In this paper, we propose a novel framework for communication efficient distributed learning, FedBDQB, by employing bidirectional dynamic quantization for model updates and bitmap-based lossless compression, and provide its descending analysis and convergence proof. The performance of FedBDQB is evaluated under two representative use cases of distributed learning, federated learning and blockchain-enabled federated learning, comparing with the classic FedAvg and two state-of-the-art methods FedDQ and TinyOFL across various experimental scenarios using multiple widely-adopted datasets (MNIST, CIFAR-10, and CIFAR-100) with both IID and non-IID data partitioning. Experimental results demonstrate that with the case of legacy federated learning, our proposed FedBDQB can significantly reduce the data communication cost over the baselines, especially when the number of clients or the size of the trained model is enlarged, with a negligible decrease in model accuracy and an acceptable increase in computational time. With the case of resource-consuming blockchain-enabled federated learning, FedBDQB can profoundly decrease the resource consumption including computation, traffic volume and storage and improve the consensus efficiency, which provides a promising solution for the practical applicability of blockchain-based distributed learning systems.

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm in which a large number of specialized AI agents collaborate to perform autonomous decisions, dynamic environmental adaptation, and complex missions. AgentNet has the potential to facilitate real-time network management and optimization functions, including self-configuration, self-optimization, and self-adaptation across diverse and complex environments, laying the foundation for fully autonomous networking systems. Despite its promise, AgentNet is still in the early stages of development and still lacks an effective networking framework to support automatic goal discovery, multi-agent self-orchestration, and task assignment. This paper proposes SANet, a novel semantic-aware AgentNet architecture for wireless networks. SANet can infer the semantic goal of the user and automatically assign agents associated with different layers of the network stack to fulfill the inferred goal. Motivated by the fact that AgentNet is a decentralized framework in which collaborating agents may generally have different and even conflicting objectives, we formulate the decentralized optimization of SANet as a multi-agent multi-objective problem, and focus on finding the Pareto-optimal solution for agents with distinct and potentially conflicting objectives. We propose three novel metrics for evaluating SANet: (the agents' objective) optimization error, (dynamic environment) generalization error, and (multi-objective) conflicting error. Furthermore, we develop a model partition and sharing (MoPS) framework in which large models, e.g., deep learning models, of different agents can be partitioned into shared and agent-specific parts that are jointly constructed and deployed according to agents' local computational resources. Two decentralized optimization algorithms, static-weighting and dynamic-weighting algorithms, are introduced to optimize the above three metrics. A bandwidth-adaptive compression framework is also proposed to enable different agents to perform in situ compression of their intermediate embeddings, dynamically adjusting to localized resource constraints and task requirements. We derive theoretical bounds for all these performance metrics and prove that there exists a three-way tradeoff among optimization, generalization, and conflicting errors. Finally, to validate our theoretical results, we develop an open-source Radio Access Network (RAN) and core network-based hardware prototype that implements three Transformer-based time-series prediction agents to interact with three different layers of the network. Experimental results show that the proposed MoPS framework achieves performance gains of up to$14.61\%$while requiring only$44.37\%$of the Floating-Point Operations (FLOPs) for inference at each agent compared to state-of-the-art algorithms. Also, compared to the static-weighting algorithm, the dynamic-weighting algorithm achieves up to$83.81\%$reduction in training errors caused by conflicting objectives.

Agentic AI networking (AgentNet) has attracted significant interest due to its promising potential to move traditional AI-based networking solutions beyond closed-loop and passive learning to proactive interaction and goal-driven action, offering a path to self-learning and generally intelligent networking systems. Despite its promise, ensuring the security and trustworthiness of such systems presents significant challenges, particularly concerning identity management, agent capability verification, and data integrity during collaborative learning. To address these issues, this paper proposes TrustAgentNet, a novel consortium blockchain-based framework for unified and trusted agent identification, traceable skillset and tag descriptions, and secure on-chain collaborative learning in AgentNet. In TrustAgentNet, a chain of skillset (CoS) is introduced, consisting of a skillset chain to distributedly store all the verified skillsets and associated tags, and a dedicated training chain for each distinct skillset can be jointly constructed and maintained by the authorized agents using a collaborative learning-based approach. Theoretical analysis suggests that there exists a three-way trade-off among the security level, skillset performance, and resource cost. This tradeoff is also empirically validated by the experimental results obtained from a hardware prototype implemented based on a Hyperledger Fabric-based consortium blockchain. To verify the practical performance of TrustAgentNet, we consider a real-world scenario of multi-agent collaborative learning under malicious attack. Experimental results suggest that TrustAgentNet can effectively guarantee the security of skillset training and enable rapid response and recovery from potential attacks within seconds.

