In recent years, uncrewed aerial vehicles (UAVs) have become increasingly prevalent for collecting environmental data from various wireless sensors. However, existing research on employing UAVs to collect data from wireless sensors has often ignored the heterogeneous requirements of sensors. In this paper, we investigate joint deployment, user association, and power allocation for data collection in the UAV-assisted wireless sensor network to accommodate the heterogeneous requirements of sensors, where a novel satisfaction function is designed for three types of sensors, including sensors with delay requirements, sensors with energy consumption requirements, and sensors with both delay and energy consumption requirements. Leveraging the satisfaction function, we formulate the optimization problem aimed at jointly optimizing the positions of UAVs, the association between sensors and UAVs, and the power allocation of sensors to maximize overall satisfaction of sensors. In order to effectively address the considered problem, we decompose it into two subproblems, i.e., joint UAV deployment and user association subproblem, and transmission power allocation subproblem. An enhanced human evolutionary algorithm is developed to tackle the joint UAV deployment and user association subproblem, and the Lagrange dual method and gradient descent method are employed to solve the transmission power allocation subproblem. The suboptimal solution is achieved by iteratively addressing the two subproblems until convergence of the proposed enhanced Lagrange and gradient descent-based human evolutionary optimization algorithm is attained. Extensive simulations demonstrate the effectiveness of the proposed algorithm in enhancing overall satisfaction of sensors, underscoring its significant advantages in managing heterogeneous network environments.

In this paper, we investigate an air-to-ground (A2G) wireless network system where multiple uncrewed aerial vehicles (UAVs) provide downlink communication coverage for mobile ground users (GUs). This system accounts for UAVs progressively depleting their energy during coverage provision, ceasing operations when their energy reserves fall below a predefined threshold. We aim to maximize cumulative system throughput over the task period while satisfying the minimum fairness requirement through joint trajectory control and resource allocation (JTCRA) optimization. To meet the fairness requirement, enhancing system robustness is critical; energy-sufficient UAVs must autonomously assist GUs that lose connectivity when their serving UAVs terminate operations. Therefore, we propose a multi-agent deep reinforcement learning (MADRL) framework with a parameter-sharing architecture to solve this problem. As conventional parameter sharing is restricted to homogeneous agents with identical observation-action spaces, we design a dual-agent structure: a trajectory agent (Traj-agent) and a communication agent (Comm-agent) are deployed for each UAV. This separation organizes the heterogeneous tasks of trajectory control and resource allocation into distinct homogeneous agent groups, facilitating effective parameter sharing within each type. Based on this framework, we apply two alternative algorithms: an MAPPO-based JTCRA algorithm and a QMIX-based JTCRA algorithm. Simulation results demonstrate the superiority and effectiveness of our proposed JTCRA algorithms, which maintain service continuity for GUs through intelligent trajectory control, thereby minimizing the adverse impact of coverage gaps.

Optimizing unmanned aerial vehicle (UAV)-assisted wireless networks to serve mobile users (MUs) via beamforming presents significant challenges, mainly due to the dynamic and complex environments. Traditional single-modal data-based modeling methods are often insufficient for capturing the varying environmental characteristics, leading to inaccurate UAV trajectory design and beamforming. To address these issues, we propose a multi-UAV-assisted integrated sensing, communication, and computation (ISCC) framework that processes multi-modal data to enhance environmental awareness and improve communication performance. We then formulate an optimization problem to maximize the average sum rate by jointly optimizing the UAV trajectory and beamforming vectors. Given the non-convex nature of the problem, traditional optimization techniques are inadequate. To this end, we introduce a fine-tuned multi-modal large language model (M2LLM)-driven deep reinforcement learning (DRL)-based joint optimization framework. Specifically, a pre-trained M2LLM is first fine-tuned to predict future MU positions by leveraging historical multi-modal data, including texts, images, and wireless sensing data. The fine-tuned M2LLM is then employed to extract environmental features, where the output of the fine-tuned M2LLM’s last hidden layer is regarded as the environment state vector to eliminate the output uncertainty of the M2LLM. Subsequently, we use a DRL agent to optimize the UAV trajectory and beamforming in a coordinated manner. Extensive simulation results demonstrate that the proposed framework can significantly enhance network performance by enabling environment-aware and adaptive trajectory design and beamforming. The code is available in https://huggingface.co/blYin/MmllmDrlUavTdBf.

In Wi-Fi mmWave networks, blockage and mobility cause frequent beam switching, risking link stability. In 802.11ad, neighboring beam search method is used to select optimal beams, which only tests the limited candidate directions near the current beam, possibly leading to suboptimal communication performance. To improve the link stability, in this paper we propose a sub-7GHz and mmWave integrated Wi-Fi network architecture, where the omnidirectional sub-7GHz band is leveraged to improve the network coverage. To enhance the beam tracking performance, we further propose a CNN-LSTM based beam tracking scheme, where the CNN-LSTM is leveraged to extract features from both sub-7GHz and partial mmWave channel state information (CSI). Simulation results show that our proposed scheme can improve beam tracking accuracy, reduce beam search time and achieve higher system capacity.

The IEEE 802.11ax and 802.11be standards support multiple data transmission modes, including orthogonal frequency division multiple access (OFDMA), single-user multiple input multiple output (SU-MIMO), multi-user MIMO (MU-MIMO), and combination of OFDMA and MU-MIMO transmission mode. One of the significant challenges of improving system performance is how to select proper transmission mode and allocate resources based on the characteristics of transmission modes and quality of service (QoS) requirements of different traffic types. In this paper, we propose an adaptive algorithm for selecting data transmission modes. The optimization goal is to maximize the sum of user satisfactions degree, namely system satisfaction degree, subject to the delay requirements of latency-sensitive traffics. We adopt the genetic algorithm to obtain the decision of transmission mode selection. Moreover, we design the resource unit (RU) and spatial stream allocation algorithms to maximize resource utilization ratio. The simulation results show that the proposed algorithm can adaptively select suitable transmission modes for the scheduled users, and provide frequency and stream resources allocation. Compared to three baseline algorithms, the system satisfaction and throughput can be improved significantly,

Multi-link operation (MLO) is one of the key features of 802.11be, which can achieve higher throughput, and lower latency than single-link operation in a heavily loaded network. For a nonsimultaneous transmit and receive (NSTR) multi-link device (MLD), synchronous transmissions across multiple links are required to avoid the in-device coexistence (IDC) interference. Compared to legacy access mechanisms of single-link, the modeling and performance theoretical analysis of synchronous multi-link access mechanisms face some new challenges. The two-dimensional Markov performance analysis model for single-link cannot be applied to multi-link scenarios. In this paper, we present an analytical model to compute the multi-link synchronous access probability and the saturation throughput in the assumption of ideal channel conditions. The proposed Markov model combines the distributed coordination function (DCF) mechanism with the process of multi-link synchronous access based on the threshold of waiting time. Different from other proposed MLO models, we consider the stochastic state of channel during the synchronous access backoff procedures of each link, and define the concept of waiting time threshold. Comparison with simulation results show that the proposed model is accurate in predicting the multi-link synchronous access probability, as well as system saturation throughput.

