Millimeter-wave (mmWave) imaging is increasingly being adopted across the supply chain and security industries to see through occlusions and detect hidden objects. Yet, most industrial mmWave imaging relies on a static, bulky, and expensive infrastructure to achieve precisely measured positions required for synthetic aperture imaging. In contrast, a handheld mmWave scanner can provide a compact, mobile and cost-effective alternative for high-resolution mmWave imaging, but it suffers from severe phase errors and image distortions due to motion inaccuracy.

mmWave radar based sensors are increasingly being deployed for robotics and automobile applications to sense the environment. Unfortunately, specular reflections of smooth and planar surfaces makes mmWave sensing unreliable at detecting objects and vehicles in the real world. In fact, due to the small wavelength of mmWave radars, most surfaces are smooth reflectors for the radar signals. This paper addresses this fundamental limitation of detecting specular reflectors by capturing the energy reflected away from the radar.

Accurately identifying materials in real-world environments is critical for applications in robotics, security screening and autonomous systems. While more expensive approaches for material characterization such as spectroscopy or ellipsometry are ill-suited for large scale deployment, RF-based solutions present a new potential low-cost alternative. Prior RF-based material sensing approaches are either too invasive, suffer from lower resolution or are highly specialized in particular materials, presenting a need for a more general-purpose solution.

As new applications evolve rapidly, wireless networks increasingly require low-delay communication to significantly enhance the quality of user experience. In response, the evolution of the medium access control (MAC) layer has gained more attention, particularly through the application of reinforcement learning to optimize access strategies. In order to meet the low-delay requirements, we propose a reinforcement learning-based MAC protocol, named soft actor-critic multiple access (SAC-MA). To mitigate frequent collisions caused by the exploratory behavior, we propose a multiple waiting actions mechanism that allows stations to wait for multiple time slots. This mechanism enables the agent to develop a more flexible and intelligent access strategy, thereby effectively reducing delay. Additionally, we introduce an innovative formulation in which the head-of-line packet is treated as the agent, enabling more timely feedback and observations. We conduct extensive simulations to demonstrate that SAC-MA: 1) reduces delay by approximately 27.9% and 56.5% compared to the conventional MAC protocol with standard parameters under the collision and capture models, respectively; 2) adapts to environmental changes in dynamic scenarios; 3) coexists harmoniously with legacy stations and reduces the network delay in heterogeneous scenarios. Finally, we perform ablation studies to evaluate the effectiveness of the proposed mechanisms.

The emergence of new intelligent applications has fostered the development of a task-oriented communication paradigm, where a comprehensive, universal, and practical metric is crucial for unleashing the potential of this paradigm. To this end, we introduce an innovative metric, the Task-oriented Age of Information (TAoI), to measure whether the content of information is relevant to the system task, thereby assisting the system in efficiently completing designated tasks. We apply TAoI to a wireless monitoring system tasked with identifying targets and transmitting their images for subsequent analysis. To minimize TAoI and determine the optimal transmission policy, we formulate the dynamic transmission problem as a Semi-Markov Decision Process (SMDP) and transform it into an equivalent Markov Decision Process (MDP). Our analysis demonstrates that the optimal policy is threshold-based with respect to TAoI. Building on this, we propose a low-complexity relative value iteration algorithm tailored to this threshold structure to derive the optimal transmission policy. Additionally, we introduce a simpler single-threshold policy, which, despite a slight performance degradation, offers faster convergence. Comprehensive experiments and simulations validate the superior performance of our optimal transmission policy compared to two established baseline approaches.

This paper focuses on spectrum sharing in heterogeneous wireless networks, where nodes with different Media Access Control (MAC) protocols to transmit data packets to a common access point over a shared wireless channel. While previous studies have proposed Deep Reinforcement Learning (DRL)-based multiple access protocols tailored to specific scenarios, these approaches are limited by their inability to generalize across diverse environments, often requiring time-consuming retraining. To address this issue, we introduce Generalizable Multiple Access (GMA), a novel Meta-Reinforcement Learning (meta-RL)-based MAC protocol designed for rapid adaptation across heterogeneous network environments. GMA leverages a context-based meta-RL approach with Mixture of Experts (MoE) to improve representation learning, enhancing latent information extraction. By learning a meta-policy during training, GMA enables fast adaptation to different and previously unknown environments, without prior knowledge of the specific MAC protocols in use. Simulation results demonstrate that, although the GMA protocol experiences a slight performance drop compared to baseline methods in training environments, it achieves faster convergence and higher performance in new, unseen environments.

Recent popularization of the Internet of Vehicles (IoVs) and vehicles-to-everything (V2X) enables the emergence of real-time vehicular applications, posing challenges to resourcelimited vehicles. Toward this end, vehicle edge computing (VEC) has been proposed to alleviate the computational burden on vehicles by leveraging resources from roadside units (RSUs) and VEC servers. While existing works mainly focus on the task requirement for either vehicles or RSUs, the joint task offloading for both V2X and RSUs-to-everything (R2X) has not been fully studied. In this paper, we aim at optimizing the task offloading strategies for both vehicles and RSUs, and adopt a multi-hop task offloading manner to fully utilize the VEC network resources. This problem introduces a severe state-action space shift issue with varying dimensions and representation, which poses challenges for conventional DRL approaches. To address it, we propose a Bidirectional Encoder Representations from Transformers (Bert)-based matching Q-network (BMQN) algorithm. First, we design the BMQN model to efficiently capture correlations among all vehicles and RSUs through bidirectional attention. Then, we propose type-embedded grouped attention and available action embedding to mitigate the overfitting sequence length issue, thereby enhancing generalization capacity. Moreover, we propose to address the state-action space shift issue through a matching-based manner, which can significantly enhance the task offloading ability by matching the states among devices. Simulation results demonstrate that: 1) the BMQN can achieve much better performance than other approaches in scenarios comprising various numbers of vehicles and RSUs as well as diverse road lengths; 2) the BMQN has sufficient generalization capacity to adapt to inexperienced scenarios through matching-based architecture and available action embedding.

The rapid scalability of large language models (LLMs) has driven significant advancements across various natural language processing tasks. However, the immense size of LLMs and the growing demand for large-scale datasets present challenges in fine-tuning these models in resource-constrained environments. Federated learning (FL) has emerged as a promising solution, enabling collaborative model fine-tuning on distributed private data without requiring data sharing. Despite its potential, the heavy computational and communication burdens imposed by LLMs hinder the widespread adoption of FL-based fine-tuning. To mitigate these challenges, we propose FedSIT (Federated Split Importance-Based Tuning), a novel federated fine-tuning framework designed to optimize LLM training in environments with limited computational resources. FedSIT splits the pre-trained model into Bottom, Trunk, and Top layers, offloading the computationally intensive Trunk layer to the server while distributing the Bottom and Top layers to client devices. Additionally, FedSIT leverages layer importance scores to selectively fine-tune the most critical layers, reducing the number of parameters to be fine-tuned. Our extensive experiments demonstrate that FedSIT achieves comparable performance to existing methods while significantly reducing resource requirements, offering an efficient and scalable solution for federated fine-tuning of LLMs in real-world settings.

Recently, the Project Authorization Request (PAR) for IEEE 802.11 bn (Wi-Fi 8) has outlined throughput, the 95th percentile of latency distribution, and medium access control Protocol Data Unit loss as pivotal optimization indicators. In the meanwhile, Spatial Reuse (SR) is harnessed to facilitate concurrent transmissions, aiming to establish highly efficient wireless local area networks. In this paper, we introduce a deep reinforcement learning based approach to optimize SR operation. Taking Wi-Fi 8 PAR into account, we mathematically model the optimization problem and reward function is exquisitely designed to align with the optimization objectives. Our proposed method incorporates a novel Markov state transition process, accounting for information from other nodes in both state and reward. Each node is empowered to autonomously decide whether to transmit, with simultaneous implementation of rate adaptation and power control. We evaluate the performance of our algorithm in random topologies and dynamic environments, demonstrating significant advancements in both latency reduction and loss mitigation.

Multi-link operation (MLO) is regarded as one of the most disruptive features in the upcoming IEEE 802.11be standard, known as Wi-Fi 7. However, the performance characterization of heterogeneous multi-link IEEE 802.11be networks, which consist of Multi-Link Devices (MLDs) and legacy Single-Link Devices (SLDs), remains largely unknown. The challenge originates from the lack of proper modeling of multi-link channel access schemes. In this paper, a novel model is established to study the throughput optimization of heterogeneous two-link IEEE 802.11be networks. MLDs adopt one representative synchronous multi-link channel access scheme with the primary channel. Based on the proposed model, explicit expressions of throughput of MLDs and legacy SLDs are both characterized and verified by simulation results. The network throughput is further maximized by optimally choosing the transmission probabilities of SLDs and MLDs. The analysis shows that MLO can enable MLDs to achieve higher device throughput than SLDs, yet the maximum network throughput of heterogeneous networks decreases compared to homogeneous networks composed solely of MLDs or legacy SLDs.