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm that relies on a large number of specialized AI agents to collaborate and coordinate for autonomous decision-making, dynamic environmental adaptation, and complex goal achievement. It has the potential to facilitate real-time network management alongside capabilities for self-configuration, self-optimization, and self-adaptation across diverse and complex networking environments, laying the foundation for fully autonomous networking systems in the future. Despite its promise, AgentNet is still in the early stage of development, and there still lacks an effective networking framework to support automatic goal discovery and multi-agent self-orchestration and task assignment. This paper proposes SANNet, a novel semantic-aware agentic AI networking architecture that can infer the semantic goal of the user and automatically assign agents associated with different layers of a mobile system to fulfill the inferred goal. Motivated by the fact that one of the major challenges in AgentNet is that different agents may have different and even conflicting objectives when collaborating for certain goals, we introduce a dynamic weighting-based conflict-resolving mechanism to address this issue. We prove that SANNet can provide theoretical guarantee in both conflict-resolving and model generalization performance for multi-agent collaboration in dynamic environment. We develop a hardware prototype of SANNet based on the open RAN and 5GS core platform. Our experimental results show that SANNet can significantly improve the performance of multi-agent networking systems, even when agents with conflicting objectives are selected to collaborate for the same goal.

In this paper, we propose a novel framework for communication efficient federated learning, FedBDQB, by employing bidirectional dynamic quantization for model updates and bitmap-based lossless compression, and provide its descending analysis and convergence proof. The performance of FedBDQB is evaluated and compared against the classic FedAvg and a state-of-the-art method FedDQ across various experimental scenarios, including two widely-adopted datasets MNIST and CIFAR-10, as well as both IID and non-IID data partitioning. Experimental results demonstrate that our proposed FedBDQB can significantly reduce the data communication volume, achieving compression rates of up to 24.21 and 12.87 times over FedAvg and FedDQ, respectively. The substantial improvement in communication efficiency is attained with a negligible decrease in model accuracy (with the maximum error remaining below 1.5% across all experiments) and an acceptable increase in computational time (less than two times over FedAvg and FedDQ across all experiments).

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.

As a groundbreaking feature in IEEE 802.11be, multi-link operation (MLO) is expected to support emerging applications that are strongly delay-sensitive. A key to the effective use of MLO for such cases rests on the optimal allocation of application traffic across multiple links. Our initial simulation experiments in ns-3 reveal that the proposed traffic allocation policies in prior art are significantly sub-optimal in terms of achievable delay performance of multi-link devices (MLDs), particularly in the presence of legacy single-link devices (SLDs). In this work, we first develop a new analytical model for the mean end-to-end (E2E) delay, delay jitter and worst-case percentile latency performance of MLD-SLD-coexisting Wi-Fi 7 networks (largely unexplored to date) with saturated and unsaturated SLD traffic. Subsequently, the optimal traffic allocation strategies for minimizing the mean E2E delay and delay jitter are obtained and validated by ns-3 simulation results. It is shown that with the optimal policy, MLDs can achieve significantly better mean E2E delay, delay jitter, worst-case latency and delay cumulative distribution function (CDF) compared to existing solutions.

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.

To address the limitations in the wireless communication courses in our university including lack of in-class hours, practical experiences, integration with latest development and discussions on latency, we have developed and implemented a comprehensive research-oriented online laboratory since 2021, focusing on 5G-V2X Latency measurements, modeling and optimization in our campus environment. By incorporating recent research outcomes of our faculty members, we design four progressive and game-like task modules, all utilizing self-developed open-source software/algorithms and students' own laptops so as to support the scalability and accessibility. The curriculum and lesson planning of this laboratory follows the flipped-classroom model and BOPPPS model, respectively, to create an effective and student-oriented learning environment. After two rounds of practice, fruitful outcomes are obtained including dataset collection and algorithm design, as well as positive feedback from students particularly regarding their growing interest and self-motivation in the experiment contents and their satisfaction with the teaching and mentoring provided.

