In multi-platform mobile crowdsensing (MCS), platforms attract mobile workers to participate in sensing tasks and collect data through incentive mechanisms to provide data-driven services. However, existing studies often focus exclusively on either competition or cooperation mechanisms between platforms, overlooking scenarios where both coexist. Additionally, most studies prioritize optimizing social welfare or market fairness, neglecting the core role of platforms as data service providers in a free market and the impact of their dominant market position, which limits the utility of platform benefit optimization. In addition, the issue of privacy protection has not received sufficient attention. To this end, this paper proposes a two-stage hybrid mechanism (TS-HM) with the goal of maximizing the utility of the platform, including a decentralized multi-agent reinforcement learning pricing mechanism (DC-PM) and a two-substage cooperative matching mechanism (CMM). In the first stage, the DC-PM mechanism is used to help the platform learn the optimal pricing strategy under privacy-preserving conditions by modeling the pricing and worker contribution problem as a multi-leader-multi-follower stackelberg game; and in the second stage, the CMM mechanism is used to guarantee the matching stability and further enhance the platform utility. Experimental simulations demonstrate that the DC-PM mechanism effectively achieves rapid convergence of pricing strategies while ensuring privacy protection, and the CMM mechanism excels in matching stability and platform performance improvement. In general, the TS-HM mechanism outperforms existing approaches by increasing the total utility of the platform by an average of approximately 12. 65%, significantly increasing the effectiveness of the platform and showcasing its strong advantages.

In the context of smart cities, autonomous vehicles, such as unmanned delivery vehicles and taxis are gradually gaining acceptance. However, their application scenarios remain significantly fragmented. Typically, an Autonomous Multi-Functional Vehicle (AMFV) is not engaged in other scenarios when idle in a specific one. Currently, a unified system capable of coordinating and using these resources efficiently is lacking. Moreover, there is an absence of an advanced navigation algorithm for facilitating coordinated navigation among Heterogeneous Vehicles (HVs). To address these issues, we propose the LLM-driven Multi-vehicle Dispatching and navigation (LiMeda) framework. It comprises an LLM-driven scheduling module that facilitates efficient allocation considering task scenarios and vehicle information, which addresses the issue of incompatible vehicle resources across various smart city scenarios. And the other is a navigation module, founded on the Heterogeneous Agent Reinforcement Learning (HARL) framework we previously proposed, which can effectively perform cooperative navigation tasks among heterogeneous agents, assisting the cooperative task completion by HVs in a smart city. Experimental results show our method outperforms both traditional scheduling algorithms and Reinforcement Learning navigation algorithms in metric terms. Additionally, it shows remarkable scalability and generalization under varying city scales, vehicle numbers, and task numbers.

The socially-aware navigation system has evolved to adeptly avoid various obstacles while performing multiple tasks, such as point-to-point navigation, human-following, and -guiding. However, a prominent gap persists: in Human-Robot Interaction (HRI), the procedure of communicating commands to robots demands intricate mathematical formulations. Furthermore, the transition between tasks does not quite possess the intuitive control and user-centric interactivity that one would desire. In this work, we propose an LLM-driven interactive multimodal multitask robot navigation framework, termed LIM2N, to solve the above new challenge in the navigation field. We achieve this by first introducing a multimodal interaction framework where language and hand-drawn inputs can serve as navigation constraints and control objectives. Next, a reinforcement learning agent is built to handle multiple tasks with the received information. Crucially, LIM2N creates smooth cooperation among the reasoning of multimodal input, multitask planning, and adaptation and processing of the intelligent sensing modules in the complicated system. Detailed experiments are conducted in both simulation and the real world demonstrating that LIM2N has solid user needs understanding, alongside an enhanced interactive experience.

