Given the unpredictable and bursty nature of mission-critical ultra-reliable low-latency communication (URLLC) traffic, collaborative resource allocation across multiple base stations (BSs) enables joint resource pooling and interference management to mitigate instantaneous traffic spikes. Nevertheless, coordinating multi-cell spectrum sharing to achieve sustainable URLLC systems in the long term remains a persistent challenge, largely owing to the prohibitive computational cost of centralized designs and the potentially inefficient interference suppression in overlapping coverage regions. In this paper, we propose a radiation-aware distributed transmission scheme that collaboratively guarantees sustainable URLLC service while mitigating mutual interference. Specifically, a radiation footprint control mechanism is developed to adaptively regulate the radiated power intensity of transmitted wireless signals over the propagation space, preserving the service provisioning efficiency of the distributed network. Based on this mechanism, we formulate a green dynamic resource allocation problem to minimize long-term power consumption while ensuring sustained network stability. To address the nonlinear stochastic optimization challenge, Lyapunov optimization is first employed to transform the long-term problem into sequential short-term online ones. For the deterministic problem in each time slot, the BS selection is first abstracted as a collaborative matching game, after which a distributed scheme with upper/lower-bound approximation algorithms is proposed to solve the coupled beamforming and subcarrier assignment. Simulation results verify the effectiveness of the proposed algorithm in balancing power consumption against queue backlog.

This paper studies an elasticity-enhanced uplink architecture that synergistically integrates the coordinated full-spectrum reuse in multi-cell cellular networks with multicarrier rate-splitting multiple access (RSMA). The uplink multi-layer RSMA granularly partitions each user’s data stream by strategically distributing splitted submessages across subcarriers and employing an elaborate decoding order, thereby fully exploiting the available spatial-spectral degrees of freedom. Particularly, the sum rate maximization for the multi-cell system is formulated through joint optimization of the user association, uplink power allocation, submessage-specific subcarrier assignment, receive beamforming, and decoding order. To tackle the problem’s non-convexity and mitigate the centralized computational burden, a two-stage approach is developed. First, a low-complexity base station (BS) selection method, grounded in matching games, is proposed to partition the user set. Next, a collaborative distributed scheme is proposed to delegate computational process to the corresponding BSs, where each BS independently addresses the remaining local problems using an alternating optimization algorithm. Specifically, the majorization-minimization (MM) and dual decomposition techniques are employed to derive the suboptimal solutions for the power and subcarrier allocation, while the fractional programming and alternating direction method of multipliers (ADMM) are utilized to achieve closed-form updating of the receive beamforming. Moreover, a dynamically optimized decoding order strategy is analytically derived. Simulation results validate the efficacy of the proposed algorithm in sum rate gain and computational complexity, showcasing that the RSMA-aided multi-cell collaborative transmission can attain superior performance than existing schemes.

Ultra-reliable low-latency communication (URLLC) is a critical type of service that imposes stringent latency requirements. Considering the random and burst URLLC packets arrival characteristics, incorporating spatial frequency reuse into multi-cell networks can significantly improve the system performance. Nevertheless, how to design the frequency refuse strategy for such a multi-cell URLLC system remains technically challenging. In this article, we investigate an online dynamic resource scheduling problem in a multi-cell downlink system with URLLC services. The long-term time-averaged effective throughput is maximized while guaranteeing the instantaneous transmission reliability and prolonged network stability. The formulated problem is a mixed integer nonlinear stochastic optimization problem, in which the Lyapunov optimization is first leveraged to transform the long-term maximization problem into sequential short-term online ones. To tackle the deterministic problem in each time-slot, we further propose a distributed algorithm that delegates computational processes to corresponding base stations for collaborative execution. In this framework, the user association is abstracted as a cooperative game model. Subsequently, each base station exploits alternating optimization and convex optimization approximation algorithms to address the remaining resource allocation problem. Simulation results validate the effectiveness of the proposed algorithm in throughput-backlog trade-off, showcasing that the multi-cell collaborative transmission can attain better performance compared with existing schemes.

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.

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 application of Non-Orthogonal Multiple Access (NOMA) technology to New Radio (NR) V2X can further reduce communication delay and improve the system capacity of the vehicular network. However, in NR V2X Mode 1, the base station first needs to allocate resources in each transmission period, and then the vehicles transmit information by the allocated resource. According to this, we propose a two-stage scheme of centralized resource allocation and distributed power control to meet the requirement of the NR V2X Mode 1 while NOMA technology is employed in vehicle groups. Firstly, at the beginning of a transmission period, the base station allocates resources to vehicle groups. For this centralized manner, we propose a graph-based matching approach to allocate resources to improve system capacity. Then, each vehicle group controls and adjusts transmission power for NOMA communication in the period. For this distributed manner, we put forward a cooperative game approach to control the power of the vehicle group while increase system capacity. Simulation results show that the proposed scheme can increase the system capacity.

Placement of edge computing servers at the edge of the network can reduce task transmission delay. Connecting them into a system can provide services for a wider range. However, due to the mobility of the crowd and mobile devices, the number of tasks offloaded to each edge server may be quite different, which will seriously affect the QoS of the system. To this end, we investigate the QoS improvement of the distributed edge computing system from the game-theoretic perspective and propose a multi-agent state-based learning algorithm. Firstly, by modeling the cost of an edge computing server as the deviation between its execution time and the system average execution time, we formulate the QoS improvement of the system as a state-based game where each agent competes to maximize its own utility. Then, we propose a multi-agent state-based learning algorithm to obtain the pure Nash equilibrium strategy of each agent. Finally, compared with the existing approaches, the experiments show that the proposed algorithm can improve the QoS of the distributed edge computing system.

Through offloading task to mobile ad hoc cloud (MAHC), mobile devices can execute computation-intensive tasks faster and consume less energy without infrastructures. However, when multiple resource demanders (RDs) offload tasks to resource providers (RPs) in the MAHC, the disorderly competition among RDs can cause inhomogeneous task distributions on the RPs. This can reduce the service efficiency of the MAHC and lead to a low utility of each RD. Accordingly, we propose a stochastic game approach to solve this problem. Firstly, the utility of each RD is examined as the combination of task execution time and monetary cost. Then, we model the competition for each RD pursuing its maximum utility as a static noncooperative game. After that, we transform the single-shot game in one time slot into a stochastic game in an infinite time horizon to obtain the optimal strategy of each RD. Finally, we propose the backward iteration algorithm to reduce the computational complexity for reaching the ε-Nash equilibrium of the game. Numerical results show various performances of this stochastic game. Compared with the strategy obtained from the static game, the equilibrium strategy derived from the stochastic game can effectively improve the utility of each RD.