Multi-link operation (MLO) is one of the pivotal new features in the upcoming IEEE 802.11be Wi-Fi 7 networks. To break the performance limit of traditional random-access-based Wi-Fi, we propose a novel distributed multi-link access scheme for Wi-Fi 7, leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Specifically, a novel parameter, denoted as access opportunity, is introduced as the state transition time step in the decentralized partially observable Markov decision process, which enables the unified modeling of multiple links with varying transmission rates. With the proposed scheme, agents trained in a specific single-link network environment can be directly deployed in multi-link scenarios with varying link transmission parameters, significantly reducing training complexity. The proposed scheme undergoes evaluation in diverse network scenarios, which outperforms the throughput limit of standard multi-link Wi-Fi networks by up to 23.9%, ensures fairness among devices and is robust to environment dynamics.

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.

The scarcity of the licensed spectrum is forcing emerging Internet of Things (IoT) networks to operate within the unlicensed spectrum. Yet there has been extensive observation indicating that performance deterioration and significant unfairness would arise, when newly deployed Aloha-based networks coexist with incumbent Carrier Sense Multiple Access (CSMA)-based WiFi networks, especially without proper adjustment of packet transmission times. The key to ensuring harmonious coexistence lies in properly modeling the coexisting networks and identifying optimal access parameters. However, the complex interactions between Aloha and CSMA nodes present significant challenges to existing analytical models. In this paper, we develop a novel dual-channel analytical framework to capture these interactions. Although Aloha and CSMA nodes coexist on the same physical channel, the developed framework represents them as operating on two separate logical channels to decouple their interactions. A discrete-time Markov renewal process is then employed to characterize the dynamics of this dual-channel network. Based on this framework, the throughput performance of the coexisting network is characterized under various packet transmission times. To achieve harmonious coexistence, the total throughput of the coexisting network under a given desired throughput proportion is optimized by tuning the packet transmission time of CSMA nodes and transmission probabilities. The optimization results indicate that the packet transmission time of CSMA nodes should be set slightly less than that of Aloha nodes. The proposed framework is further applied to enhance the network throughput and fairness of the cohabitation of LTE Unlicensed and WiFi networks.

Joint sensing and communication (JSC) systems aim to leverage the same spectral resources for both communication and sensing tasks within a single system. These systems have the potential to enhance sensing capabilities through advanced communication techniques, while also utilizing precise localization and tracking information from sensing technologies to improve communication. However, the integration of information obtained from sensing and transmitted in communication is not yet fully understood. This paper investigates the challenge of guaranteeing timely delivery of sensing information within JSC systems. We introduce a novel metric, termed as the age of estimation information (AoEI), which integrates radar mutual information (MI) and age of information (AoI). This unified metric effectively captures both the passage of time and the accuracy of estimation information, making it well-suited for the JSC system. Further, we delve into the joint optimization of time and power allocation for a single JSC node with both sensing and communication capabilities. Our objective is to minimize the long-term average AoEI while adhering to a long-term average power constraint. To tackle this problem, we formulate it as an average-reward constrained Markov decision process (CMDP) and propose a model-free constrained deep reinforcement learning (CDRL) algorithm, namely the average policy optimization (APO)-Lagrangian based algorithm. Simulation results demonstrate that our proposed algorithm effectively meets the constraint in dynamic and uncertain environments while achieving a favorable balance between AoEI and power consumption. Additionally, our algorithm outperforms four baseline schemes, showcasing its superior performance.

How to effectively support massive access and data transmission in Internet of Things scenarios has been a long-standing and critical issue for various wireless communication networks. To address this issue, a flexible and efficient medium access control protocol is the key. In this paper, we propose Slotted Aloha with Successive Transmission (SAST) scheme, in which upon the successful transmission of the Head-of-Line (HoL) packet, the node delivers the remaining packets with probability 1 until the buffer is cleared or a collision occurs, thereby capitalizing on immediate channel availability. By formulating vacation queuing models of both node and channel, the access/data throughput and access/data delay are explicitly characterized and optimized by properly choosing the transmission probability of the HoL packet. Our analysis reveals that the maximum data throughput of SAST scheme is 0.5, higher than$e^{-1}$in classic slotted Aloha. The practical insights of the analysis are also demonstrated by taking the example of 2-step Small Data Transmission (SDT) random access in 5G. It is shown that the SAST scheme can be seamlessly implemented into 5G and the comparison with 2-step SDT random access reveals that SAST can improve the throughput performance while significantly reduce the signaling overhead, nearly halved in the saturated case and up to 70% reduction in the unsaturated case.

With the rapid development of emergent applications, wireless networks require the provision of high throughput. Meanwhile, wireless scenarios exhibit highly dynamic characteristics, involving frequent changes in the network scale and traffic. To satisfy the high demand for new applications in dynamic wireless scenarios, a novel medium access control (MAC) protocol is required to allow stations to access the channel with high efficiency and adaptability. Based on multi-agent reinforcement learning (MARL), we propose a new MAC protocol, Multi-task Transformer-based Multiple Access (MTMA). Multi-task learning is applied to train a single actor to adapt to multiple wireless environments simultaneously. To improve the scalability, we propose a transformer-based critic network, which can scale to different wireless scenarios. Moreover, a novel network called “Generalization for N (Gen-N)” network is proposed to enhance the generalization ability. We conduct simulation experiments to demonstrate that MTMA: 1) achieves over 95% of upper bound of throughput while maximizing the fairness performance; 2) outperforms classic MAC protocol and MARL-based baselines in scenarios with saturated and light traffic; 3) can adapt to environmental changes quickly in dynamic scenarios; 4) can generalize to unseen scenarios during training. Finally, the ablation experiments are conducted to evaluate the effectiveness of components used in MTMA.

Next-generation multiple access techniques are crucial for providing low-latency and highly efficient data transmission services. Recently, Deep Reinforcement Learning (DRL) has emerged as a prevalent approach in the multiple access domain, aiming to facilitate user coordination and enhance transmission efficiency. However, current DRL approaches face challenges, including limited generalization ability, low sample efficiency, and the complexities associated with Partially Observable Markov Decision Processes (POMDP), which hinder their application in heterogeneous networks with varying numbers of nodes and configurations. In this paper, we propose a foundation model-based multiple access (FMA) algorithm. To address severe POMDP and sample inefficiency issues, we decompose the multiple access problem into two parts: a transmission decision part and a configuration estimation part. We leverage the strong generalization and inference capabilities of the foundation model, utilizing a Deep Learning (DL) approach instead of DRL for training, and adopt the Low-Rank Adaptation (LoRA) technique to fine-tune the foundation model for downstream multiple access tasks. Simulation results demonstrate that: 1) through the decomposition, the FMA approach exhibits sufficient generalization and inference abilities to adapt to various scenarios with various protocols, configurations, and numbers of heterogeneous nodes; 2) by incorporating expert knowledge, the FMA approach can significantly enhance network performance while ensuring certain fairness requirement for heterogeneous nodes.

The performance of mean square error (MSE) in channel estimation for multi-cell massive multiple-input multiple-output (MIMO) systems with Rician fading is studied. In this report, we initially derive the closed-form expressions of the probability distribution function and cumulative distribution function of MSE, which are applicable for any number of base-station antennas M and any Rician K-factor. Furthermore, we perform an asymptotic analysis for both strong line-of-sight (LOS) and Rayleigh fading scenarios. Subsequently, we present closed-form expressions for the expectation of MSE $\left(\operatorname{Exp}_{\mathrm{mse}}\right)$ and the variance of MSE. Next, utilizing maximal-ratio combining detector, we investigate the relationship between achievable downlink spectral efficiency and $\operatorname{Exp}_{\mathrm{mse}}$. It is observed that as $\operatorname{Exp}_{\text {mse }}$ increases, the achievable downlink spectral efficiency constantly reduces, eventually reaches a given constant. Finally, Monte-Carlo simulations are performed to corroborate the results discussed earlier.

We optimize the Age of Information (AoI) in random access networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. We derive analytical expressions for the transmission success probability and time-average AoI using stochastic geometry tools. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the time-average AoI. In addition, we establish a scaling law for the time-average AoI in random access networks, revealing that the optimal time-average AoI increases linearly with the deployment density. Notably, the growth rate under TSA is half of that under conventional slotted ALOHA.

We consider the age of information (AoI) evaluation in an Aloha-based random access network powered by energy harvesters. We derive a closed-form expression for the average AoI with general energy buffer capacity. The average AoI is then optimized by adjusting the update rate. The results indicate that when the sum of the energy arrival rate of all nodes is greater than or equal to one, the optimal average AoI in the Aloha network is equivalent to that in a network without energy constraints, by setting the update rate to one divided by the total number of nodes. The optimized average AoI then grows linearly with the number of nodes. Otherwise, a degradation of the optimal average AoI emerges, and the update rate should be tuned to be higher than the energy arrival rate.