In mobile crowd sensing (MCS), complex tasks often require collaboration among multiple workers with diverse expertise and sensors. However, few studies consider the sensing time redundancy of multiple workers to complete a task collaboratively, and the subjective and objective collaboration willingness of participating workers in forming collaboration groups for different tasks. If solely focusing on enhancing workers’ willingness to collaborate, it cannot guarantee the minimum time redundancy within the collaboration group, resulting in a decrease in the group’s efficiency. Similarly, if only aiming to reduce sensing time redundancy among the workers in the collaboration group, it may lead to a loss of workers’ willingness to collaborate, and the diminished motivation among workers will consequently reduce the group’s efficiency. To address these challenges, this paper proposes EGC-STRO, a method for forming efficient collaboration groups in MCS that optimizes sensing time redundancy while balancing the workers’ cooperation willingness as constraints. First, this method proposes an evaluation indicator to select workers who meet their reward expectations, i.e., objective collaboration willingness, and uses an incentive mechanism based on bargaining game to maximize the overall interests. Furthermore, subjective collaboration willingness is defined and a collaboration worker selection algorithm is designed. The algorithm adds workers who meet both subjective and objective willingness requirements to the candidate set and selects workers with the smallest sensing redundancy time in the worker candidate set to join the final collaboration group. Simulation results demonstrate that compared with the baseline methods, our proposed EGC-STRO increases the worker engagement by about 5%-20%, increases the task coverage by 6%-25%, increases the platform utility by 17%-50%, and increases the worker utility by 20%-60%.

Mobile crowd sensing (MCS) has attracted extensive attention as a promising method for environmental sensing and data collection. However, due to the increasing computational tasks, bandwidth, and computing pressure, traditional “Cloud-Edge–End” MCS is insufficient to efficiently offload sensing data in real-time for data aggregation. Therefore, we consider a novel MCS framework based on the “Cloud-Enhanced-Edge–End,” the framework uses MCS idle users as edge nodes (ENs) to assist edge servers to enhance computing power and reduce delay and energy consumption. To achieve efficient data aggregation in the “Cloud-Enhanced-Edge–End” MCS framework, we first consider the multiobjective optimization of delay and energy consumption to establish a utility function. Second, addressing the shortcomings of traditional optimization algorithms, which often struggle with complex decision spaces and dynamic environments, we propose a MCS offloading (MCSOL) algorithm based on deep Q-network (DQN). The algorithm uses reinforcement learning to adaptively offload computing tasks to the optimal ENs or servers to maximize utility function to reduce delay and energy consumption. Our experimental results reveal that, in comparison with the other five strategies like only offloading data to base stations and ENs, MCSOL enhances data aggregation performance within the range of 30%–60%.

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.

Image distortion during wireless transmission presents a significant challenge for real-world artificial intelligence (AI) applications. Recent methods have attempted to address this issue by integrating neural networks into the wireless transmission system. However, these approaches often require a large volume of labeled training data, which can be expensive and time-consuming to collect. To address this issue, we propose a novel approach,Self-SupervisedMulti-AgentFeedbackEnabledNeuralNetworks (S2MAFENN). S2MAFENN is designed to improve the efficiency of labeled data in wireless image transmission. It incorporates a Feedbacker agent that emulates the error correction mechanisms observed in primate brains and employs self-supervised contrastive learning to extract representations from unlabeled distorted images independently. From a theoretical perspective, we model the training process of S2MAFENN as a three-player Stackelberg game and provide evidence that S2MAFENN can achieve exponential convergence rates. We then empirically validate our approach by assessing the representations learned through S2MAFENN. We use varied labeled CIFAR10 and CIFAR100 data to simulate real image transmissions over the Rayleigh fading and 5G channels. Our results show that S2MAFENN matches or even surpasses the performance of state-of-the-art self-supervised training methods, even when only 50% of labels are used. Moreover, S2MAFENN yields average accuracy gains of 5.11%, 5.8%, and 4.58% with only 0.1, 0.2, and 0.5 of the labels transmitted over the 5G channel, respectively. For the downstream task of semantic segmentation over the 5G channel, S2MAFENN exhibits significant advancements on the ADE20K dataset. It achieves enhancements of approximately 7% and 8.7% in Mean IoU and DICE metrics, respectively, surpassing the performance of current state-of-the-art methods.