This paper reports a reform scheme for project-based courses of an engineering-practicing-oriented special class in our university, aiming at engaging undergraduate students in research and development via a project-based teaching/learning model. To address a number of key challenges in effective curriculum design, fair and comprehensive evaluation and stimulating students' innovative thinking for the current courses, we are working on formulating a two-year pipeline of progressive project-based courses, redesigning the course curriculum with novel contents, establishing a process-oriented and multi-dimensional course grading scheme, and implementing proposal-midterm-final defense stages.

In this paper, we propose a multi-agent proximal policy optimization (MAPPO) based algorithm, PPO-DMA, with centralized training and decentralized execution (CTDE) framework for the sensing-free decentralized multiple access problem. To maximize the total network throughput while guaranteeing fair access among devices in a distributed manner, a single reward function for each node is introduced. Extensive simulation experiments are conducted to show that the proposed PPO-DMA scheme: 1) significantly outperforms the theoretical upper bound of slotted-Aloha with satisfactory fairness performance among nodes; 2) shows greater stability and robustness compared to independent deep Q network (IDQN) algorithm with decentralized training and decentralized execution (DTDE) framework; 3) can harmoniously coexist with Aloha-based nodes.

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.

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.

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.

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 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.

With the emerging unlicensed spectrum sharing and Machine-to-Machine communications, the coexistence performance of multiple devices with different access schemes operating at the same unlicensed bands has received significant research interests. As the two most representative random-access schemes, Aloha and Carrier Sense Multiple Access (CSMA) both found wide applications in unlicensed bands. Yet most of the studies have focused on the coexistence of CSMA-based networks, leaving the coexistence of Aloha-based and CSMA-based networks largely unexplored. The challenge originates from the lack of a coexistence model of slotted Aloha and CSMA. In this paper, the throughput performance of coexisting Aloha and CSMA networks is characterized and optimized by extending a unified analytical framework proposed for random-access networks. The analysis shows that different from the single-network case where the maximum throughput of CSMA is much higher thanks to carrier sensing, when Aloha and CSMA coexist, Aloha would significantly outperform CSMA if each network optimizes its own throughput performance without cooperation, leading to poor throughput performance for CSMA and severe unfairness. To achieve fair coexistence, inter-network cooperation is crucial. The optimal transmission probabilities of Aloha and CSMA for maximizing the total network throughput under a given throughput ratio are further derived, and applied to coexisting LTE Unlicensed and WiFi networks to optimize their coexistence performance.

In this paper, we propose an efficient human pose estimation network-SFM (slender fusion model) by fusing multi-level features and adding lightweight attention blocks-HSA (High-Level Spatial Attention). Many existing methods on efficient network have already taken feature fusion into consideration, which largely boosts the performance. However, its performance is far inferior to large network such as ResNet and HRNet due to its limited fusion operation in the network. Specifically, we expand the number of fusion operation by building bridges between two pyramid frameworks without adding layers. Meanwhile, to capture long-range dependency, we propose a lightweight attention block-HSA, which computes second-order attention map. In summary, SFM maximizes the number of feature fusion in a limited number of layers. HSA learns high precise spatial information by computing the attention of spatial attention map. With the help of SFM and HSA, our network is able to generate multi-level feature and extract precise global spatial information with little computing resource. Thus, our method achieve comparable or even better accuracy with less parameters and computational cost. Our SFM achieve 89.0 in [email protected], 42.0 in [email protected] on MPII validation set and 71.7 in AP, 90.7 in [email protected] on COCO validation with only 1.7G FLOPs and 1.5M parameters. The source code will be public soon.

In this paper, we study the throughput optimization problem of a CSMA network with imperfect sensing by considering both miss detection errors and false alarm errors. The analysis shows that the maximum network throughput of CSMA significantly drops as the miss detection probability increases, while is less sensitive to the false alarm probability and the number of nodes. To achieve the maximum throughput, the transmission probability of each node should decrease with the miss detection probability or the number of nodes, while increase with the false alarm probability. The maximum network throughput and the optimal transmission probability are both obtained and verified by simulation results.

