A key challenge for enabling Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of M2M communications in LTE networks. Specifically, a novel double-queue model is established, which can both incorporate the queueing behavior of each Machine Type Device (MTD) and be scalable in the massive access scenarios. To evaluate the access efficiency, the network throughput is further characterized, and optimized by properly choosing the backoff parameters including the Access Class Barring (ACB) factor and the Uniform Backoff (UB) window size. The analysis reveals that the maximum network throughput is solely determined by the number of preambles, and can be achieved by either tuning the ACB factor or the UB window size based on statistical information such as the traffic input rate of each MTD. Simulation results corroborate that with the optimal tuning of backoff parameters, the network throughput can remain at the highest level regardless of how many MTDs in the network, and is robust against feedback errors of the traffic input rate and burstiness of data arrivals.

Industrial Cyber-Physical Systems (ICPS) have emerged as a critical component in the industrial domain. To facilitate seamless collaboration among massive devices, cloud-edge computing architectures have emerged as a key enabler for ICPS, leveraging distributed intelligence to orchestrate devices and computational tasks. In cloud-edge computing, efficient task offloading and resource management are essential for optimizing task performance and reducing energy costs. However, conventional centralized resource management strategies struggle to satisfy the real-time, adaptability, and performance demands of dynamic ICPS systems. Industrial Cyber-Physical Systems (ICPS) have emerged as a critical component in the industrial domain. To facilitate seamless collaboration among massive devices, cloud-edge computing architectures have emerged as a key enabler for ICPS, leveraging distributed intelligence to orchestrate devices and computational tasks. In cloud-edge computing, efficient task offloading and resource management are essential for optimizing task performance and reducing energy costs. However, conventional centralized resource management strategies struggle to satisfy the real-time, adaptability, and performance demands of dynamic ICPS systems. In this paper, we propose the Distributed Transformer-based Actor-Critic (DTAC) algorithm to jointly determine task offloading and resource management decisions in cloud-edge computing networks, particularly for delay-sensitive applications in ICPS. The DTAC algorithm integrates the powerful transformer model with the popular actor-critic architecture to address the challenge of a hybrid high-dimensional action space. We first train a centralized model to learn coordination among user equipments (UEs) and then introduce a decentralized transfer learning (TL) approach to efficiently adapt the centralized model into the DTAC framework. Using the DTAC model, each UE can independently manage its local resources based solely on local information, avoiding the significant signaling overhead inherent in centralized approaches. Simulation results demonstrate that DTAC not only outperforms other MARL and TL schemes in both small-and large-scale scenarios, but also exhibits strong generalization capabilities in inexperienced settings. Furthermore, DTAC and decentralized TL approaches significantly reduce training costs by 73% compared to other methods, making them more practical for ICPS deployment.

Multi-link operation is regarded as a crucial feature in the upcoming WiFi 7 networks, which allows a single multi-link device (MLD) to make concurrent data transmissions over multiple links. To facilitate synchronous multi-link channel access, IEEE 802.11 Task Group BE has proposed various channel access schemes, such as Longest Backoff (LB) access and Shortest Backoff (SB) access. However, the coexisting performance of WiFi 7 networks with multiple channel access schemes remains largely unexplored. In this paper, we develop an analytical model to evaluate the data rate and mean access delay performance of a multi-link WiFi 7 network with two types of devices adopting LB and SB, respectively, each employing different initial backoff window sizes. The ratio of device data rates between LB-MLDs and SB-MLDs is inversely correlated with the number of links, and the ratio of their initial backoff window sizes, indicating potential unfairness if the backoff parameters are not appropriately chosen. The optimal initial backoff window sizes to maximize the network sum rate and minimize the mean access delay under a given data rate ratio are further derived and verified by simulation results. The maximum network sum rate scales with the number of links, and is independent of the target fairness requirement or number of devices. Conversely, the minimum mean access delay for each type of devices, is strongly influenced by the target fairness requirement, and shows a linear increase with the network size.

This letter focuses on the optimization of access delay performance of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. Specifically, by deriving the probability generating function of access delay, both the first and second moments of access delay are obtained as functions of system parameters. The mean access delay is further minimized by optimally tuning the Access Class Barring (ACB) factor, and significant gains are demonstrated over the standard setting where the ACB factor is fixed.

This paper aims at optimizing the Peak Age of Information (PAoI) performance of slotted Aloha networks with two representative data queue service disciplines, i.e., first-come first-served (FCFS) and last-come first-served (LCFS). Specifically, by assuming a unit-size buffer and Bernoulli packet arrivals, the average PAoI are derived and minimized by properly tuning the packet arrival rate and the channel access probability of each sensor. Depending on whether the packet arrival rate can be tuned or not, the individual optimization and joint optimization are both considered. The optimal system parameters and corresponding minimum PAoI are explicitly characterized. The analysis shows that in the joint optimization case, the PAoI linearly increases with the network scale and the minimum PAoI with LCFS is 16.8% lower than that with FCFS in the massive access scenario. Yet, for achieving such performance gain over FCFS, each sensor with LCFS should generate a new sample in each time slot, indicating soaring energy consumption due to the sampling operation. The analysis shows that the energy efficiency with LCFS is always lower than that with FCFS, and the gap keeps increasing with the number of sensors, which reveals a clear age-energy tradeoff in terms of service disciplines.

In the IEEE 802.11ax Wireless Local Area Networks (WLANs), Orthogonal Frequency Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN amendment. However, with the growth of the number of devices, it is difficult for the Access Point (AP) to schedule uplink transmissions, which calls for an efficient access mechanism in the OFDMA uplink system. Based on Multi-Agent Proximal Policy Optimization (MAPPO), we propose a Mean-Field Multi-Agent Proximal Policy Optimization (MFMAPPO) algorithm to improve the throughput and guarantee the fairness. Motivated by the Mean-Field games (MFGs) theory, a novel global state and action design are proposed to ensure the convergence of MFMAPPO in the massive access scenario. The Multi-Critic Single-Policy (MCSP) architecture is deployed in the proposed MFMAPPO so that each agent can learn the optimal channel access strategy to improve the throughput while satisfying fairness requirement. Extensive simulation experiments are performed to show that the MFMAPPO algorithm 1) has low computational complexity that increases linearly with respect to the number of stations 2) achieves nearly optimal throughput and fairness performance in the massive access scenario, 3) can adapt to various diverse and dynamic traffic conditions without retraining, as well as the traffic condition different from training traffic.