Text-based speech editing systems are developed to enable users to modify speech based on the transcript. Existing state-of-the-art editing systems based on neural networks do partial inferences with no exception, that is, only generate new words that need to be replaced or inserted. This manner usually leads to the prosody of the edited part being inconsistent with the surrounding speech and a failure to handle the alteration of intonation. To address these problems, we propose a cross-utterance conditioned coherent speech editing system, that first does the entire reasoning at the inference time. Our proposed system can generate speech by utilizing speaker information, context, acoustic features, and the mel-spectrogram from the original audio. Experiments conducted on subjective and objective metrics demonstrate that our approach outperforms the baseline on various editing operations regarding naturalness and prosody consistency.

In mobile crowd sensing (MCS), most of the research work does not consider the dropout situation of the workers, resulting in a lower completion rate of the tasks. To decrease the performance loss caused by the dropout of workers, one solution is to prevent workers from dropping out in advance. However, this solution cannot be widely applied to some emergent scenarios and avoid dropouts strictly. In this paper, we consider another solution to respond to worker dropouts actively and innovatively propose a two-stage incentive framework, namely the Game and Reverse Auction based Incentive Mechanism (GRAIM), which aims at motivating dropout workers to submit perceived data and effectively recruiting high-quality idle workers to complete all unfinished tasks, including the tasks left behind by the dropout workers and other incomplete tasks. In the first stage, a bargaining game-based reward allocation method (BGRA) is proposed to incentivize the perceived data submission of dropout workers with a reward equilibrium between the platform and the dropout workers. In the second stage, worker recruitment is modeled as a multi-armed bandits (MAB) issue in a reverse auction, and the extended upper confidence bound (EUCB) algorithm is proposed for the platform to recruit high-quality idle workers to perform unfinished tasks. The experimental results show that compared with the state-of-art mechanisms, our proposed GRAIM increases social welfare by about 15%~ 23%, reduces average reward by about 16% ~ 25%, and increases task completion rates by about 8% ~ 20%.

Yang Li, Cheng Yu, Guangzhi Sun, Hua Jiang, Fanglei Sun, Weiqin Zu, Ying Wen, Yang Yang, Jun Wang. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Music generation, as a creativity problem, attracts growing attention from artificial intelligence researchers. Among the challenging tasks, lyrics-conditional melody generation aims to leverage natural language processing (NLP) techniques to generate music from texts, for which Generative Adversarial Networks (GAN) has become a promising unsupervised solution. The adversarial training of two agents, i.e., generator and discriminator, allows GAN to achieve a better generation performance and has been proven effective in conditional generation tasks. In this paper, we propose the multi-criteria relational GAN (MRGAN), which includes a relation memory-based generator and two discriminators with a unique discrimination criterion each. The relational memory in the generator is adopted for long-time dependency modeling. Meanwhile, the two discriminators can judge both musical quality and conditional correspondence. Based on the bilingual evaluation understudy (BLEU) score, a new metric, named Music-BLEU, has also be designed to evaluate the lyrics-conditional melody generation. Experimental results verify that MRGAN outperforms existing approaches in related key metrics.

Numerical Partial Differential Equation (PDE) solvers often require discretizing the physical domain by using a mesh. Mesh movement methods provide the capability to improve the accuracy of the numerical solution without introducing extra computational burden to the PDE solver, by increasing mesh resolution where the solution is not well-resolved, whilst reducing unnecessary resolution elsewhere. However, sophisticated mesh movement methods, such as the Monge-Ampère method, generally require the solution of auxiliary equations. These solutions can be extremely expensive to compute when the mesh needs to be adapted frequently. In this paper, we propose to the best of our knowledge the first learning-based end-to-end mesh movement framework for PDE solvers. Key requirements of learning-based mesh movement methods are: alleviating mesh tangling, boundary consistency, and generalization to mesh with different resolutions. To achieve these goals, we introduce the neural spline model and the graph attention network (GAT) into our models respectively. While the Neural-Spline based model provides more flexibility for large mesh deformation, the GAT based model can handle domains with more complicated shapes and is better at performing delicate local deformation. We validate our methods on stationary and time-dependent, linear and non-linear equations, as well as regularly and irregularly shaped domains. Compared to the traditional Monge-Ampère method, our approach can greatly accelerate the mesh adaptation process by three to four orders of magnitude, whilst achieving comparable numerical error reduction.