A general consumer around the world gets general awareness about the causes of mechanical, electrical or digital problems in their house (or neighborhood) and how to fix them through common life experiences or learning from their peers. However, despite similar level of integration into everyday life, the understanding of wireless technologies lags significantly. This bottleneck leads to consumer dissatisfaction and reduced profits for cellular providers.

A general consumer around the world gets general awareness about the causes of mechanical, electrical or digital problems in their house (or neighborhood) and how to fix them through common life experiences or learning from their peers. However, despite similar level of integration into everyday life, the understanding of wireless technologies lags significantly. This bottleneck leads to consumer dissatisfaction and reduced profits for cellular providers.

The fundamental bottleneck in adapting data-driven wireless solutions to real world is the lack of tools for augmenting good quality data which is environment-specific. Indeed, much of the data required for popular data-driven wireless communication and sensing systems requires domain expertise beyond the reach of an average consumer. This demo presents a radical new vision for generating synthetic data in consumer-specific environments by leveraging the power of modern compute.

Mobile-edge Computing (MEC) has now emerged as a complement to cloud computing, providing computational capacity for the resources-constrained edge devices. Recently, intelligent computation offloading and cache placement stands as effective approaches to enhance the performance of dynamic MEC networks. In this paper, we propose an online centralized joint resource management approach, named Transformer-based Actor-Critic (TAC), to minimize the task execution time subject to resource constraints. We decouple this mixed-integer non-linear programming (MINLP) problem into a non-convex offloading decision part and a convex joint resources allocation part, and propose the TAC approach to address the non-convex task offloading problem with low computational complexity. In the joint resources management problem, the high-dimensional state-action space is addressed by the transformer-based actor-critic architecture. Through the proposed TAC, the joint cache, communication and computation resource management can be obtained without the knowledge of future task arrivals. Simulation results demonstrate that the TAC can save 48.4% average task execution time with only 2.3% additional computation delay compared to Random with lowest computational complexity. In particular, it further demonstrates great generalization ability to enhance the performance in untrained scenarios.

With the development of wireless communication systems, the large-scale deployment of Internet of Things (IoT) devices becomes popular. Due to limited energy, the multiple access approaches without carrier sensing requirement are widely deployed in IoT devices, including Aloha and time-division multiple access (TDMA). However, these approaches encounter the transmission inefficiency issue, especially in dynamic heterogeneous networks comprising nodes with diverse protocols and varying numbers and transmission configurations over time. In this article, combining curriculum learning (CL) and multitask reinforcement learning (MTRL), we propose the curriculum multitask heterogeneous-agent proximal policy optimization (CMHA) algorithm to improve the throughput performance while guaranteeing fairness in dynamic heterogeneous networks. We introduce the elastic weight consolidation (EWC) in the CMHA to further enhance generalization capacity, which can better address the challenging MTRL problem in dynamic heterogeneous networks. Combining the monotonic improvement feature of heterogeneous-agent proximal policy optimization (HAPPO) and the generalization capacity of EWC, the proposed CMHA can achieve a nearly monotonic improvement in all possible scenarios. The simulations show that the CMHAalgorithm 1) has sufficient generalization capacity for massive scenarios in dynamic heterogeneous networks; 2) can significantly enhance the network throughput; and 3) can guarantee the fairness of both agents and heterogeneous nodes.

Slotted Aloha provides a simple solution for facilitating the massive access of machine-type-devices (MTDs). To prolong the battery lifetime of MTDs, on-demand sleep mechanisms are usually adopted, with which the lifetime and delay performance of MTDs crucially depends on the access and sleep parameters, and may significantly deteriorate if they are not properly selected. In this article, an analytical framework is proposed for on-demand-sleep-based slotted Aloha to optimize the lifetime and delay performance. Specifically, by establishing a novel node-centric model, explicit expressions of the expected lifetime of nodes and the mean queueing delay of data packets are derived, based on which the transmission probability of each node for maximizing the expected lifetime and minimizing the mean queueing delay is obtained. The tradeoff between the optimal lifetime and delay performance is also characterized. The practical insights of the analysis are further demonstrated by conducting a case study on the 2-step random-access-based small data transmission (RA-SDT) scheme with mobile initiated connection only (MICO)-based on-demand sleep mechanism.

Federated learning (FL) framework enables user devices to collaboratively train a global model based on their local data sets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Besides, to quantify the training performance, the utility of clustered model training is defined based on the analysis results. Then, aiming at optimizing the system utility, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two subproblems. First, given the results of device clustering, a low-complexity iterative algorithm based on the convex optimization theory is proposed to make the bandwidth allocation and the transmit power control. Then, according to the individual stability, a coalition formation algorithm is proposed for the device clustering. Finally, the real-data experiments on the classification tasks (e.g., MNIST, CIFAR-10, and CIFAR-100) validate the results of convergence analysis and advantages of the proposed algorithm in terms of the test accuracy.

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.

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.

We optimize the Age of Information (AoI) in mobile networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. Using stochastic geometry tools, we derive analytical expressions for the transmission success probability, mean peak AoI, and time-average AoI. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the mean peak and time-average AoI, respectively. In addition, we establish a scaling law for the mean peak AoI and time-average AoI in mobile networks, revealing that the optimal mean peak AoI and time-average AoI increase linearly with the deployment density. Notably, the growth rate of time-average AoI under TSA is half of that under SA. When considering the optimal mean peak AoI, the TSA protocol exhibits comparable performance to the traditional slotted ALOHA protocol. These findings conclusively affirm the advantage of TSA in reducing higher-order AoI, particularly in densely deployed networks.

This paper focuses on the timely delivery of sensing information in a joint sensing and communication (JSC) system to meet the requirements of emerging applications. Specifically, we investigate the time allocation of a single JSC node equipped with both sensing and communication functions to minimize the long-term average age of estimation information (AoEI) while satisfying the long-term average power constraint. The proposed metric, AoEI, combines radar mutual information (MI) and age of information (AoI) to capture both the passage of time and the accuracy of estimation information, making it more suitable for the JSC system. To solve this problem, we formulate the time allocation problem as a constrained Markov decision process (CMDP) and propose a model-free constrained deep reinforcement learning (CDRL) based algorithm. The simulation results demonstrate that the proposed algorithm can achieve a good trade-off between AoEI and power consumption and converge to a policy that satisfies the constraint in a highly dynamic and uncertain environment.

With the rapid development of smart devices, the next generation wireless networks (NGWNs) are expected to achieve high access efficiency in a high-dynamic scenario. To tackle the above challenges in NGWNs, this paper proposes a new MAC protocol, MAAC-advanced Listen-Before-Talk (MLBT), which employs multi-agent reinforcement learning (MARL) algorithm. As a MARL paradigm, centralized training with decentralized execution (CTDE) is confronted with the scalability issue. To address it, we design a scalable neural network architecture based on the attention mechanism, which can cope with the varying number of stations. Moreover, a novel reward function is designed to achieve the max-min fairness and maximum aggregate network throughput simultaneously. Extensive simulation experiments are provided to show that MLBT approaches the optimal performance and accelerates the centralized training process when stations join or leave the network.

This paper studies the downlink spectral efficiency of a two-way cell-free massive multiple-input multiple-output system with few-bit analog-to-digital converts (ADCs). By utilizing minimum mean squared error channel estimation method and maximal-ratio transmission precoder, we derive a closed-form expression for the effective downlink signal-to-interference-plus-noise ratio (SINR), which applies to any number of access point (AP) antennas M and distortion factors of ADCs in both AP and user pair sides. Additionally, the obtained analytical expression specializes to the conventional one when the ideal ADCs are adopted. Moreover, the asymptotic performance and power scaling law of the effective SINR in high M regime are studied. The corresponding analysis indicates that increasing M can provide considerable gains to compensate the rate loss caused by non-ideal ADCs and also, appropriately cutting down the pilot power and transmission power will not affect the SINR performance in high M region. Finally, all the theoretical results are verified via simulations.

Abstract Deploying networks in unlicensed spectrum has been drawing significant attention, which serves to alleviate the increasing demands in licensed spectrum. However, the network coexistence in unlicensed channel may lead to throughput degradation and unfairness. An appropriate spectrum‐sharing mechanism is therefore of great significance. In this paper, we study the performance limit of two representative mechanisms used in the coexistence with WiFi, including Duty Cycle (DC) and Listen‐Before‐Talk (LBT). In particular, both the throughputs of the coexisting network and WiFi under two mechanisms are derived as explicit expressions of system parameters, based on which the maximum total throughput of the coexisting network and WiFi is characterized under throughput fairness and 3GPP fairness, respectively. A systematic comparison between the optimal throughput performance of DC and LBT is conducted. It is found that if the coexisting network with LBT occupies the channel for a large period each time it successfully accesses the channel, then the maximum total throughput in LBT would be close to that in DC under both throughput fairness and 3GPP fairness. The optimal settings for DC and LBT mechanisms to achieve maximum total throughput are obtained, respectively, which sheds important light on the design of fair and efficient spectrum‐sharing protocols.