With the rapid advancements in wireless communication fields, including low-altitude economies, 6G, and Wi-Fi, the scale of wireless networks continues to expand, accompanied by increasing service quality demands. Traditional deep reinforcement learning (DRL)-based optimization models can improve network performance by solving non-convex optimization problems intelligently. However, they heavily rely on online deployment and often require extensive initial training. Online DRL optimization models typically make accurate decisions based on current channel state distributions. When these distributions change, their generalization capability diminishes, which hinders the responsiveness essential for real-time and high-reliability wireless communication networks. Furthermore, different users have varying quality of service (QoS) requirements across diverse scenarios, and conventional online DRL methods struggle to accommodate this variability. Consequently, exploring flexible and customized AI strategies is critical. We propose a wireless network intent (WNI)-guided trajectory generation model based on a generative diffusion model (GDM). This model can be generated and fine-tuned in real time to achieve the objective and meet the constraints of target intent networks, significantly reducing state information exposure during wireless communication. Moreover, The WNI-guided DRL optimization trajectory generation can be customized to address differentiated QoS requirements, enhancing the overall quality of communication in future intelligent networks. Extensive simulation results demonstrate that our approach achieves greater stability in spectral efficiency variations and outperforms traditional DRL optimization models in dynamic communication systems.

To effectively support diverse services and applications, operation of future Wi-Fi networks has to be highly intelligent. AI/ML has been considered an important component of the next-generation Wi-Fi standard (i.e., Wi-Fi 8). In addition, the distributed and relatively stable Wi-Fi operational environment bring more realistic AI/ML applications than other wireless networks. By opportunistically leveraging distributed characteristics of Wi-Fi, this paper focuses on the service-differentiated joint optimization of communication and computing resource allocation with varying privacy sensitivity. By extending multi-agent deep reinforcement learning (MADRL), a new semi-centralized and fully distributed joint resource optimization structure is created. Our purpose is to maximize the service quality of the DRL model for privacy-insensitive users while minimizing private information exchange during the training period of privacy-sensitive users during the DRL interaction process. The proposed approach leverages the varying service requirements of different stations (STAs), facilitates real-time optimization of local communication resource allocation, and enables concurrent decision-making for computing resources. In addition, we explored the heterogeneous differences in channel states between communication nodes and utilized the federated weighting (FedWgt) method to address this issue, further improving the stability of the distributed model in solving the service-differentiated joint optimization problem of resource allocation. Extensive simulation experiments demonstrate that the proposed scheme outperforms baseline methods significantly in terms of throughput, calculation latency, and energy consumption improvement.

The introduction of Integrated Sensing and Communication (ISAC) technology in high-speed railway mobile networks (HSRMNs) addresses reliability concerns within existing railway operation control systems. However, current ISAC research often focuses on overall performance assessment across extended periods, potentially overlooking practical sensing requirements, resulting in degraded or even unstable communication performance. To mitigate these challenges, we propose an Integrated On-Demand Sensing and Communication (IDSAC) mechanism for HSRMNs. IDSAC optimizes train communication energy efficiency (EE) by jointly optimizing target sensing selection and transmit beamforming, while adhering to Age of Information (AoI) constraints and ensuring beam pattern gain for designated targets. To tackle the non-convex mixed-integer programming challenge, we decompose the problem into two parts. First, we employ iterative algorithms using successive convex approximation (SCA) and semidefinite relaxation (SDR) to address the EE problem. Second, we introduce a dynamic control algorithm employing the Lyapunov drift-plus-penalty method to manage AoI constraints and sensing selection. Our joint optimization approach achieves optimal EE under dynamic operational constraints typical of high-speed rail systems. Simulation results validate the effectiveness of IDSAC in optimizing ISAC performance, enhancing railway safety and communication efficiency.

With the explosion of connected devices and Internet-of-Things (IoT) services in the smart city, the challenge to meet the demands of urban computing is increasingly prominent. Recent advances in vehicle-to-everything (V2X) communications promote urban Internet-connected vehicles to become excellent candidates for computing tasks. However, due to the limited computing capacity of vehicles, conducting computation in the vehicular networks themselves is insufficient to satisfy the demands of smart city applications. Edge computing, which delivers computing tasks to edge servers (e.g., base stations, BSs, or roadside units, RSUs) with plenty of computing resources, could be a possible solution. However, the static deployment of edge servers may cause severe load unbalance among servers in both real-time communication and computation, thereby decreasing the system performance. This paper explores a two-hop vehicle-assisted edge computing network framework in which vehicles are able to offload the tasks beyond their capabilities to underloaded edge servers relaying via neighbor vehicles. According to the state of the time-varying vehicular environment and the dynamic traffic loads among RSUs, we formulate the task offloading, relay node selection, and resources allocations problem as a Markov decision process (MDP) aiming at maximizing the performance of the computation offloading capacity with the considerations of load balancing and latency constraints. We propose a deep reinforcement learning (DRL) algorithm with a DNN as Q action-value function approximator to solve this problem. Extensive simulation results reveal that the proposed scheme can significantly improve the system performance compared to other state-of-the-art algorithms.

In an integrated sensing and communication (ISAC) system, it is often necessary to sense both the state of individual targets and the overall situation of a group target (GT) simultaneously, but the latter is more challenging due to the limited sensing capabilities and resources of a single base station. Owing to the recent rapid development of artificial intelligence (AI) and unmanned aerial vehicle (UAV) technologies, it is feasible to acquire the high-performance situation awareness of the GT by using AI to process sensing data under the cooperation of multiple UAV aerial base stations. However, due to many force majeure factors, such as power depletion, and disruptive actions, some UAVs may be disabled, which affects the situation awareness of the GT. Therefore, it is important to improve the robustness of the situation awareness. To achieve that, we consider a multi-UAV-enabled integrated sensing, communication and computation (ISCC) network, where more than one UAVs complete the group target sensing task (GTST) cooperatively while providing communication services for users. Furthermore, we deploy a pre-trained AI model to process the sensing data for improving the performance of the GTST. To enhance the robustness of the GTST, we apply split learning (SL) to divide the inference task of disabled UAVs into multiple neural network (NN) blocks that are cooperatively inferred by working UAVs. Then, we formulate a GTST completion rate maximization problem in which the trajectory, resource allocation, sensing target scheduling, beamforming, and NN layer split policy are optimized. Due to the non-convexity of the problem, we propose a collaborative multi-agent reinforcement learning scheme. The simulation results show that the proposed scheme can effectively improve the GTST performance while the minimum communication and sensing performance are guaranteed, and its performance is better than that of some benchmark schemes.

The continuous evolution of future mobile communication systems is heading towards the integration of communication and computing, with Mobile Edge Computing (MEC) emerging as a crucial means of implementing Artificial Intelligence (AI) computation. MEC could enhance the computational performance of wireless edge networks by offloading computing-intensive tasks to MEC servers. However, in edge computing scenarios, the sparse sample problem may lead to high costs of time-consuming model training. This paper proposes an MEC offloading decision and resource allocation solution that combines generative AI and deep reinforcement learning (DRL) for the communication-computing integration scenario in the 802.11ax Wi-Fi network. Initially, the optimal offloading policy is determined by the joint use of the Generative Diffusion Model (GDM) and the Twin Delayed DDPG (TD3) algorithm. Subsequently, resource allocation is accomplished by using the Hungarian algorithm. Simulation results demonstrate that the introduction of Generative AI significantly reduces model training costs, and the proposed solution exhibits significant reductions in system task processing latency and total energy consumption costs.