In the intelligent communication field, deep learning (DL) has attracted much attention due to its strong fitting ability and data-driven learning capability. Compared with the typical DL feedforward network structures, an enhancement structure with direct data feedback have been studied and proved to have better performance than the feedfoward networks. However, due to the above simple feedback methods lack sufficient analysis and learning ability on the feedback data, it is inadequate to deal with more complicated nonlinear systems and therefore the performance is limited for further improvement. In this paper, a novel multi-agent feedback enabled neural network (MAFENN) framework is proposed, consisting of three fully cooperative intelligent agents, which make the framework have stronger feedback learning capabilities and more intelligence on feature abstraction, denoising or generation, etc. Furthermore, the MAFENN frame work is theoretically formulated into a three-player Feedback Stackelberg game, and the game is proved to converge to the Feedback Stackelberg equilibrium. The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback. To verify the MAFENN framework’s feasibility in wireless communications, a multi-agent MAFENN based equalizer (MAFENN-E) is developed for wireless fading channels with inter-symbol interference (ISI). Experimental results show that when the quadrature phase-shift keying (QPSK) modulation scheme is adopted, the SER performance of our proposed method outperforms that of the traditional equalizers by about 2 dB in linear channels. When in nonlinear channels, the SER performance of our proposed method outperforms that of either traditional or DL based equalizers more significantly, which shows the effectiveness and robustness of our proposal in the complex channel environment.

In 5G massive multiple input multiple output (MIMO) system, the main challenges to mitigate inter-cell interference (ICI) are overhead of information exchange and computational complexity. In this paper, we propose an interference approximation method based on spatial features, which can cover the major channel information by low overhead. And based on this method, a novel distributed low-complexity interference coordination algorithm called SFDIC is proposed, which is based on the idea of leader-follower game to avoid strong ICI. The experimental results show that the proposed interference approximation method is strongly consistent with the traditional channel matrix based interference calculation method on the trend, whose correlation coefficient is 0.9098 and Kullback-Leibler (KL) divergence is close to 0. In low and medium-speed scenarios, the SFDIC increases system and edge throughput by more than 20% and 107% than joint space division multiplexing (JSDM) respectively. And these scenarios reduce the sharing overhead by more than 50% simultaneously. In addition, the new channel predicted module based on Koopman operator is incorporated to improve practical feasibility and system performance loss causing by delay for the first time.

Feedback mechanism has been widely used in wireless communication such as channel equalization and resource allocation. In recent years, deep learning (DL) has made great progress in the field of wireless communication. There is now some work that attempts to introduce plain feedback mechanisms into DL algorithm to solve wireless communication problems. However, the improvement of plain feedback DL methods is limited in complex situations due to those methods lack sufficient learning ability on feedback information. In this paper, we propose a Multi-Agent Feedback Enabled Neural Network (MAFENN) equalizer, which consists of a specific learnable feedback agent and two feed-forward agents. Three fully cooperative intelligent agents help the system improve the ability to remove wireless inter-symbol interference (ISI) in receiving ends. We further formulate it into a three-player Stackelberg Game, which helps us to optimize and train this model more efficiently. To verify the feasibility of our proposed MAFENN system and the Stackelberg Game optimization, we conduct a series of experiments to compare the symbol error rate (SER) performance of the MAFENN equalizer and the other methods which utilizes quadrature phase-shift keying (QPSK) modulation scheme. Our performance outperforms that of the other equalizers at different signal-to-noise ratio (SNR) settings for both linear and nonlinear channels.

High overhead of sharing and feedback and high computational complexity are common problems in multi-cell processing. In this paper, a novel framework for bidirectional signal transformation between space and frequency domains of massive MIMO channels is proposed to reduce system processing overhead and complexity. We design new space and frequency features and build the framework by two off-line trained neural networks (NN). Moreover, the uniqueness of spatial features is proved. Average errors of uni- and bi-directional transformation are 7.6% and 7.3%. When applying the framework to inter-cell interference coordination (ICIC), the system and edge throughput are both increased compared to the traditional scheme with low information sharing overhead.