Delivering as many data packets as possible and making the life time of the network as long as possible is one fundamental request for battery-driven wireless network design, where sleeping schemes are usually adopted for prolonging the life time, while, at the sacrifice of the throughput performance. For random access networks, fulfilling this fundamental request is rather challenging due to the distributed nature of the access behavior of nodes. This paper considers massive Machine-Type Communication (mMTC) networks where each node adapts the representative random access scheme Aloha and periodical sleeping-awake cycle. We aim to address how to maximize the life-time throughput of each node, i.e., average number of packets each node can successfully deliver during its life time, with a guarantee of targeted life time via optimal selection of the channel access probability and the sleeping ratio of each node. By deriving the explicit expressions of the life time and the life-time throughput of each node and jointly tuning both the channel access probability and the sleeping ratio, we characterize the maximum life-time throughput with targeted life time, and the corresponding optimal settings. The analysis reveals that if only the channel access probability is optimally tuned, then the throughput and life-time throughput cannot be optimized simultaneously when the network becomes saturated with a large packet arrival rate. In contrast, the network would operate at unsaturated conditions via the joint tuning of the access probability and the sleeping ratio. In this case, the maximum life-time throughput always grows with the packet arrival rate. In addition, it is shown that the effect of the life-time constraint becomes significant only when it exceeds a threshold, where maximum life-time throughput will sacrifice for life-time expectation. The analysis sheds important light on the access and sleeping scheme design of practical Aloha-type networks. By taking Narrow Band-IoT with Power Saving Mode (PSM) as an example, extensive simulation results corroborate that with the proposed optimal setting, the life-time throughput could be significantly improved, especially when the life time requirement is demanding, e.g., 10 years without battery replacement.

With the rapid development of 5G, massive machine-type communication is expected to experience significant growth, leading to severe random access collisions. To address this issue, we first adopt deep neural networks to detect random access collisions by learning the features of the received signals. Based on the collision-detection results, we propose a double-contention random access (DCRA) scheme, with which the base station can schedule one more contention process for devices experiencing collisions. To fully harness the collision-resolution capability of the proposed DCRA scheme, we further analyze its performance and illustrate how to tune the backoff parameters to optimize the network throughput. It is revealed that the maximum throughput of the DCRA scheme depends on the number of random access preambles and the collision recognition accuracy. The corresponding optimal backoff parameters are then obtained, which greatly facilitates implementations in practice. Simulation results show that with a high collision recognition accuracy, the proposed scheme can achieve significant throughput improvement.

Carrier Sense Multiple Access (CSMA) has been widely applied to various kinds of wireless networks, such as Wi-Fi, to serve portable devices which are usually greedy in terms of throughput, but with finite battery budget. Accordingly, how to optimize the usage of finite battery budget to get the best possible throughput performance is of great importance. This paper aims to address this issue by focusing on a saturated CSMA network. Explicit expressions of maximum energy efficiency and the corresponding optimal backoff parameter with or without throughput constraint are derived. It is revealed that optimizing the energy efficiency leads to throughput performance degradation. With a stringent throughput constraint, the energy efficiency has to be sacrificed. The energy efficiency and the throughput can be optimized at the same time only in special cases, e.g., the network size is large. The analysis is verified by simulations and sheds important light on performance optimization of practical CSMA-based networks such as Wi-Fi 6 networks.

Federated learning (FL) framework enables user devices collaboratively train a global model based on their local datasets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Then, aiming at optimizing the model training performance, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two sub-problems. Specifically, a coalition formation algorithm is proposed for the device clustering sub-problem, and the sub-problem of bandwidth allocation and transmit power control is solved directly due to its convexity. Finally, simulation experiments are conducted on the MNIST dataset to validate the performance of the proposed algorithm in terms of test accuracy.

The deployment of 5G New Radio in unlicensed spectrum is a promising solution to alleviate the spectrum crunch for cellular networks. With the openness of unlicensed spectrum, 5G New Radio Unlicensed (NR-U) will coexist with the incumbent Wi-Fi networks. It is therefore important to study how to maintain harmonious coexistence with the Wi-Fi network. To address this issue, this paper considers two alternative throughput optimization strategies under the 3GPP fairness by adjusting the access parameter: one is to maximize the total throughput of coexisting scenario, and the other is to maximize the throughput of NR-U network. It is shown that the throughput gain of both optimization strategies are related to the initial backoff window size and the network size of Wi-Fi. Moreover, the first strategy can maximize the total throughput yet it may be unfair to the NR-U network while the second strategy can maximize NR-U throughput yet may be harmful to the total throughput. In practical scenario where the IEEE 802.11 EDCA protocol is adopted in Wi-Fi, the performance of NR-U cannot be guaranteed when optimizing the total throughput, and thus optimizing the throughput of NR-U is suggested for fair coexistence.

Opening the unlicensed bands provides additional spectrum resources for the next generation wireless network, while severe unfairness and performance degradation occur when one coexists with the incumbent users of these bands. Therefore, plenty of efforts have been made towards fair coexistence, mainly focusing on parameter tuning of listen-before-talk (LBT) and duty-cycle (DC) mechanisms. For better utilization of the unlicensed bands, it is of paramount importance to establish an access mechanism that guarantees the fairness objective among feasible mechanisms. Such access mechanism and the corresponding benchmark, nevertheless, remain largely unknown. To address this issue, this paper considers the coexistence between WiFi and the other unlicensed nodes, and aims to maximize the α-fairness between them. A benchmark is first given by solving the optimization problem. Then we propose a deep reinforcement learning (DRL) mechanism to help the unlicensed nodes make access decisions, such that they coexist with WiFi harmoniously. Extensive simulations have been carried out, and the results show that the DRL mechanism can approach the benchmark.

Multi-link operation is considered to be one of the new key features in the next generation WiFi 7, i.e., IEEE 802.11be. This paper studies the maximum network sum rate of a general M-link 802.11be network with two different synchronous multi-link channel access methods being proposed by Task Group BE, i.e., Longest Backoff and Shortest Backoff. By using a Markov renewal process to model the behavior of each Head-of-Line packet, explicit expressions of the maximum network sum rate and the corresponding optimal initial backoff window sizes are derived, and verified by simulation results. The analysis shows that Longest Backoff and Shortest Backoff achieve an identical maximum network sum rate. However, to achieve the performance limit, the initial backoff window sizes need to be adaptively tuned in a different manner under the two access methods. As the number of links grows, the initial backoff window size with Longest Backoff should be monotonically decreased, while that with Shortest Backoff should be enlarged.

5G New Radio (NR) introduces a new Radio Resource Control (RRC) state, i.e., RRC INACTIVE, for providing the efficient service for massive Machine Type Communications (mMTC). To release the full potential of the new RRC state, it is of great importance to properly model the new RRC state transition process and reveal the effect of system parameters on the network performance. To address the above issue, this paper proposes a novel 5G RRC analytical model based on discrete-time vacation queuing theory, where the time period of the device in RRC INACTIVE state is regarded as the vacation period of the server in the queueing system. By leveraging this novel model, key performance metrics, such as the random access rate and the RRC resource utilization ratio, are explicitly characterized and obtained as functions of system parameters, including packet arrival rate, service rate and inactivity timer. The analysis reveals that to reduce the random access rate, the system should increase the inactivity timer, packet arrival rate or decrease the service rate. On the other hand, to improve the RRC resource utilization ratio, the inactivity timer should be cut down especially when the arrival rate is small or the service rate is large. The analysis is verified by simulations and sheds important light on practical 5G network design for supporting mMTC.

In this paper, we focus on visible light-based position and pose detection (VLP) for user devices in dynamical environments. Traditional model-based VLP methods usually depend on a perfect signal propagation model (SPM) with fixed parameters, and hence their performance will be seriously decreased when localization environment varies over time, e.g., due to diffuse scattering and reflections. To address this challenge, in this paper we propose a novel branch-structured residual convolutional neural network (RCNN)-based VLP method, without any requirement on perfect SPM knowledge. We observe that there are environment-invariant texture features in received visible light signal samples, which can be exploited for VLP performance enhancement. A branch-structured RCNN-based VLP scheme is devised for exploiting diverse-level stable texture features from received measurement samples, rendering a reliable VLP solution against environmental dynamics. It is verified by simulations that our branch-structured RCNN-based VLP solution outperforms existing machine learning-based VLP methods.