In this paper, to avoid communication interruptions when moving between APs in WLANs, we propose a multilink based seamless handoff scheme for next generation Wi-Fi networks, in which the power of target AP is adjusted to facilitate the connection establishment of STAs in advance. Furthermore, to mitigate extra energy consumption and overlapping basic service set (OBSS) interference to other STAs, we develop a dynamic power adjustment (DPA) algorithm inspired by the PSO (particle swarm optimization) algorithm. The DPA algorithm determines the optimal period of power adjustment and power levels during the handoff process. In the scheme, we also employ three metrics including throughput, power consumption, and OBSS interference to evaluate the system performance. Compared with the traditional AP handoff scheme, our proposed scheme effectively reduces OBSS interference and energy consumption while avoiding communication interruptions.

With the proliferation of densely deployed access points (APs), competition among multiple APs has led to increased occurrences of co-channel interference and access conflicts between APs. The IEEE 802.11 working group has proposed multi-AP coordination schemes in the next-generation Wi-Fi standard to enhance Wi-Fi performance in multi-AP scenarios. We primarily investigate the coordinated spatial reuse (CSR) mechanism for multiple APs. In the CSR group, a set of APs utilize the same channel for data transmission within a single transmission opportunity (TXOP), during which power control for individual devices is necessary to mitigate interference between APs. We propose a Federated Learning Double Deep Q-Network (FL-DDQN) algorithm for power allocation among multiple users in the CSR environment. The simulation results indicate that the proposed algorithm can improve throughput performance by approximately 11.06% to 100.51% and reduce the latency by approximately 61.34% to 86.71 % compared to several baseline schemes.

The wireless sensor based railway monitoring system faces challenges such as limited battery lifetime of sensor nodes (SNs). Moreover, information freshness of sensing data is a crucial metric for environment monitoring. In this paper, to extend SNs' battery lifetime and enhance information freshness of sensing data, we propose to employ unmanned aerial vehicles (UAVs), whose trajectory is planable, to cooperate with base station (BS) for data collection from wirelss sensor networks (WSNs). Then, we formulate the optimization problem with the objective to minimize the energy consumption of SNs and the age of information (AoI) which is used to evaluate the information freshness, by jointly adjusting the task allocations of data collection and the UAV trajectories. We find that our formulated optimization problem is a Markov decision process (MDP), based on which we propose a deep reinforcement learning (DRL)-based UAV-BS-coordinated data collection algorithm to find an asymptotically optimal solution. Simulation results demonstrate that our proposed DRL-based UAV-BS-coordinated data collection algorithm can significantly reduce AoI of sensing data and effectively extend the battery lifetime of the SNs compared to other baseline algorithms.

The safety in autonomous driving depends on reliable and efficient sharing of massive sensor data via the internet of vehicles (IoV). In the upcoming sixth-generation (6G) mobile communication network, joint communication and sensing (JCAS) on millimeter wave (mmWave) bands has become a representative technique to achieve the dual functions of ultra-broadband communications and high-precision sensing at the same hardware and software costs. However, under high-mobility IoV,directional mmWave beams cannot provide robust network coverage. Based on this observation, in this paper we devise a sub-6GHz and mmWave integrated IoV wireless network architecture, where the sub-6GHz bands carry control information, and the mmWave bands perform large-volume communication and driving environment sensing. Each vehicle operates two mmWave beams respectively to realize communication and environment sensing. Then, we analyze the inter-beam interference between communication and sensing beams, and propose a Q-learning based beam resource allocation scheme to alleviate the interference problem. Simulation results demonstrate that our proposed scheme can highly improve the mmWave communication and sensing performance.

In this paper, we consider deploying multiple unmanned aerial vehicles (UAVs) to provide integrated sensing, communication, and computation (ISCC) services. During serving communication users, each UAV also senses targets and collaborates with the edge server to run a deep neural network (DNN) model to process the obtained sensing data for target classification. Considering that applying the fixed collaborative computation configurations for the UAVs and edge server cannot adapt to various task latency requirements and dynamic network conditions, we propose to adaptively split the DNN into two parts and execute them on the UAV and the edge server separately to realize flexible collaborative computation. We aim to maximize the average sum rate of users by jointly optimizing the user association, target assignment, DNN splitting, transmit beamforming, computation resource allocation, and UAVs’ locations, subject to the latency and accuracy requirements of sensing tasks. We apply alternating optimization algorithm to solve this complicated non-convex optimization problem. Specifically, the problem is decomposed into four subproblems, and the matching-based method, penalty dual decomposition, and successive convex approximation are leveraged to solve them. Finally, simulation results demonstrate the superiority of the proposed adaptive DNN splitting scheme and the effectiveness of the proposed algorithm.

In this paper, we investigate the joint unmanned aerial vehicle (UAV) deployment and resource allocation problem to minimize the latency of multi-UAV-assisted computation offloading in air-ground integrated millimeter-wave (mmWave) networks, in which UAVs have both computing and relaying capabilities, thereby providing more opportunities for ground user equipments (UEs) to access the moble edge computing (MEC) servers with rich computing resources. Moreover, the study also takes into account the dynamic interference experienced by UEs due to different uploading completion times during the computation offloading process. To efficiently address the considered non-convex problem, we split it into four subproblems, i.e., UAV deployment, MEC server selection, computation resource and task ratio allocation, and power allocation subproblems, and solve them iteratively. Specifically, the first one is solved by three-dimensional-strategy iterative weekly acyclic game, the second one is addressed by Markov Approximation approach in which the third one is solved by the interior point method at each iteration, and the last one is solved by whale optimization algorithm (WOA). Finally, extensive simulations are provided to demonstrate the effectiveness of the proposed approach, and results have shown the approach can effectively mitigate the effect of blockage on mmWave transmissions and reduce the total latency of all UEs, particularly in scenarios where the communication bandwidth is limited or data volumes of tasks are large.

To cope with the complex and constantly changing communication environment, Multi-Link Operation (MLO) has attracted extensive attention in research and development of next-generation Wi-Fi technology, IEEE 802.11be standard (Wi-Fi 7). MLO can transmit information simultaneously on different channels in the same device, which can significantly increase the capacity of Wi-Fi for future communication systems. Previous relevant studies have shown that network throughput is not simply and positively correlated with frame aggregation length. Furthermore, due to the variability of communication environments, the optimal frame aggregation length in the scheduling process is not unique within a given time, and the traditional methods are limited to solving non-convex optimization problems. To fill this gap, we present a novel approach using deep reinforcement learning (DRL) to tackle the optimization of frame aggregation lengths in 802.11be for multiple links. Our research offers a comprehensive depiction of the communication and interaction structure among multiple links and DRL, which helps drive the advancement of artificial intelligence (AI) solutions in future network designs and demonstrates the feasibility of exploiting DRL in next-generation wireless networks. Extensive simulation experiments show that the proposed method can achieve superior performance compared to the existing methods.

Owing to the controlling flexibility and cost-effectiveness, fixed-wing unmanned aerial vehicles (UAVs) are expected to serve as flying base stations (BSs) in the air-ground integrated network. By exploiting the mobility of UAVs, controllable coverage can be provided for mobile group users (MGUs) under challenging scenarios or even somewhere without communication infrastructure. However, in such dual mobility scenario where the UAV and MGUs are all moving, both the non-hovering feature of the fixed-wing UAV and the movement of MGUs will exacerbate the dynamic changes of user scheduling, which eventually leads to the degradation of MGUs’ quality-of-service (QoS). In this paper, we propose a fixed-wing UAV-enabled wireless network architecture to provide moving coverage for MGUs. In order to achieve fairness among MGUs, we maximize the minimum average throughput between all users by jointly optimizing the user scheduling, resource allocation, and UAV trajectory control under the constraints on users’ QoS requirements, communication resources, and UAV trajectory switching. Considering the optimization problem is mixed-integer non-convex, we decompose it into three optimization subproblems. An efficient algorithm is proposed to solve these three subproblems alternately till the convergence is realized. Simulation results demonstrate that the proposed algorithm can significantly improve the minimum average throughput of MGUs.