Benefiting from the breakthrough development of the fifth generation (5G), beyond 5G (B5G) wireless communication networks and Artificial Intelligence (AI) in recent years, the artificial intelligence of things (AIoT) is a new trend in the future. AIoT devices often have high-quality wireless video transmission requirements. However, the propagating signals at millimeter wave suffer from high propagation loss and sensitivity to blockage, resulting in the received video is vulnerable to be interfered. Due to the ability of Deep Learning (DL) to discover and learn good representations, some DL methods have achieved breakthrough performance in video recovery. However, most of these methods cannot exploit information from the higher to lower level to refine themselves. In this paper, we propose a novel retrospective thinking based multi-agent (ReTMA) system to solve the interference problem experienced on wireless channels. Compared with other plain feedback models, we add a retrospective agent on the feedback loop, which makes the entire system have stronger capabilities to learn good representative features. We further formulate it as a Stackelberg game to analyze the dependency relationship between the agents and facilitate the complex training issue of the multiple agents. To verify the feasibility of ReTMA system, we randomly add masks to simulate the severe interference received by the video frames in wireless transmissions. Experimental results show that the performances of similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and classification accuracy all achieve significant gains compared with those of other plain feedback models at different mask ratios.

In the previous TD-LTE systems, the semi-static TDD DL/UL configuration can not work effectively under the instantaneous varied DL/UL traffic. Dynamic TDD DL/UL reconfiguration was proposed and discussed as one of the key features in LTE-A to increase the radio resource utilization. However, the freely reconfiguration of each cell TDD DL/UL configuration may result in significant co-channel co-subframe DL-UL interference. In this paper, we propose and evaluate a novel cell clustering scheme for dynamic TDD DL/UL configuration with traffic adaptation in HTN networks where multiple outdoor pico cells and multiple macro cells are deployed on the same carrier frequency. Cells that suffer from serious co-channel co-subframe DL-UL interference are dynamically grouped into different clusters. In order to increase the UL/DL resource utilization with traffic adaptation for clusters, an optimal TDD UL/DL reconfiguration algorithm is proposed to balance the UL/DL buffer status and further increase the transmission performance. Finally, the system level simulation results are provided for the evaluation of our proposed algorithms with non-full buffer traffic under different traffic load levels.

The dynamic TDD DL/UL configuration based on instantaneous asymmetric DL/UL traffics is an effective way to increase the radio resource utilization. However, free reconfiguration of each cell's TDD DL/UL configuration may result in significant co-channel co-subframe DL-UL interference. In this paper, we propose and evaluate a novel centralized cell clustering interference mitigation scheme for dynamic TDD DL/UL configuration with traffic adaptation in HTN networks where multiple outdoor pico cells and multiple macro cells deploy on the same carrier frequency. Cells that suffer from serious eNB-to-eNB and UE-to-UE interference are dynamically grouped into different clusters in a centralized manner. In order to increase the DL/UL resource utilization with traffic adaptation for clusters, an optimal TDD DL/UL reconfiguration scheme is proposed based on the metric of maximizing the predicted total cluster DL and UL throughput deduced by the variations of buffer status and statistic resource utilization. Finally, the system level simulation results are provided for the evaluation of our proposed algorithms with non-full buffer traffic under different traffic load level.

A closed-form precoding scheme using the knowledge of transmit coupling and spatial covariance matrices is proposed, which ensures that the achievable rate increases with the number of transmit antennas when the size of the transmit antenna array is fixed and the radiated power, but not the amplifier output power, is constrained. Eigenvalue-proportional power allocation is employed in this precoding scheme. Simulation shows that the performance of eigenvalue-proportional power allocation is close to that of covariance matrix based waterfilling power allocation. Energy efficiency of different schemes is evaluated and compared.

Orthogonal frequency-division multiplexing (OFDM) and multiple-input-multiple-output (MIMO) are two key technologies adopted in future wireless communication systems, such as LTE/LTE-A. There are many papers addressed the resource allocation problems for OFDM and MIMO systems independently. However few discussions targets on the joint frequency-spatial resource allocation problem, particularly on eigenmode scheduling level. In this paper, we try to propose novel resource allocation schemes on eigenmode selection level, with the joint consideration of precoding problem in spatial domain, eigenmode scheduling and power allocation cross spatial and frequency domains.