In this demonstration, in collaboration with licensed therapists, we introduce an AI therapist that takes advantage of the smart-home environment to screen day-to-day functioning and infer mental wellness of an occupant. Our system can assess a user's daily functioning and mental wellness based on a combination of direct conversation with users and information obtained from smart home devices using psychological rubrics proposed in [1]. We demonstrate that our system can converse with a user in a natural way (through a smartphone or smart speaker) and analyze a user's response semantically and sentimentally. In addition, we show that our system can provide preliminary interventions to help improve the user's wellness. In particular, when abnormal behavior is detected during the conversation or by smart home devices, the system provides psychotherapeutic consolations during the conversation and will check on the occupant's condition by actuating a home robot.

The rate adaptation (RA) algorithm, which adaptively selects the rate according to the quality of the wireless environment, is one of the cornerstones of the wireless systems. In Wi-Fi networks, dynamic wireless environments are mainly due to fading channels and collisions caused by random access protocols. However, existing RA solutions mainly focus on the adaptive capability of fading channels, resulting in conservative RA policies and poor overall performance in highly congested networks. To address this problem, we propose a model-free deep reinforcement learning (DRL) based RA algorithm, named as drl RA, in this work, which incorporates the impact of collisions into the reward function design. Numerical results show that the proposed algorithm improves the throughput by 16.5% and 39.5% while reducing the latency by 25% and 19.3% compared to state-of-the-art baselines.

The timeliness of information is of capital importance for numerous Internet of Things (IoT) services. To improve the information freshness in large-scale distributed IoT systems, this paper focuses on the Peak Age of Information (PAoI) optimization of slotted Aloha networks. Specifically, by assuming the first-come-first-served (FCFS) service discipline and Bernoulli packet arrival model, the mean PAoI is characterized and then optimized by either individually tuning the channel access probability or jointly tuning the channel access probability and packet arrival rate of each sensor. The explicit expressions of optimal parameter settings and the corresponding minimum PAoI are obtained, based on which the age-throughput tradeoff is evaluated. The analysis is verified by simulations. It is found that in the massive access scenarios, the minimum PAoI linearly increases with the network scale in both individual optimization and joint optimization cases, while the latter attains a lower increasing rate, better age performance, and less throughput loss.

To meet the demanding need for global connectivity, non-terrestrial networks can provide essential support to complement and extend the terrestrial infrastructure. However, the presence of non-terrestrial networks comes up with new demands. For example, a critical problem is satisfying the latency constraints of delay-sensitive applications while reducing the signaling consumption to save valuable channel resources in the random access stage. To address this issue, we propose a distributed access class barring (ACB) factor determination algorithm in this paper to satisfy the specific latency constraints of delay-sensitive applications and reduce the signaling exchange between user equipments (UEs) and the base station simultaneously. With this algorithm, UEs can estimate the required ACB factor only by their previous experiences in an estimation period rather than relying on the base station. Simulations show that the proposed distributive ACB factor determination algorithm can satisfy the requirements of delay-sensitive applications well when the delay constraint is not too strict. Besides, the influence of the variation of UEs and the estimation period is also discussed. It is found that the estimation period plays an important role in the accurate estimation of the ACB factor and needs to adapt to the changes in the number of UEs.

In large-scale wireless networks, severe interference may incur that leads to the age of information (AoI) degradation. It is therefore important to study how to optimize the AoI performance. This paper focuses on the average AoI minimization in random access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the average AoI is derived, based on which the optimal average AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. We further compare the average AoI optimization with the peak AoI optimization. The comparison reveals that the optimal channel access probability for the average AoI optimization and the peak AoI optimization are the same. Yet, the optimal packet arrival rate for the average AoI optimization is smaller than that for the peak AoI optimization. The gap enlarges when the node deployment density becomes small.

5G New Radio Unlicensed (5G NR-U) and WiFi are considered to be the two most representative radio access technologies in the newly released 6 GHz unlicensed bands, and thus their efficient and fair coexistence becomes crucial. In this paper, we study the coexistence performance of 5G NR-U and WiFi by accounting the new physical layer (PHY) enhancements in 5G NR including flexible numerologies and mini-slot scheduling. Consider the 3GPP notion of fairness as the requirement, we further study how to maximize the total network effective throughput of the WiFi and NR-U coexisting network. Explicit expressions of the maximum total network effective throughput and the corresponding optimal initial backoff window sizes of WiFi and NR-U nodes are derived, and verified by simulation results. The analysis shows that if the transmission opportunity (TXOP) value of NR-U nodes exceeds a certain threshold, then a win-win coexistence can be achieved, where both the WiFi and 5G NR-U network can perform no worse than the case when two WiFi networks coexist. In this case, the maximum total network effective throughput steadily grows as the time slot length of NR-U nodes decreases, indicating the PHY enhancement in 5G NR can benefit the coexistence performance of 5G NR-U and WiFi in the unlicensed spectrum.

For battery-limited IoT networks, the energy efficiency and Age of Information (AoI) are two key performance metrics. Yet the tradeoff between energy efficiency and AoI remains unclear for large-scale networks since the analysis becomes challenging due to the couple queue problem. This paper aims to address this issue by studying the performance limit of energy efficiency under AoI constraint.Specifically, we evaluate the energy efficiency via the expected number of successfully transmitted packets during each transmitter’s life time for which the explicit expression is derived based on the spatio-temporal analytical framework in [1]. By further taking the AoI constraint into consideration, explicit expressions of the Maximum Expected Number of Successfully Transmitted Packets (MENSTP) and the corresponding channel access probability are obtained. The analysis reveals that if the Power Ratio of the Transmission state and the Waiting state (PRTW) equals one, i.e., the energy consumption per time slot of the transmission state equals to that of the waiting state, then the expected number of successfully transmitted packets during each transmitter’s life time and the peak AoI can be optimized simultaneously; otherwise, the MENSTP declines with a stringent AoI constraint. Moreover, the performance gap enlarges when the PRTW or the node distribution density increases which reveals a crucial tradeoff between the energy efficiency and AoI. It is therefore of importance to properly tuning the channel access probability to strike an optimal energy-age tradeoff in battery-limited large-scale IoT networks.

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks with a large number of nodes, severe interference may incur that leads to Age-of-Information (AoI) degradation. It is, therefore, important to study how to optimize the AoI performance. This article focuses on the AoI minimization in random-access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on which the optimal peak AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. The analysis shows that when the channel access probability (resp., the packet arrival rate) is given, the optimal packet arrival rate (resp., the optimal channel access probability) is equal to one when nodes are sparsely deployed, and decreases as the node deployment density increases. With a joint tuning of these two system parameters, the optimal channel access probability always equals one. Moreover, with the sole tuning of the channel access probability, the optimal peak AoI is improved with a smaller packet arrival rate only when the node deployment density is high. In contrast, a higher channel access probability always improves peak AoI performance when the packet arrival rate is solely tuned. The analysis in this article sheds important light on freshness-aware design for large-scale networks.

Age of Information (AoI), a notion that measures the information freshness, is an essential performance measure for time-critical applications in Internet of Things (IoT). With the surge of computing resources at the IoT devices, it is possible to preprocess the information packets that contain the status update before sending them to the destination so as to alleviate the transmission burden. However, the additional time and energy expenditure induced by computing also make the optimal updating a nontrivial problem. In this article, we consider a time-critical IoT system, where the IoT device is capable of preprocessing the status update before the transmission. Particularly, we aim to jointly design the preprocessing and transmission so that the weighted sum of the average AoI of the destination and the energy consumption of the IoT device is minimized. Due to the heterogeneity in transmission and computation capacities, the durations of distinct actions of the IoT device are nonuniform. Therefore, we formulate the status updating problem as an infinite horizon average cost semi-Markov decision process (SMDP) and then transform it into a discrete-time Markov decision process. We demonstrate that the optimal policy is of threshold type with respect to the AoI. Equipped with this, a structure-aware relative policy iteration algorithm is proposed to obtain the optimal policy of the SMDP. Our analysis shows that preprocessing is more beneficial in regimes of high AoIs, given it can reduce the time required for updates. We further prove the switching structure of the optimal policy in a special scenario, where the status updates are transmitted over a reliable channel and derive the optimal threshold. Finally, simulation results demonstrate the efficacy of preprocessing and show that the proposed policy outperforms two baseline policies.