Owing to the abundance of onboard energy and wide coverage, fixed-wing unmanned aerial vehicles (FW-UAVs) have better capabilities to serve as aerial base stations, thereby extending communication coverage and improving the performance of ground wireless communication networks. Therefore, the FW-UAV is regarded as one of the essential components of the sixth-generation (6G) communication networks. However, due to its inability to hover, a single FW-UAV may only serve a few mobile users (MUs) at a given time which introduces challenges in ensuring uninterrupted service. Additionally, the limited communication resource further impacts the quality of service (QoS). In order to improve the QoS and guarantee the uninterrupted services of the MUs that are located in a wide range, we consider a multi-FW-UAV communication network to maximize the cumulative throughput by optimizing the trajectory, power control, user association, and subcarrier allocation policy jointly. Since the above problem is non-convex, we first decompose the optimization problem into two subproblems i.e., the trajectory optimization subproblem and the power control, user association, and subcarrier allocation policy optimization subproblem. Then, a multi-agent deep reinforcement learning (MA-DRL)-based joint optimization scheme is proposed to optimize the two subproblems jointly. Simulation results demonstrate that the proposed scheme can maximize the cumulative throughput and gain superior performance compared to the benchmark schemes.

Millimeter-wave (mmWave) communication is one of the key technologies to drive future high-capacity communication in the sixth generation (6G) network. However, the easily blocking characteristics of mmWave communication links hinder the reliable transmission in mmWave communication. To improve the reliability of mmWave transmission, we investigate the joint coordinated multi-beam selection and power control scheme that maximizes average sum rate of UEs. To efficiently solve this problem, we recast it as a potential game and propose a best response based approach to find the Nash equilibrium (NE). Simulation results have shown that the proposed solution has better average sum rate, and can achieve more reliable mmWave communications.

Signals over millimeter wave (mmWave) bands suffer from severe path loss and are easily blocked by obstacles, which greatly degrades the link quality and reliability of mmWave communications. One of the promising ways to overcome this challenge is multi-connectivity, which enables a user to associate with multiple small cells simultaneously. In this paper, we investigate the association problem of a given user to multiple mmWave base stations (mBSs), which we termed user-centric association. In particular, we consider an intelligent reflecting surface (IRS)-aided ultra-dense mmWave communication system, in which multiple distributed IRSs are deployed to expand the coverage of mmWave signals to blind spots. The user-centric association problem is formulated to maximize the user achievable sum-rate with respect to the mBS-user association, auxiliary IRS selection, and power allocation in a combinatorial manner. The original optimization problem is a mixed-integer nonlinear programming problem, which is NP-hard. To solve it, we first relax the formulated problem into a continuous one, then decouple it into three subproblems by utilizing decomposition technique, and finally propose an alternating iteration based algorithm to obtain the optimal solution. Numerical simulations show that the user sum-rate can be greatly improved by the joint optimization scheme.

Multi-Access Point (AP) cooperation is one of the potential core technologies in the future Wi-Fi 8 standard. By sharing information among multiple APs, it can improve spectrum efficiency and throughput in dense scenarios. However, most of existing researches on Wi-Fi access control mechanism optimization focuses on adjusting the contention window (CW) and carrier sense threshold (CST) values within a single Basic Service Set (BSS), which do not well support the multi-AP cooperation for densely deployed multi-BSS scenarios. The overlapping BSS preamble-detection (OBSS_PD) defined in the 802.11ax standard is the basis of spatial reuse (SR) in OBSS scenarios. It only provides the adjustment range and constraints for OBSS_PD, but does not specify how to adjust OBSS_PD. Moreover, there is a certain coupling relationship between CW and OBSS_PD. In order to efficiently carry out the multi-AP cooperation in dense Wi-Fi scenarios, in this paper, we propose an AI-enabled optimization algorithm for multi-AP access control based on a deep reinforcement learning method—the Deep Deterministic Policy Gradient (DDPG) method, which jointly adjusts the CW and OBSS_PD parameters through multi-AP cooperation. The goal is to improve the aggregate throughputs in OBSS scenarios. The simulation results indicate that the proposed algorithm can improve throughput performance by approximately 7.91% to 56.85% compared to several baseline schemes.

Frame aggregation and physical rate adaptation are the most important enhancement for Wi-Fi network. However, both of them involve certain tradeoffs between achieving higher throughput and facing a higher error rate. The gain suffers from the imperfect and highly dynamic channel condition. In addition, there is a certain coupling relationship between the aggregation frame length and the physical rate. That means the selection of physical rate may affects the optimal frame length, and vice versa. Therefore, a joint frame length and rate adaption scheme is needed. Moreover, the large number of all available frame lengths and rates makes the joint adaption more challenging. In this paper, we propose a joint frame length and rate adaption (JFRA) scheme based on Double Deep Q-learning (DDQN) algorithm. The proposed scheme can automatically explore the environment and learn the optimal frame length and rate from experience. We apply prioritized training and incorporate reward value into the computation of experience priority. It can improve learning efficiency and accelerate the convergence of JFRA. We implement and evaluate JFRA in ns3-ai framework and the simulation results show that JFRA can outperform the Minstrel HT and Thompson Sampling algorithm by up to 21.3% and 68.9% in various cases.

With miscellaneous computation-intensive applications generated in vehicular network, e.g., automatic driving and in-car entertainment, it cannot achieve ideal computing performance since vehicles have limited task processing capabilities. To fulfill the goal of low latency and sustainable development in beyond 5G network, we design a cost efficient task processing scheme in dual-band cooperative vehicular network where the tasks can be processed locally, or offloaded to macro-cell base station or road side unit through low-frequency or high-frequency band. Based on this, the sum cost considering energy consumption and latency is minimized through optimizing the task scheduling, computation and communication resource allocation while considering the vehicle sojourn time. Owing to the non-convexity of problem, we decompose it into three subproblems and propose an alternating algorithm to solve them iteratively. Numerical results validate the superiority of the proposed dual-band cooperative task processing scheme and evaluate its performance under different parameter settings, which prove it to be an efficient edge computing scheme for the future vehicular network.

Due to the flexible mobility and agility, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future air–ground-integrated wireless networks, providing temporary and controllable coverage and additional computation capabilities for ground Internet of Things (IoT) devices with or without infrastructure support. Meanwhile, with the breakthrough of artificial intelligence (AI), more and more AI applications relying on AI methods such as deep neural networks (DNNs) are expected to be applied in various fields, such as smart homes, smart factories, and smart cities, to improve our lifestyles and efficiency dramatically. However, AI applications are generally computation intensive, latency sensitive, and energy consuming, making resource-constrained IoT devices unable to benefit from AI anytime and anywhere. In this article, we study mobile-edge computing (MEC) for AI applications in air–ground-integrated wireless networks. Our goal is to minimize the service latency while ensuring the learning accuracy requirements and energy consumption. To achieve that, we take DNN as the typical AI application and formulate an optimization problem that optimizes the DNN model decision, computation and communication resource allocation, and UAV trajectory control, subject to the energy consumption, latency, computation, and communication resource constraints. Considering the formulated problem is nonconvex, we decompose it into multiple convex subproblems and then alternately solve them till they converge to the desired solution. Simulation results show that the proposed algorithm significantly improves the system performance for AI applications.