It is well-known that wireless scheduling algorithm could exploit multi-user diversity to enhance the network capacity. With multiple transmit/receive antennas, there are additional degrees of freedom which could deliver either spatial multiplexing gain and/or spatial diversity gain. With cross layer scheduling, there is also multi-user selection diversity which contributes to both network capacity and coverage. Due to the huge complexity involved for the optimal solution and big performance gap caused by greedy schemes, in this paper, we proposed a series of genetic algorithm based scheduling schemes with different chromosome code designs for both single- and multi-cell system resource allocation. For single-cell scheduling, we mainly focused on uplink multi-user scheduling with successive interference cancellation at receivers, and joint spatial-frequency scheduling. While for multi-cell scheduling, we proposed a centralized genetic algorithm based scheduling solution that could effectively increase the multi-cell cooperation gain. Simulation results show that the proposed algorithms could fill in most of the performance gap at reasonable complexity compared with optimal solution and greedy scheduling.

In this paper, we investigate the problem of dynamic subchannel assignments in the downlink of OFDM (Orthogonal Frequency Division Multiplexing) systems. The Kuhn-Munkres algorithm can provide the maximum weighted bipartite matching for assignment problems. In this paper, we formulate the multiobjective optimization (MO) problem in bipartite matching, and propose a modified bipartite matching algorithm(MBM) for assignments with MO requirements. This algorithm can be used to solve the weighted bipartite matching problem with multiobjective optimization. We illustrate the application of MBM to subchannel assignments in wireless OFDM systems. The simulation results show that MBM enjoys low computational complexity and maximizes the system capacity, while keeping the fairness among mobile users.

In this paper, we investigate the problem of joint frequency-spatial resource allocation for OFDM-MIMO systems. Based on Hungarian algorithm, the Kuhn-Munkres algorithm can provide the maximum weight bipartite matching for assignment problems. However it can only solve one-dimension resource allocation problems. For multi-dimension problems, such as joint frequency-spatial scheduling, we propose two modified bipartite matching algorithms to optimize the resource allocation for OFDM-MIMO systems. For LTE uplink systems with particular scheduling requirements, a modified bipartite matching algorithm is proposed. The simulation results show that our proposed algorithms can effectively increase the wireless spectrum usage and achieve high system capacity.

Based on the Hungarian algorithm, the Kuhn-Munkres algorithm can provide the maximum weight bipartite matching for assignment problems. However, it can only solve the single objective optimization problem. In this paper, we formulate the multi-objective optimization (MO) problem for bipartite matching, and propose a modified bipartite matching (MBM) algorithm to approach the Pareto set with a low computational complexity and to dynamically select proper solutions with given constraints among the reduced matching set. In addition, our MBM algorithm is extended to the case of asymmetric bipartite graphs. Finally, we illustrate the application of MBM to antenna assignments in wireless multiple-input multiple-output (MIMO) systems for both symmetric and asymmetric scenarios, where we consider the multi-objective optimization problem with the maximization of the system capacity, total traffic priority, and long-term fairness among all mobile users. The simulation results show that MBM can effectively reduce the matching set and dynamically provide the optimized performance with different quality of service (QoS) requirements.

In this paper, a dynamic resource allocation algorithm to satisfy the packet delay requirements for real-time services, while maximizing the system capacity in multiuser orthogonal frequency division multiplexing (OFDM) systems is discussed. Our proposed cross-layer algorithm, called Joint Dynamic Subcarrier Allocation and Flow Control (DSA-FC) algorithm, consists of two interactive components. In the medium access control (MAC) layer, the users' expected transmission rates in terms of the number of subcarriers per symbol and their corresponding transmission priorities are evaluated. With the subcarrier gain information of each user, the physical (PHY) layer subcarrier allocation is optimally designed to satisfy the users' requirements under the system signal-to-noise ratio (SNR) and power constraints. In a system where the number of active users changes dynamically, the MAC-layer congestion control and removal schemes can guarantee the quality of service (QoS) of the existing users in the system and fully utilize the bandwidth resource. The proposed algorithm combines these two components, and the numerical results show that it significantly improves the system performance in terms of the bandwidth efficiency and delay performance for real-time services.