In the next generation wireless networks, more applications will emerge, covering virtual reality movies, augmented reality, holographic three-dimensional telepresence, haptic telemedicine and so on, which require the provisioning of high bandwidth efficiency and low latency services. In order to better support the aforementioned applications and services, novel distributed channel access (DCA) schemes are necessary. Therefore, we propose a new MAC protocol, QMIX-advanced Listen-Before-Talk (QLBT), based on the cutting-edge multi-agent reinforcement learning (MARL) algorithm. It employs a centralized training with decentralized execution (CTDE) framework to exploit the overall information of all agents during training, and ensure that each agent can independently infer the optimal channel access behavior based on its local observation. We enhance QMIX, a well-known MARL algorithm, by introducing an extra individual Q-value for each agent in the mixing network apart from the original total Q-value, which makes QLBT more stable. Moreover, delay to last successful transmission (D2LT) is first introduced in this work as a part of the observations of each QLBT agent, which facilitates agents to reach a cooperative policy that prioritizes the agent with the longest delay. Finally, extensive simulation experiments are provided to show that the proposed QLBT algorithm: 1) outperforms CSMA/CA and even its theoretical performance bound in various scenarios including saturated traffic, unsaturated traffic and delay-sensitive traffic; 2) is robust in dynamic environment; and 3) is able to friendly coexist with “legacy” CSMA/CA stations.

In Internet of Things (IoT), the freshness of status updates is crucial for mission-critical applications. In this regard, it is suggested to quantify the freshness of updates by using Age of Information (AoI) from the receiver’s perspective. Specifically, the AoI measures the freshness over time. However, the freshness in the content is neglected. In this paper, we introduce an age-based utility, named asAge of Changed Information(AoCI), which captures both the passage of time and the change of information content. By modeling the underlying physical process as a discrete time Markov chain, we investigate the AoCI in a time-slotted status update system, where a sensor samples the physical process and transmits the update packets to the destination. With the aim of minimizing the weighted sum of the AoCI and the update cost, we formulate an infinite horizon average cost Markov Decision Process. We show that the optimal updating policy has a special structure with respect to the AoCI and identify the condition under which the special structure exists. By exploiting the special structure, we provide a low complexity relative policy iteration algorithm that finds the optimal updating policy. We further investigate the optimal policy for two special cases. In the first case where the state of the physical process transits with equiprobability, we show that optimal policy is of threshold type and derive the closed-form of the optimal threshold. We then study a more generalized periodic Markov model of the physical process in the second case. Lastly, simulation results are laid out to exhibit the performance of the optimal updating policy and its superiority over the zero-wait baseline policy.

To reduce the signaling overhead for sporadic small packet transmission in massive Machine Type Communications (mMTC), Packet-Based Random Access (PBRA) scheme is introduced in 5G system, where devices can transmit data packets in the random access procedure without connection establishment. Yet, even with PBRA, the signaling overhead may surge if the system parameters are configured improperly. This paper aims to address this issue by studying how to tune the Access Class Barring (ACB) factor to maximize the throughput while maintaining the signaling-to-throughput ratio below a certain level. Explicit expressions of maximum throughput and the corresponding optimal ACB factor in saturated and unsaturated cases are derived. It reveals that with a demanding requirement on signaling-to-throughput ratio, the throughput performance has to be sacrificed even with optimal tuning of ACB factor. To boost the throughput performance, the system should either loose the signaling constraint or enlarge the packet length. The analysis is verified by simulations and sheds important light on practical 5G network design for supporting mMTC with PBRA.

Federated learning (FL) enables devices to update a global model while keeping the training data local, so that data privacy is protected. However, the local data of devices is usually non-independent and identically distributed (non-i.i.d.), which leads to performance degradation. This paper aims to address this issue by a client-selection approach. In particular, in consideration of balancing the label distribution of the selected clients, a new client selection method called grouping based scheduling (GS) scheme is proposed, with which clients are divided into several groups based on a new metric called group earth mover’s distance (GEMD). Experiment results show that the GS can improve the performance of FL algorithms, compared to the random scheduling scheme. An encryption method is further proposed to enhance privacy protection, which facilitates the application of the proposed GS scheme.

Low latency and high reliability are significant trends in the development of wireless communications. Both the new generation of data link system, Tactical Targeting Network Technology (TTNT), and the fifth-generation mobile networks (5G) have put forward higher requirements for low latency and high reliability. Statistical priority-based multiple access protocol (SPMA) can be applied to these systems by virtue of its excellent performance. It is very important to model and analyze the performance of SPMA. In this paper, we establish the node state model by discrete-time Markov renewal process under a saturated network. We construct a fixed-point equation to solve the medium access probability. Then the average packet success rate and throughput are obtained. We evaluate the performance of the saturated SPMA system through the three variables. We also analyze the impact of system parameters on protocol performance. Extensive simulations demonstrate that our theoretical results closely match the simulation results. Moreover, the throughput limit can be obtained by adjusting the system parameters.

Federated learning (FL) has become a promising solution to train a shared model without exchanging local training samples. However, in the traditional cloud-based FL framework, clients suffer from limited energy budget and generate excessive communication overhead on the backbone network. These drawbacks motivate us to propose an energy-aware hierarchical federated learning framework in which the edge servers assist the cloud server to migrate the local models from the clients. Then a joint local computing power control and client association problem is formulated in order to minimize the training loss and the training latency simultaneously under the long-term energy constraints. To solve the problem, we recast it based on the general Lyapunov optimization framework with the instantaneous energy budget. We then propose a heuristic algorithm, which takes the importance of local updates into account, to achieve a suboptimal solution in polynomial time. Numerical results demonstrate that the proposed algorithm can reduce the training latency compared to the scheme with greedy client association and myopic energy control, and improve the learning performance compared to the scheme in which the associated clients transmit their local models with the maximal power.

The massive machine-type communications (mMTC) is one of the three generic services for 5G. With the connection-based random access (CBRA) scheme, each machine-type device (MTD) establishes a connection with the base station (BS) prior to its data transmission. Due to the explosive growth of the number of MTDs, many MTDs would establish connections with the BS, which necessitates the study on how to efficiently manage the massive connections with MTDs. To address this issue, in this article, we propose a unified utility-based analytical framework for the optimal connection management of mMTC in 5G networks, where the signaling overhead for connection establishment, access delay, and the connection resource utilization ratio is included. Specifically, we first derive key performance metrics, i.e., the mean time length of each connection and resource utilization ratio, as functions of traffic input rate and inactivity timer. By further considering the signaling overheads and access delay of each MTD, the network utility is formulated and maximized by optimally choosing the inactivity timer. We then present a detailed discussion on the effect of system parameters on the optimal inactivity timer and the corresponding maximum network utility. Finally, we extend the analytical framework to the scenario in which the CBRA scheme coexists with the packet-based random access scheme, i.e., transmitting packets in the random access channel without connection establishment. The critical threshold in terms of the traffic input rate is characterized, which sheds important light on the access scheme selection issue.

The massive Machine Type Communications (mMTC) is one of the three generic services to be supported by 5G wireless systems. To fulfill the ever-increasing network access demand from a large number of Machine Type Devices (MTDs), this paper develops a cluster-based group paging scheme. Specifically, with the proposed scheme, MTDs are divided into clusters and the group paging period is decomposed into two parts: intracluster access period and inter-cluster access period. In the intracluster access period, preamble reuse is adopted for facilitating the access request transmissions from MTDs in each cluster to its cluster head. In the inter-cluster access period, only cluster heads send access requests to the base station.To evaluate and optimize the access efficiency of the proposed scheme, the probability of successful access of each MTD is characterized, based on which the maximum probability of successful access and the corresponding optimal number of clusters and optimal length of the intra-cluster access period are obtained as explicit functions of key system parameters including the number of preambles and the number of MTDs. A comparative study of the access efficiency for group paging with clustering and without clustering is conducted, which reveals the critical threshold in terms of the number of MTDs, above which clustering is beneficial. The analysis is verified via extensive simulations. It is shown that the access performance of the proposed scheme significantly outperforms that of the traditional group paging scheme, especially in massive access scenarios.

The freshness of status updates is imperative in mission-critical Internet of things (IoT) applications. Recently, Age of Information (AoI) has been proposed to measure the freshness of updates at the receiver. However, AoI only characterizes the freshness over time, but ignores the freshness in the content. In this paper, we introduce a new performance metric, Age of Changed Information (AoCI), which captures both the passage of time and the change of information content. Also, we examine the AoCI in a time-slotted status update system, where a sensor samples the physical process and transmits the update packets with a cost. We formulate a Markov Decision Process (MDP) to find the optimal updating policy that minimizes the weighted sum of the AoCI and the update cost. Particularly, in a special case that the physical process is modeled by a two-state discrete time Markov chain with equal transition probability, we show that the optimal policy is of threshold type with respect to the AoCI and derive the closed-form of the threshold. Finally, simulations are conducted to exhibit the performance of the threshold policy and its superiority over the zero-wait baseline policy.