Unmanned aerial vehicle (UAV) has high flexibility and controllable mobility, therefore it is considered as a promising enabler for future integrated sensing and communication (ISAC). In this paper, we propose a novel adaptable ISAC (AISAC) mechanism in the UAV-empowered system, where the UAV performs sensing on demand during communication and the sensing duration is flexibly configured according to the application requirements rather than keeping the same with the communication duration. Our designed mechanism avoids the excessive sensing and waste of radio resources, therefore improving the resource utilization and system performance. In the UAV-empowered AISAC system, we aim at maximizing the average system throughput by optimizing the communication and sensing beamforming as well as the UAV trajectory while guaranteeing the quality-of-service requirements of communication and sensing. To efficiently solve the considered non-convex optimization problem, we propose an efficient alternating optimization algorithm to alternately optimize the communication and sensing beamforming as well as the UAV trajectory to obtain a suboptimal solution. Numerical results validate the superiority of the proposed adaptable mechanism and the effectiveness of the designed algorithm.

Multiple Resource Unit (MRU) is one of the new key features introduced by IEEE 802.11be draft. Different from IEEE 802.11ax standard, which supports assigning only one RU to each user, 802.11be supports MRU assigned to a single user to enhance the resource utilization and flexibility. Due to the strict restrictions on combinations of MRU sizes and locations specified in IEEE 802.11be draft, the resource allocation algorithms face some new challenges. In this paper, we designed an MRU resource allocation algorithm based on user priority, which considered the restrictions of MRU distribution pattern and MRU combination on different channel bandwidth. User priority is defined according to the channel condition and the priority of traffic types. Then an optimal MRUs allocation mechanism for users with different priorities is proposed to maximize the aggregate satisfaction level of the scheduled users. The matching problem of STAs and MRU locations is modeled based on graph theory KM (Kuhn-Munkras) algorithm, to find the perfect matching solution. Simulation results show that the proposed scheme can improve throughput by 39.8% and the user satisfaction level by 22.03%, compared with the baseline schemes, which evenly assigned the RU resources.

IEEE 802.11bc is especially designed for enhancing the broadcast performance in wireless local network. Focusing on improving the broadcast data frame scheduling and origin authenticity performance, the broadcast data frame scheduling scheme is completely re-designed and the data origin authenticity is introduced in the IEEE 802.11bc. However, after introducing these updated mechanisms, the data broadcast performance due to station mobility, especially during the handover period, remains un-explored. Motivated by this, in this paper, we investigate the downlink handover broadcast data frame loss rate in the IEEE 802.11bc. With special focus on the updated broadcast data frame scheduling and data authentication in the IEEE 802.11bc, we derive an analytical model for the downlink broadcast frame loss rate. Based on the analytical model and simulation test, we give the broadcast parameters configuration for the specified average broadcast data frame loss rate constraint.

IEEE 802.11bc introduces enhanced broadcast service (EBCS) to improve data broadcast scheduling and authentication performance in wireless local network (WLAN). The essential design in the EBCS is introducing a central scheduling scheme for data broadcasting, and this makes that the conventional distributed coordination function (DCF) based broadcast performance analysis does not apply. Thus, in this paper, we investigate the frame collision between DCF-based unicast data and scheduling-based broadcast data within the framework of IEEE 802.11bc, which has not been explored yet. Specifically, we study the tradeoff among station arrival intensity, broadcast and unicast frame collision, and broadcast control frame transmission interval, which directly determines the broadcast overhead. We derive an analytical model to quantity the above performance tradeoff. Based on the analytical model and simulation test, we optimize the broadcast control frame transmission interval to balance the broadcast overhead and data collision.

The abundant available spectrum resources have made millimeter wave (mmWave) communications the key feature of the fifth generation (5G) mobile communications, allowing ultra-high transmission capacity. Additionally, mmWave bands, already widely used in radar systems, show a great advantage in environment sensing. Based on these observations, to satisfy the ever-growing mobile service requirements, and meanwhile to improve the maintenance efficiency for high-speed railways (HSRs), in this paper, we present the joint sensing and communication HSR mmWave wireless network, where two mmWave beams are intelligently controlled to provide broadband communications and environment sensing, respectively. Moreover, to mitigate the inter-beam interference between communication beams and sensing beams, we propose a deep reinforcement learning (DRL) based beam management scheme, where the beamwidth and inter-beam spacing are adaptively adjusted according to dynamic wireless environments. Simulation results demonstrate that our proposed scheme can better balance the communication capacity and the sensing performance compared to conventional schemes with fixed beamwidth and inter-beam spacing.

IEEE 802.11ax standard aims to improve performance in scenarios with densely deployed access points (APs) and stations (STAs). It provides an uplink OFDMA-based random access (UORA) mechanism which allows the STAs that failed to report their butter status to the AP to transmit data, or the unassociated STAs to transmit an association request frame. Existing studies on UORA performance analysis focus on the analytical model of contention of resource units (RUs) within single basic service set (BSS), which is only in frequency domain. However, in high-density scenarios, where multiple BSSs may locate within the interference range of each other and work on the same channel, the performance of UORA not only relates with the contention in frequency domain, but also associates with the contention in time domain among multiple APs or legacy STAs working on the same channel. As the UORA mechanism is based on the successful transmission of trigger frame (TF), the transmission of TF and UORA process should be analyzed as a whole system. In this paper, we combine the transmission of TF and UORA process, and build a systematic theoretical model to practically characterize the performance of UORA in multi-BSS scenario. The average throughput and successful probability of UORA are derived. In addition, we validate our analysis through extensive simulations.

To provide various onboard entertainment services, the ever-increased Internet contents to be exchanged among remote data centers, roadside units (RSUs), and vehicles demand reliable and fast content dissemination in the vehicular networks. Edge precaching technology is expected to provide flexible and low-latency content dissemination by allowing edge nodes (i.e., RSUs and vehicles) to precache contents. However, the content dissemination process of edge precaching still suffers from high mobility and highly dynamic topology of vehicular networks. The recently proposed platoon-based vehicular network has potentials to mitigate the mobility challenges, but need to deal with multihop wireless content dissemination’s latency and reliability issues. Additionally, the network resources are limited in edge nodes, whereas various onboard Internet services with different Quality-of-Service (QoS) requirements share the same resource pool by the same network resource scheduling policy, thereby decaying the network performance. Based on the above observations, to cope with the challenging content precaching problem under diverse QoS requirements in a platoon-based edge vehicular network, we first abstract two isolated virtual content service slices with different QoS requirements based on network slicing technology to provide on-demand customized services. Then, we propose an intelligent deep reinforcement learning (DRL)-based content precaching scheme, which optimally matches the available communication resources and limited caching capacities in the edge vehicular network. The scheme jointly considers the impacts of content precaching policy and multihop wireless transmission on the content precaching performance. Simulation results show that our proposed DRL-based content precaching scheme achieves a competitive performance of reliability and latency comparing with other state-of-the-art algorithms.

In this paper, we focus on the performance tradeoff optimization problem between communication and radar in a time-division joint CommRadar system, and this promises a low-complexity hardware architecture. We ensure the communication performance in terms of time averaged data throughput and queueing delay. We guarantee radar detection opportunities in two aspects: 1) ensuring the detection performance (maximum detection range and velocity, velocity resolution) for each detection, and 2) maintaining the minimal time averaged detection chance over long term. To achieve this, we explore the communication traffic diversity to design an adaptive scheduling policy. We optimize the CommRadar mode selection, utilize the un-occupied transmission time for increasing radar detection opportunities when the data traffic is light, and balance the time allocation between communication and radar while the data traffic is heavy. We provide a quantitative performance bound for data throughput, queueing delay, and detection opportunities for our adaptive scheduling method. We verify the performance of our method through simulation. Extensive simulation results demonstrate that our method could greatly reduce the communication performance loss after integrating the radar functionality, while guaranteeing radar detection opportunities.