In this paper, we develop contention-based medium access control (MAC) schemes for both best-effort data transmissions and delay-sensitive multimedia transmissions over WLANs. A user detection module and a multi-rate adaptation module are proposed in the physical layer to assist in link differentiation. With these two modules, for best-effort data transmissions, a new distributed queuing MAC protocol (PALD-DQMP) is proposed. Based on different users' channel states, PALD-DQMP makes use of a distributed queuing system to schedule the transmissions. To support delay-sensitive multimedia transmissions, an enhanced PALD-DQMP (E-PALD-DQMP) is designed by providing two-level optimized transmission scheduling for four access categories, thus eliminating both external and internal collisions among mobile stations. Simulation results show that our proposed protocols outperform the standard MAC protocols for both delay-sensitive and best-effort traffics. All these improvements are mainly contributed by the availability of cross-layer channel state information, and the consequent multi-rate adaptation scheme.

This paper develops a multipolling MAC protocol which exploits cross-layer information to support delay-sensitive multimedia services over WLANs. A user detection module and a multi-rate adaptation module are proposed in the physical layer to assist in link differentiation. With these two modules, our new multi-polling MAC protocol, named PALD-MPMP not only reduces the polling overhead, but also provides an effective polling scheduler by allocating transmission priorities to users according to their delay requirements or levels and channel gains. Simulation results show that our proposed protocol outperforms the standard point coordination function (PCF) for delay-sensitive services. All the performance improvements are mainly contributed by the awareness of cross-layer channel state information, and the consequent multi-rate adaptation schemes.

The binary exponential backoff (BEB) algorithm used in IEEE 802.11 DCF suffers from the unfairness problem and yields low throughput under heavy load. With physical layer assisted loss differentiation, this paper proposes a new distributed queuing medium access control (MAC) protocol (PALD-DQMP). In this protocol, utilizing the user detection module in the physical layer, losses due to collisions are distinguished from those due to link errors, and such information is made available to the MAC layer. Based on different users' channel states, PALD-DQMP schedules their transmissions. Simulation results show that the proposed scheme outperforms the standard MAC protocol in terms of network throughput and fairness. This improvement is mainly due to the availability of cross-layer channel information, and the elimination of collisions and backoff periods.

Transmission control protocol (TCP) performs well in traditional wired networks where the packet loss rate is low. However, in heterogeneous wired/wireless networks, the high packet loss rate over wireless links may result in excessive invocation of the congestion control algorithm, thus deteriorating the performance of TCP. In this paper, a novel localized link layer retransmission protocol, called clustered-loss retransmission protocol (CLRP), is proposed. CLRP consists of three protocol components, namely, TCP-FH deployed on a fixed host, TCP-MH deployed on a mobile host and CLRP-BS deployed on a base station. CLRP can provide not only explicit distinction between congestion and packet corruption losses, and effective multiple wireless loss information for retransmissions, but also better retransmission control for wireless losses. Thus it is well suited to wireless networks, in which packet loss and bursty packet corruption is a serious problem. Moreover, CLRP does not require any modifications to TCP deployed on fixed hosts.

TCP is the most widely adopted transport layer communication protocol. In heterogeneous wired/wireless networks, however, the high packet loss rate over wireless links can trigger unnecessary execution of TCP congestion control algorithms, resulting in performance degradation. TCP performs poorly on wireless links with bursty losses, when it is forced to rely on limited information available from batched acknowledgements, (i.e., multiple packets are acknowledged with one acknowledgment packet). In this paper, a selective negative acknowledgement (SNACK) mechanism is designed to overcome the limitation of batched acknowledgments. A new link layer retransmission protocol, called, SNACK-NS (new snoop), is proposed. Through the detection and retransmission functions that are provided by the two protocol components of SNACK-NS, namely, SNACK-snoop and SNACK-TCP, the transmission performance of TCP over wireless network is greatly enhanced in both fixed host (FH) to mobile host (MH) and MH to FH transmissions.