The deployment of Long Term Evolution (LTE) networks in unlicensed spectrum is a promising solution to overcome the scarcity of licensed spectrum. Yet it has been widely observed that severe unfairness and performance degradation would occur when LTE coexists with WiFi, the incumbent user of unlicensed bands, without proper adjustment. Fair and efficient coexistence of these two networks thus becomes crucial. It, nevertheless, remains largely unknown how to optimize the total throughput of the LTE and WiFi networks under fairness constraints. To address the above open issue, this paper considers that a WiFi network coexists with an LTE network using the Category 3 or Category 4 Listen-Before-Talk (LBT) mechanism, and aims to characterize the maximum total throughput of the LTE and WiFi networks under two fairness constraints including throughput fairness and 3GPP fairness. The analysis shows that the maximum total throughput is independent of which LBT mechanism the LTE network adopts, and can be improved as the mean successful transmission time of the LTE network increases. Explicit expressions of the optimal initial backoff window sizes to achieve the maximum total throughput under both throughput fairness and 3GPP fairness are also derived, which shed important light on the practical network design. It is found that the initial backoff window size of the LTE network should be enlarged as the mean successful transmission time of the LTE network increases, indicating that for fair and efficient coexistence with a WiFi network, the LTE network needs to access the unlicensed channel infrequently with large packets. To facilitate implementation in practice, distributed schemes are further proposed, with which WiFi and LTE can optimally adjust the backoff window sizes based on their own observation and estimation without the need of coordination between these two networks.

Group paging is a baseline solution proposed by the long-term evolution (LTE) standardization body for supporting machine-to-machine (M2M) communications in the current-generation and the next-generation cellular networks. Yet, in conventional group paging scheme, upon the reception of paging message, all machine-type devices (MTDs) in a group will simultaneously access the base station, leading to severe network congestion and intolerably low access efficiency. To handle this issue, in this article, we propose a dynamic group paging mechanism, where only the MTDs with packets to send will join the contention process, and the collisions in the random access channel are addressed by the contention resolution scheme. Explicit expressions of key performance measures including the mean access delay of each MTD are derived as functions of the length of waiting period (interval between two consecutive paging periods) TW, where a smaller TWindicates a higher frequency of group paging. It is shown that TWis a key system parameter that determines the crucial tradeoff between the signaling overheads of the system during the paging period and the access delay performance of each MTD. To study how to properly tune the waiting period length, a utility-based analytical framework is established by taking the aforementioned tradeoff into consideration. The optimal waiting period length for maximizing the network utility is derived and verified by simulation results. The analysis in this article reveals that the network should increase the group paging frequency as the traffic becomes heavier or the number of preambles decreases. Providing more preambles can indeed improve the delay performance, while the gain becomes marginal if the number of preambles is large.

The machine-to-machine (M2M) communication is an emerging technology that is widely utilized in a vast number of industrial Internet-of-Things (IIoT) applications. Due to the diversity of IIoT applications, provisioning of heterogeneous delay requirements of delay-sensitive machine type devices (MTDs) while optimizing the access efficiency of delay-tolerate MTDs becomes a critical challenge for M2M communications. To address this issue, a multigroup analytical framework for massive random access of M2M communications in IIoT is proposed in this article. Specifically, we consider delay-sensitive MTDs and delay-tolerate MTDs coexist in the network, and those MTDs are divided into multiple groups according to their delay requirements. The access behavior of each MTD is characterized by a double-queue model. Based on this model, the throughput and the mean access delay of each group are characterized. It is found that for each group, the mean access delay decreases as the throughput increases and is minimized when the throughput is maximized. To achieve the maximum throughput of delay-tolerate MTDs under delay constraints of delay-sensitive MTDs, the backoff parameters of delay-sensitive MTDs should be tuned according to the delay constraints while that of delay-tolerate MTDs should be tuned further according to the aggregate packet arrival rate and the number of MTDs in each group. It is further demonstrated that the optimal tuning of backoff parameters is robust against the burstiness of input traffic. The analysis sheds important light on the access design of M2M communications in IIoT with delay constraints.

Aimed at providing high data rate in wireless local area networks (WLANs), the IEEE 802.11ac standard has been developed with key enhancements, including increasing the transmission rate and enlarging the packet payload length. The improvement in the sum rate performance, nevertheless, could become marginal or even disappear when nodes of legacy 802.11a/n standards coexist. It is, therefore, of paramount importance to study how to optimize the network sum rate of a multi-standard WLAN. In this paper, a multi-group model is proposed to analyze the data rate performance of a multi-standard WLAN where nodes with different standards have distinct transmission rates and packet payload lengths. It is shown that the packet payload length is a key system parameter that has a crucial impact on both the network sum rate and the ratio of node data rates. The enhancement proposed in the latest 802.11ac standard on enlarging the packet payload length can improve the data rate performance of its own nodes, but it may lead to the starvation of the legacy 802.11a/n nodes, and even impair the sum rate performance. To maximize the network sum rate with given target ratios of node data rates, the optimal packet payload lengths with or without joint tuning of the initial backoff window sizes are further obtained, which shed important light on the optimal network design of WLANs.

A fundamental difference between the two most representative random-access schemes, Aloha and carrier sense multiple access (CSMA), is sensing. There has been a common belief that the access efficiency can always be improved by the use of carrier sensing, which is indeed based on an implicit assumption that the packet length is much larger than the sensing time. For machine-to-machine (M2M) communications featured with short packets, the benefit of sensing may not overweigh the cost any more. It is therefore of paramount importance to identify the conditions for CSMA to outperform Aloha. In this paper, the sum rate performance of CSMA networks with two representative receiver structures, i.e., the collision model and the capture model, is characterized and optimized, based on which a comparative study of the optimal sum rate performance between Aloha and CSMA is conducted to establish criteria for beneficial sensing. The analysis shows that the maximum sum rates of CSMA with both receiver structures logarithmically increase with the mean received SNR$\rho $at the high SNR region, and the rate gain of the capture model over the collision model is significant only when$\rho $is small. The critical threshold for the ratio of the sensing time to the packet length for beneficial sensing is characterized under various scenarios, and found to be close to zero at the low SNR region when the capture model is adopted, indicating that the packet length needs to be extremely large for CSMA to outperform Aloha in that case. The analysis sheds important light on the access design of M2M communications, and suggests that Aloha could be a more favorable option when short packets are sent by a massive amount of low-power machine-type devices.

The recently released IEEE 802.11ac standard has implemented enhancements including higher maximum transmission rate and larger maximum packet payload length. In wireless local area networks (WLANs) where the latest 802.11ac nodes and the legacy ones coexist, nevertheless, the effects of the enhancements on the data rate performance remain largely unknown. In this paper, we tackle this open problem. The analysis shows that with a growing transmission rate of 802.11ac nodes, the data rates of all the nodes in the network can be improved. If the packet payload length of 802.11ac nodes grows, on the other hand, the data rate of each 802.11ac node increases while the other coexisting nodes' are degraded. To avoid starvation, we further study how to adaptively tune system parameters to optimize the network sum rate under a certain service differentiation requirement among distinct groups. Explicit expressions of the maximum network sum rate and the optimal packet payload lengths are obtained, and verified by simulation results.

In heterogeneous networks (HetNets), the network energy efficiency is critically determined by the base station (BS) deployment density. In this paper, we consider a BS density optimization problem by turning on only a fraction of micro BSs according to an activation ratio to minimize the network average power consumption per area in a 2- tier HetNet. In contrast to previous studies where a BS is assumed to be transmitting packets all the time, such that the network power consumption monotonically increases as the BS density increases, we assume that each BS can be busy or idle depending on the dynamic packet arrivals. The network power consumption is thus closely related to the average traffic intensity of each tier. With the assumption of universal spectrum reuse, the average traffic intensity of each tier is found to be uniquely determined by a set of fixed-point equations, based on which the network average power consumption per area is characterized. Simulation results demonstrate that the network average power consumption per area can be minimized by properly tuning the activation ratio. It is further revealed that the optimal activation ratio increases as the mean packet arrival rate of each user increases.

Simultaneous wireless information and power transfer is an innovative way to provide electrical energy for user equipments. However, most previous works mainly focus on energy harvesting over a relatively narrow frequency range. Due to small energy harvested by the users, the practical applications are usually limited to low power devices. In this paper, an energy-efficient uplink resource allocation problem is investigated in a cellular network with ambient radio frequency (RF) energy harvesting. In order to obtain sufficient energy, a broadband rectenna is adopted to harvest ambient RF energy over six frequency bands at the same time. From the viewpoint of service arrival in the ambient transmitter, a new energy arrival model is presented. The problem of sub-carrier and power allocation is formulated as a mixed-integer nonlinear programming problem. The objective is to maximize the energy efficiency while satisfying the energy causality and the total data rate requirement. In order to reduce the computational complexity, a suboptimal solution is derived by employing a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the QPSO method has higher energy efficiency than a conventional particle swarm optimization approach.