In this paper, we focus on designing radar detection method under the framework of IEEE 802.11ad standard. Exploring the communication frame preamble as the radar detection waveform, we configure the IEEE 802.11ad access point working in a pulse radar mode by sending multiple null data packets. However, since IEEE 802.11ad uses repeated Golay sequence as its preamble, and this would bring high side-lobe problem for radar detection. If directly applying the conventional radar detection method, it will result in high false alarm detection probability. To overcome this issue, we match the IEEE 802.11ad preamble structure, and systematically design a radar detection signal processing scheme. By optimizing the matched filter coefficient selection in the radar pulse compression step, and the reference window selection in the constant false alarm rate operation, we can significantly improve the impact of high sidelobe problem when applying the IEEE 802.11ad preamble for detection. Further, to avoid the severe signal attenuation in the mmWave band, we optimize the radar detection signal processing scheme to improve the echo signal to noise ratio (SNR). The simulation results show that our algorithm could achieve a high range and velocity estimation accuracy under the framework of IEEE 802.11ad.

In this paper, we study the throughput optimization problem in energy harvesting based cognitive Internet of Things (IoT), under cooperative spectrum sensing mode. Considering the user diversity in energy harvesting efficiency, spectrum sensing performance and data quality-of-service requirement, we optimize the harvesting-sensing-transmission tradeoff. To achieve this, we formulate it as a network-level throughput optimization problem by jointly optimizing time splitting and sensor selection. With the proposed throughput-based greedy algorithm, we first fix the sensor selection variable, and then transform the problem into an equivalent convex optimization problem. Simulation results show that our proposed scheme has great advantages in terms of secondary network throughput.

Abstract With the increase of Wi‐Fi connections and throughput, the problems posed by unreliable connection and unstable delay need to be urgently solved through advanced methods. The IEEE 802.11ax standard was designed to improve the reliability of connection and increase the throughput in dense Wi‐Fi scenario where the inter‐site interference becomes more serious and the spectral resources are more insufficient. Furthermore, in the next generation of Wi‐Fi standard IEEE 802.11be, the hybrid automatic repeat request (HARQ) and multi‐band technologies will be introduced to solve the problems of unstable delay, serious interference and insufficient spectrum resources. In this paper, retransmission schemes combined multi‐band and hybrid automatic repeat request are deeply studied to improve the retransmission efficiency. Combining hybrid automatic repeat request with multi‐band, this paper proposes three kinds of retransmission mode: insisting on the current frequency band (ICFB) retransmission, switching to the backup frequency band (SBFB) retransmission and concurrent retransmitting on dual‐band (CRDB). Then, an intelligent selection algorithm of retransmission mode based on machine learning is designed to determine the optimal mode. Theoretical analysis and experimental results show that the proposed method can greatly improve the retransmission efficiency and thereby reduce the transmission latency and increase the throughput for multi‐band Wi‐Fi networks.

While Wireless Local Area Network (WLAN) gains growing popularity during the last two decades, it faces several new challenges in meeting the requirements of emerging applications due to spectrum shortage, complicated network management and inefficient radio resource allocation (RRA) in densely deployed scenarios. The operating band has evolved from microwave band (a.k.a. sub-6 GHz) to millimeter-wave band (mmWave for short) or even multi-band to provide higher data rates. However, the existing distributed WLAN architecture works in different bands separately and doesn't support the coordination among access points (APs), making it difficult for efficient network management and improved quality of service (QoS) provisioning. In overcoming these challenges, a centralized control architecture for WLAN with decoupled control and data planes can be used to orchestrate the RRA within the whole network while achieving improved communication performance. Therefore, a new control/data plane decoupled WLAN architecture is designed in this paper to realize efficient network management and optimized RRA by separating the control plane and data plane into sub-6 GHz and mmWave respectively. Then, we propose the sub-6 GHz assisted mmWave beamforming protocol, which can significantly reduce the overhead of beamforming training (BFT). Last, we investigate joint RRA for the control/data plane decoupled WLAN since the control plane affects the RRA in the data plane. Performance analysis and simulation show that the joint RRA mechanism in the control/data plane decoupled network can significantly improve the number of successfully scheduled users and the sum-rate compared with the traditional RRA.

In this paper, we study the beam management in mmWave network from a systematic perspective. First, instead of optimizing the network performance regarding the sum-rate, we improve the beam coverage to better support user mobility, which can further reduce the beam tracking overhead. Second, to compensate for the severe signal attenuation in mmWave band, we conFigure the beam so as to satisfy the user link outage probability constraint. Third, to reduce the interference among users, we consider the inter-beam interference from both main-lobe and side-lobe. We formulate our beam management scheme as a non-linear integer optimization problem, which typically has high computational complexity. By deliberately transforming it to a geometric optimization problem and designing the rounding method, we give an optimized feasible solution with low computational complexity. We verify the performance of our beam management scheme through simulation. Extensive simulation results demonstrate that our proposed algorithm can efficiently manage the beam in mmWave network.

Network slicing, as a key 5G enabling technology, is promising to support with more flexibility, agility, and intelligence towards the provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and large-dimensioned. This contradicts the dominant network slicing solutions that only customize immediate performance over one snapshot of the system in the literature. Instead, this paper first presents a two-stage slicing optimization model with time-averaged metrics to safeguard the network slicing in the dynamical networks, where prior environmental knowledge is absent but can be partially observed at runtime. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. Therefore, we propose a learning augmented optimization approach with deep learning and Lyapunov stability theories. This enables the system to learn a safe slicing solution from both historical records and run-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, we demonstrate up to 2.6× improvement in the simulation when compared with three state-of-the-art algorithms.

Enabling completely universal coverage, the space-ground communication system integration is one of the most important aspects in the fifth generation (5G) or even the next 6G wireless communications, which significantly benefits railways whose transportation lines always cross diverse environments. Based on this observation, to achieve seamless coverage for environment-diverse high-speed railways (HSRs), by leveraging the control/user-plane (C/U-plane) decoupling and cloud radio access network (C-RAN) technologies, we propose a space-ground integrated cloud railway network consisting of space and ground cloud layers, where in the space, baseband units (BBUs) of low earth orbit (LEO) satellites are collected and centrally-managed by geostationary earth orbit (GEO) satellites. To improve the mobility support and take advantage of the stable and ultra-wide terrestrial coverage of GEO satellites, we establish an additional backup space C-plane (BS-C-plane) connection between trains and GEO satellites. Under this architecture with diverse network resources, we develop a safety-oriented resource allocation scheme based on both the resource allocation priority of safety services and the network handover costs to deliver the safety-oriented services. Simulation results demonstrate that the proposed scheme can always meet the transmission requirements for safety services in HSRs.

