program, greedy algorithm, matrix derivation, and weighted minimum mean square error methods are used to compute the phase shift matrix, device selection vector, decoding vector, and transmit power, respectively. Simulation results show that the proposed algorithm can reduce 11.2% energy consumption of devices compared to an FL algorithm that is implemented at a network without any IRSs.

In this paper, the problem of joint subcarrier and power allocation is studied for multi-cell orthogonal frequency-division multiplexing (OFDM) systems. This joint subcarrier and power resource allocation problem is formulated as an optimization problem whose goal is to maximize the system spectral efficiency. To solve the proposed problem, the original optimization problem is first decomposed into two subproblems: subcarrier allocation and power allocation. By solving these two subproblems, an initial subcarrier and power allocation scheme is accordingly obtained. An multi-agent reinforcement learning (MARL) algorithm is proposed to further increase the spectral efficiency. In particular, using the proposed MARL algorithm, each BS can adapt its allocation scheme according to the wireless environmental states. Numerical results show that the proposed MARL method can achieve up to 53.6% gain in terms of spectral efficiency compared to the conventional scheme. The proposed MARL scheme also converges more rapidly than the conventional single-agent Q-learning approach.