LTE Licensed Assisted Access (LTE-LAA) is a promising solution for harmonious coexistence with WiFi in unlicensed spectrum. Although LTE-LAA employs a listen-before-talk approach similar to the distributed coordination function in IEEE 802.11, it uses different parameters and varying transmission durations. As a result, achieving fair coexistence between LTE-LAA and WiFi (by any definition) remains an open question, which in a pragmatic framework, devolves to: how LTE-LAA should select its parameters. To address this issue, a multi-group model is proposed for LTE-LAA and WiFi coexistence, as a function of respective initial backoff window sizes, sensing durations, maximum backoff stages, retry limits and transmission opportunities. The network steady-state point in saturated conditions is obtained, based on which the node airtime and total network airtime are derived as functions of system parameters of LTE-LAA and WiFi networks. The analysis shows that LTE-LAA can maintain proportional fairness with WiFi by either tuning its initial backoff window size or sensing duration. In particular, the optimal initial backoff window size and number of sensing slots of LTE-LAA are both derived and verified by simulation. The significance of our analysis is two-fold: a) it exposes the result that the current standard-proposed parameter settings will not generically achieve fairness and thereafter b) suggests optimal settings whereby LTE-LAA and WiFi nodes can achieve equal per-node airtime. It is further revealed that the initial backoff window size tuning of LTE-LAA could be a preferable option for achieving fairness between LTE-LAA and WiFi in practical scenarios as it requires less system information and achieves better precision.

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.

Different from the conventional packet-based random access schemes where each data packet needs to contend for channel access, with connection-based random access, a connection is first established before data packet transmission. Despite the consensus that there exists a critical threshold of the data packet transmission time, only above which establishing a connection is beneficial, characterization of such a threshold has received little attention. In this paper, a comparative study will be presented on the optimal throughput performance of the packet-based random access and the connection-based random access to characterize criteria for beneficial connection establishment. Based on a unified channel-centric model, explicit expressions of the maximum effective throughput are obtained for both packet-based and connection-based Aloha and Carrier Sense Multiple Access (CSMA). The analysis shows that whether connection establishment is beneficial crucially depends on the sensing capability of nodes. The threshold of data packet transmission time with Aloha is found to be much lower than that with CSMA, indicating that the throughput gain brought by connection establishment is more significant when sensing is absent. The analysis sheds important light on the access design of machine-to-machine (M2M) communications.

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.

For IEEE 802.11 networks with multiple basic service sets (BSSs), most studies have focused on how to allocate different frequency sub-channels to BSSs for minimizing the co-channel interference. With the significant increase of the sub-channel bandwidth, however, it becomes increasingly important to study the network performance with universal frequency reuse. In this paper, we focus on an uplink M-BSS IEEE 802.11 network, where all the BSSs share the frequency band rather than operate at different sub-channels. By dividing the nodes in each BSS into multiple groups according to the set of access points (APs) they can be heard by, the steady-state points of M BSSs in saturated conditions are obtained as the functions of the number of nodes in each group and the initial backoff window size of nodes of each BSS. The maximum network throughput is further characterized by optimally choosing the initial backoff window sizes of all the nodes and shown to be closely dependent on the percentage of nodes that can be heard by multiple APs. The comparison with orthogonal frequency division reveals that although the maximum network throughput is degraded due to interference among BSSs, a higher network data rate can still be achieved by universal frequency reuse, which makes it a preferable option for multi-BSS IEEE 802.11 networks.

Due to the rapid growth of real-time applications in wireless local area networks (WLANs), quality-of-service (QoS) guarantee becomes one of the key issues for IEEE 802.11e enhanced distributed channel access (EDCA) networks. In contrast to most existing studies which only focus on providing delay guarantee to real-time flows, in this paper we study the open question of how to adaptively tune system parameters to maximize the aggregate throughput of non-real-time flows with a certain mean access delay constraint on real-time flows for saturated IEEE 802.11e EDCA networks. Explicit expressions of the maximum aggregate throughput of non-real-time flows and the optimal initial backoff window sizes are obtained, and verified by simulation results. The analysis shows that for a given mean access delay constraint, the maximum aggregate throughput of non-real-time flows declines as the number of real-time nodes grows. It drops to zero when the number of realtime nodes exceeds a critical threshold, indicating that the delay requirement cannot be satisfied. An admission control scheme is further proposed, where the maximum number of real-time nodes that can be enrolled is derived as a linearly increasing function of the mean access delay constraint.

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.

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.