To support the demand of multi-Gbps sensory data exchanges for enhancing (semi)-autonomous driving, millimeter-wave bands (mmWave) vehicular-to- infrastructure (V2I) communications have attracted intensive attention. Unfortunately, the vulnerability to blockages over mmWave bands poses significant design challenges, which can be hardly addressed by manipulating end transceivers, such as beamforming techniques. In this paper, we propose to enhance mmWave V2I communications by augmenting the transmission environments through reflection, where highly-reflective cheap metallic plates are deployed as tunable reflectors without damaging the aesthetic nature of the environments. In this way, alternative indirect line-of-sight (LOS) links are established by adjusting the angle of reflectors. Our fundamental challenge is to adapt the time-consuming reflector angle tuning to the highly dynamic vehicular environment. By using deep reinforcement learning, we propose the learning-based Fast Reflection (LFR) algorithm, which autonomously learns from the observable traffic pattern to select desirable reflector angles in advance for probably blocked vehicles in near future. Simulation results demonstrate our proposal could effectively augment mmWave V2I transmission environments with significant performance gain.

Ambient backscatter communication (AmBC) enables information delivery over an ambient RF signal without carrier generation and has emerged as a promising technology to build up the self- sustainable Internet-of-Things (IoT). However, when a strong ambient signal appears, multiple backscatter nodes may initiate data transmission simultaneously, causing severe contention and wasting the precious transmission opportunity. The nondeterministic and sporadic nature of ambient signals makes it a great challenge for efficient multiple access design in ambient backscatter aided wireless network (AmBWN). Moreover, the stringent energy supply and ultra-low-cost design of the backscatter transmitter makes most multiple access schemes no longer suitable for AmBWN. To fully share carrier resources for backscattering, we resort to the non-orthogonal multiple access (NOMA) to allow multiple devices in the same regime to transmit over an ambient signal with low latency. Moreover, we propose a hierarchical multiple access scheme, which allows beamforming based spatial division multiple access among groups, and NOMA for multiple users access within a group. The evaluation result shows latency and SINR can be significantly improved with minimal overhead at the transmitter.

In future vehicular networks, to satisfy the ever-increasing capacity requirements, ultrahigh-speed directional millimeter-wave (mmWave) communications will be used as vehicle-to-vehicle (V2V) links to disseminate large-volume contents. However, the conventional IP-based routing protocol is inefficient for content disseminations in high-mobility and dynamic vehicular environments. Furthermore, the vehicle association also has a significant effect on the content dissemination performance. The content segment diversity, defined as the difference of desired content segments between content requesters and content repliers, is a key consideration during vehicle associations. The relative velocity between vehicles, which highly influences the link stability, should also been taken into account. The beam management, such as the beamwidth control, determines the link quality and, therefore, the final content dissemination rate. Based on the above observations, to improve the content dissemination performance, we propose an information-centric network (ICN)-based mmWave vehicular framework together with a decentralized vehicle association algorithm to realize low-latency content disseminations. In the framework, by using the ICN protocol, contents are cached and retrieved at the edge of the network, thereby reducing the content retrieval latency. To enhance the content dissemination rate, the vehicle association algorithm, which jointly considers the content segment diversity, the relative velocity between vehicles, and the transceiver's beamwidths, is operated in every vehicle. Considering the blindness of directional mmWave links, a common control channel operating at low frequency bands with omni-directional coverage is used to share the information related to vehicle associations. The simulation results demonstrate that the proposed algorithm can improve the content dissemination efficiency and reduce the content retrieval latency.

Millimeter-wave (mm-wave) communication has attracted increasing attention as a promising technology for 5G networks. One of the key architectural features of mm-wave is the possibility of using large antenna arrays at both the transmitter and receiver sides. Therefore, by employing directional beamforming, both mm-wave base stations (MBSs) and mm-wave user equipments (MUEs) are capable of supporting multi-beam simultaneous transmissions. However, most of the existing research results have only considered a single beam. Thus, the potentials of mm-wave have not been fully exploited yet. In this context, in order to improve the performance of short-range indoor mm-wave networks with multiple reflections, we investigate the challenges and potential solutions of downlink multi-user multi-beam transmission, which can be described as a beamspace multi-user multiple-input multiple-output (MU-MIMO) technique. We first exploit the characteristic of MBS/MUEs supporting multiple beams simultaneously to improve the efficiency of multi-user BF training. Then, we analyze the inter-user interference to avoid beam selection conflicts. Furthermore, we propose blockage control strategies and multi-user multi-beam power allocation solutions for the beamspace MU-MIMO. The theoretical and numerical results demonstrate that the beamspace MU-MIMO compared with single beam transmission can largely improve the rate performance and robustness of mm-wave networks.

The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.

The weighted K‐nearest neighbour (WKNN) algorithm is widely applied to fingerprint positioning. However, the node position estimated by the WKNN algorithm is not optimal in a noisy environment. To obtain the optimised node location estimate, the authors propose an optimal WKNN (OWKNN) algorithm for wireless sensor network (WSN) fingerprint localisation in a noisy environment. The proposed OWKNN algorithm is composed of an adaptive Kalman filter (AKF) and a memetic algorithm (MA). First, the AKF is utilised to reduce the measurement noise of the received signal strength indication (RSSI) between the nodes in the WSN. Then, the MA is employed to optimise the calibration point weight for estimating the position of a target node in the WSN according to the filtered RSSI and a calibrated radio map. Finally, an optimal node location estimate is achieved based on the optimised weight. The extensive experimental results reveal that the localisation accuracy of the proposed algorithm is at least ∼50% higher than those of the state‐of‐the‐art fingerprint localisation algorithms regardless of the placement of the target node, number of beacon nodes, and size of the calibration cell.

Wireless self-backhauling is a promising solution for dense millimeter wave (mmWave) small cell networks, the system efficiency of which, however, depends upon the balance of resources between the backhaul link and access links of each small cell. In this paper, we address the discrete power control and non-unified transmission duration allocation problem for self-backhauling mmWave cellular networks, in which each small cell is allowed to adopt individual transmission duration allocation ratio according to its own channel and load conditions. We first formulate the considered problem as a non-cooperative game G with a common utility function. We prove the feasibility and existence of the pure strategy Nash equilibrium (NE) of game ' under some mild conditions. Then, we design a centralized resource allocation algorithm based on the best response dynamic and a decentralized resource allocation algorithm (DRA) based on control-plane/user-plane split architecture and loglinear learning to obtain a feasible pure strategy NE of game G. For speeding up convergence and reducing signaling overheads, we reformulate the considered problem as a non-cooperative game G' with local interaction, in which only local information exchange is required. Based on DRA, we design a concurrent DRA to obtain the best feasible pure strategy NE of game q'. Furthermore, we extend the proposed algorithms to the discrete power control and unified transmission duration allocation optimization problem. Extensive simulations are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms.

Multi-beam concurrent transmission is one of promising solutions for a millimeter-wave (mmWave) network to provide seamless handover, robustness to blockage, and continuous connectivity. Nevertheless, one of the major obstacles in multi-beam concurrent transmissions is the optimization of beam pair selection, which is essential to improve the mmWave network performance. Therefore, in this paper, we propose a novel heterogeneous multi-beam cloud radio access network (HMBCRAN) architecture which provides seamless mobility and coverage for mmWave networks. We also design a novel acquirement method for candidate beam pair links (BPLs) in HMBCRANs architecture, which reduces user power consumption, signaling overhead, and overall time consumption. Based on HMBCRANs architecture and the resulted candidate BPLs for each user equipment, a beam pair selection optimization problem aiming at maximizing network sum rate is formulated. To find the solution efficiently, the considered problem is reformulated as a non-operative game with local interaction, which only needs local information exchanging among players. A decentralized algorithm based on HMBCRANs architecture and binary log-linear learning is proposed to obtain the optimal pure strategy Nash equilibrium of the proposed game, in which a concurrent multi-player selection scheme and an information exchanging protocol among players are developed to reduce the complexity and signal overheads. The stability, optimality, and complexity of the proposed algorithm are analyzed via theoretical and simulation method. The results prove that the proposed scheme has better convergence speed and sum rate against the state-of-the-art schemes.