This paper presents the sum rate analysis of a saturated M-group multi-rate carrier sense multiple access network, where nodes in different groups have distinct packet transmission rates. An explicit expression of the network sum rate is derived, based on which the maximum sum rate is obtained by optimizing the transmission probabilities of nodes. It is found that to achieve the maximum sum rate, only the group of nodes with the largest transmission rate is allowed to access the channel, which leads to severe unfairness. To ensure certain fairness, two constraints, namely, throughput fairness (TF) and data-rate fairness (DF), are proposed, with which each node acquires a target proportion of the network throughput and the network sum rate, respectively. Explicit expressions of the network maximum sum rate with TF and DF are derived, which show that by including the fairness constraints, the network maximum sum rate becomes inferior to that without fairness constraints as long as there is difference in the transmission rates of nodes. The analysis is further applied to IEEE 802.11 networks, where the optimal initial backoff window sizes of nodes to achieve the network maximum sum rates with both fairness constraints are derived.

A retry limit is usually adopted in practical carrier sense multiple access (CSMA) networks, where a packet is discarded if the maximum number of retransmission attempts is reached. Despite extensive studies, the effect of retry limit on the performance optimization of CSMA networks has remained largely unknown. This paper focuses on a CSMA network with a finite retry limit M , and aims to address the following open issues. First, for a given retry limit M , how should the backoff parameters be adaptively tuned to achieve the optimal network performance? Second, how does the optimal network performance vary with M ? Specifically, in this paper, the explicit expressions of the network steady-state points, the network throughput, and moments of access delay of successfully transmitted packets are all obtained as the functions of the retry limit M , based on which the optimal network performance is further characterized. It is revealed that a CSMA network with a finite retry limit M may have three steady-state points, and the retry limit M has distinct effects on the throughput and delay performance at these steady-state points. The maximum network throughput is found to be independent of M . Yet to achieve the maximum network throughput, the aggregate input rate and the initial transmission probability of each node should be set according to M in unsaturated and saturated conditions, respectively. To optimize the mean access delay, on the other hand, the initial transmission probability should be carefully selected in saturated conditions. The minimum mean access delay can be greatly reduced by choosing a smaller retry limit M , which, nevertheless, leads to a significant throughput loss. The analysis sheds important light on performance optimization of practical CSMA-based networks such as IEEE 802.11 networks.

Binary Exponential Backoff (BEB) is a key component of the IEEE 802.11 DCF protocol. It has been shown that BEB can achieve the theoretical limit of throughput as long as the initial backoff window size is properly selected. It, however, suffers from significant delay degradation when the network becomes saturated. It is thus of special interest for us to further design backoff schemes for IEEE 802.11 DCF networks that can achieve comparable throughput as BEB, but provide better delay performance. This paper presents a systematic study on the effect of backoff schemes on throughput and delay performance of saturated IEEE 802.11 DCF networks. In particular, a backoff scheme is defined as a sequence of backoff window sizes {Wi}. The analysis shows that a saturated IEEE 802.11 DCF network has a single steady-state operating point as long as {Wi} is a monotonic increasing sequence. The maximum throughput is found to be independent of {Wi}, yet the growth rate of {Wi} determines a fundamental tradeoff between throughput and delay performance. For illustration, Polynomial Backoff is proposed, and the effect of polynomial power x on the network performance is characterized. It is demonstrated that Polynomial Backoff with a larger x is more robust against the fluctuation of the network size, but in the meanwhile suffers from a larger second moment of access delay. Quadratic Backoff (QB), i.e., Polynomial Backoff with x=2, stands out to be a favorable option as it strikes a good balance between throughput and delay performance. The comparative study between QB and BEB confirms that QB well preserves the robust nature of BEB and achieves much better queueing performance than BEB.

Enhanced distributed channel access (EDCA) is an extension of the distributed coordination function to support quality-of-service for IEEE 802.11 wireless local area networks. By assigning distinct backoff parameters to each access category (AC), differentiated throughput performance can be achieved when the network is saturated. Although it has been long observed that the network throughput with the current EDCA standard setting may significantly degrade as the network size grows, how to properly tune the backoff parameters to optimize the network throughput under a certain differentiation requirement remains largely unknown. In this paper, a new analytical model is proposed to address this open issue. Specifically, we focus on an M-AC IEEE 802.11e EDCA network where nodes in the same AC have identical backoff parameters, including the initial backoff window sizeW(g), the cutoff phaseK(g), and the arbitration interframe spaces (AIFS) numberA(g), g = 1, . . . , M. The network steady-state operating point in saturated conditions, i.e., pA, is characterized by using the steady-state probability of successful transmission of head-of-line (HOL) packets given that the channel is idle, based on which explicit expressions of node throughput and network throughput are further obtained. For given target ratios of node throughput of ACs, the optimal initial backoff window sizes and AIFS numbers to maximize the network throughput are derived and verified by simulation results. The analysis reveals that the maximum network throughput is solely determined by the holding time of HOL packets in successful transmission and collision states. To achieve the maximum network throughput, the initial backoff window size of each AC should be linearly increased with the network size. In the meantime, the increasing rate of the initial backoff window size, or the AIFS number, of each AC should be also carefully set according to the target ratios of node throughput. Although the maximum network throughput with pre-specified target ratios of node throughput of ACs can be achieved in both ways, the backoff window size differentiation could be a more preferable option as it requires fewer tuning parameters and provides better precision than the AIFS differentiation.

A key open question in IEEE 802.11e networks with enhanced distributed channel access (EDCA) is how to properly tune the backoff parameters to optimize the network throughput performance under a certain differentiation requirement. To tackle this problem, a new analytical model for IEEE 802.11e EDCA networks is proposed in this paper, based on which the maximum network throughput is derived as an explicit function of the holding times of head-of-line (HOL) packets in successful transmission and collision states. The optimal initial backoff window sizes to achieve the maximum network throughput under pre-specified target node-throughput ratios are also obtained, and verified by simulation results.

In this paper, a unified analytical framework is established to study the stability, throughput, and delay performance of homogeneous buffered IEEE 802.11 networks with Distributed Coordination Function (DCF). Two steady-state operating points are characterized using the limiting probability of successful transmission of Head-of-Line (HOL) packets $(p)$ given that the network is in unsaturated or saturated conditions. The analysis shows that a buffered IEEE 802.11 DCF network operates at the desired stable point $(p=p_{L})$ if it is unsaturated. $(p_{L})$ does not vary with backoff parameters, and a stable throughput can be always achieved at $(p_{L})$. If the network becomes saturated, in contrast, it operates at the undesired stable point $(p=p_{A})$, and a stable throughput can be achieved at $(p_A)$ if and only if the backoff parameters are properly selected. The stable regions of the backoff factor $(q)$ and the initial backoff window size $(W)$ are derived, and illustrated in cases of the basic access mechanism and the request-to-send/clear-to-send (RTS/CTS) mechanism. It is shown that the stable regions are significantly enlarged with the RTS/CTS mechanism, indicating that networks in the RTS/CTS mode are much more robust. Nevertheless, the delay analysis further reveals that lower access delay is incurred in the basic access mode for unsaturated networks. If the network becomes saturated, the delay performance deteriorates regardless of which mode is chosen. Both the first and the second moments of access delay at $(p_A)$ are sensitive to the backoff parameters, and shown to be effectively reduced by enlarging the initial backoff window size $(W)$.

This paper presents the throughput analysis of an M-group heterogeneous IEEE 802.11 DCF network where nodes in different groups have distinct input rates and initial backoff window sizes. An explicit expression of the network steady-state operating point is obtained based on the fixed-point equation of the limiting probability of successful transmission of Head-of-Line (HOL) packets given that the channel is idle, which is shown to be closely dependent on the backoff parameters of saturated groups and the input rates of unsaturated groups. Both the network throughput and the group throughput performance are further characterized, and the maximum network throughput is derived as an explicit function of the holding times of HOL packets in successful transmission and collision states. The analysis reveals that to achieve the maximum network throughput, the optimal set of input rates of unsaturated groups and initial backoff window sizes of saturated groups should satisfy a constraint that is determined by the group sizes of saturated groups. Given the input rates of unsaturated groups, for instance, the initial backoff window sizes of saturated groups should linearly increase with their group sizes, and those with higher increasing rates achieve lower group throughput.

The multimedia streams over the wireless access networks has increased dramatically over the past few years. Since the wireless link is featured with restricted bandwidth and limited resources, increasing demand of real-time applications imposes challenges on wireless networks to provide Quality of Service (QoS). Plenty of schemes were proposed to provide QoS, among which the admission control is a typical method. However, admission control mechanisms only consider the case at the network entering phase. After the admission, another mechanism is needed to continue to monitor the ongoing traffic and to react to fluctuation of resource consumption during the connection. Therefore, other than only using admission control schemes, which try to restrict access to the network, schemes to control on-going best-effort background are proposed in this paper. The proposed dynamical halting and resuming schemes of background traffic provide a finer way to allocate resources for video streams. It is shown via extensive simulations that proposed schemes can provide direct and solid enhancement of Quality-of-Experience (QoE) for video subscribers, and achieve good tradeoff between video quality and bandwidth utilization.