Fingerprint localisation technology using received signal strength indication (RSSI) has become one of the hot spots in the research field of indoor positioning based on wireless sensor networks (WSNs). Due to the presence of the measurement noise of the RSSI, the weight of the calibration point in the current fingerprint positioning algorithm is not optimised. The authors propose a fingerprint localisation algorithm for noisy WSNs based on an innovative multi‐objective evolutionary model. The proposed algorithm at first employs the Kalman filter to filter the abnormal RSSI value, and then utilises a noise covariance estimator to perceive the noise covariance of the RSSI. Finally, the multi‐objective evolutionary model is used to search for the optimised weight of the calibration point via the filtered RSSI and the perceived noise covariance. That the novel evolutionary model can find the best fingerprint estimate with the optimised weight has been proven theoretically in this work. Extensive experimental results on an off‐the‐shelf WSN testbed show that the authors’ proposed algorithm improves the accuracy of the state‐of‐the‐art fingerprint positioning algorithm by at least 50% regardless of the placement of the target node, the number of beacon nodes, the size of the calibration cell, and the number of nearest neighbours.

Fingerprint localisation technology based on received signal strength indication (RSSI) is an active area of study in wireless sensor network (WSN) research, mainly because it is cheap and easy to implement. However, due to measurement noise and the multipath effect in RSSI, the accuracy of state‐of‐the‐art fingerprint positioning algorithms is diminished. To solve this problem, the authors propose a multi‐channel fingerprint localisation algorithm for WSNs in multipath environments. This algorithm also addresses the issue of existing techniques based on multi‐channel signal strength requiring an accurate initial estimate of target–beacon distance or prior knowledge of the target–reference distance. Their proposed algorithm first uses an adaptive Kalman filter to reduce the noise in RSSI measured in different channels, and then calculates the matched fingerprint according to the weight of different channels. Finally, a memetic algorithm is utilised to generate the optimised estimate of fingerprint and location. Extensive experimental results on an actual WSN testbed show that the proposed algorithm improves the accuracy of the existing fingerprint localisation algorithm by at least 50%, regardless of the placement of target node, the number of beacon nodes, and the number of calibration points.

Unlicensed spectra access holds the promise of alleviating licensed spectra scarcity and providing super high-rate mobile data services, which has been viewed as one of the key technologies of fifth generation (5G) cellular networks. In this paper, we first design a framework for 5G licensed and unlicensed spectra interoperable networks based on the cloud radio access network technology and the control/data decoupled architecture. Then, we investigate network-level cost-reliability tradeoff from two aspects. First, we study a fundamental tradeoff between the cost and the reliability by minimizing cost for a given reliability level. Second, we define a quality of experience efficiency utility as the complementary measure to characterize the cost and the reliability, offering an inherent tradeoff between them. Moreover, an interference power estimation method is proposed to more accurately estimate channel states to guarantee resource allocation effectiveness. Finally, we conduct extensive simulation study and demonstrate the effectiveness of the interference power estimation method.

For future networks [i.e., the fifth generation (5G) wireless networks and beyond], millimeter-wave (mmWave) communication with large available unlicensed spectrum is a promising technology that enables gigabit multimedia applications. Thanks to the short wavelength of mmWave radio, massive antenna arrays can be packed into the limited dimensions of mmWave transceivers. Therefore, with directional beamforming, both mmWave transmitters (MTXs) and mmWave receivers (MRXs) are capable of supporting multiple beams in 5G networks. However, for the transmission between an MTX and an MRX, most works have only considered a single beam, which means that they do not make full potential use of mmWave. Furthermore, the connectivity of single beam transmission can easily be blocked. In this context, we propose a single-user (SU) multi-beam concurrent transmission scheme for future mmWave networks with multiple reflected paths. Based on spatial spectrum reuse, the scheme can be described as a multiple-input multiple-output (MIMO) technique in beamspace (i.e., in the beam-number domain). Moreover, this paper investigates the challenges and potential solutions for implementing this scheme, including multi-beam selection, cooperative beam tracking, multi-beam power allocation, and synchronization. The theoretical and numerical results show that the proposed beamspace SU-MIMO can largely improve the achievable rate of the transmission between an MTX and an MRX and, meanwhile, can maintain the connectivity.

Previously, we proposed a broadband fifth-generation (5G) control/user (C/U)-plane decoupled heterogeneous railway wireless system to meet the exponentially increasing capacity demands in railway. Nevertheless, service interruptions due to the handovers in overlapping registration areas are still challenging in railway wireless systems. How to achieve soft and fast handovers to overcome these challenges attracts intensive attention finally. In this paper, we propose a novel network architecture for 5G C/U-plane decoupled heterogeneous railway wireless systems to physically separate and stagger the C-plane and U-plane handovers. During the C-plane handover process that occurs between the macro-cells, the U-plane is always kept connected without any handover, which achieves non-interruptible soft handover. Moreover, since there are no handovers in the U-plane, the handover procedure is significantly simplified, thereby accelerating the handover process. Similarly, during the U-plane handover process, which happens between the small cells, the C-plane is kept connected all the time, saving the otherwise intensive C-plane handover signaling. Besides, the coordinated multi-point transmission and reception (CoMP) and bi-casting technologies are applied to establish the target U-plane connection ahead of cutting off the old one, so that no interruption occurs during the U-plane handover process. This paper demonstrates that the proposed handover schemes can greatly improve the handover performance.

Previously, we have proposed a C/U-plane (Control/User plane) decoupled railway wireless network in which higher frequency bands are adopted by small cells to provide wider available spectra for the U-plane of passengers' services. In order to guarantee reliable connectivity to wayside eNodeBs (eNBs), an onboard mobile relay (MR), consisting of two components, namely, MR-UE (User Equipment) and MR-AP (Access Point), is employed to forward passengers' services over backhaul links between MR-UE and wayside eNBs. The remaining problem here is how to utilize spectra effectively and efficiently under this new configuration. Since a given wayside eNB hosts only one single accessed user most of the time, we design an additional uplink scheduler for the MR-UE to self-manage the usage of uplink resources, avoiding the complicated uplink grant procedure commonly used in the conventional cellular systems. Moreover, we develop eNB schedulers to coordinate the spectra in small cells. To deal with occasional multi-user scenarios, we propose an uplink scheduler switcher for the macro cell to judge which uplink scheduler should be activated. Furthermore, an uplink resource allocation scheme with high spectrum efficiency is deliberately designed for the new dual-scheduler configuration. Finally, we carry out theoretical analysis and numerical simulations to demonstrate the effectiveness of our proposed scheme.

As the low frequency band has become more and more crowded, millimeter-wave (mmWave) has attracted significant attention. The IEEE has released the 802.11ad standard to satisfy the demand of ultra-high-speed communication. It adopts beamforming technology that can generate directional beams to compensate for high path loss. In the association beamforming training (A-BFT) phase of BF training, a station (STA) randomly selects an A-BFT slot to contend for training opportunity. Due to the limited number of A-BFT slots, the A-BFT phase suffers high probability of collisions in dense user scenarios, resulting in inefficient training performance. Based on the evaluation of the IEEE 802.11ad standard and 802.11ay draft in dense user scenarios of mmWave wireless networks, we propose an enhanced A-BFT beam training and random access mechanism, including the separated A-BFT (SA-BFT) and secondary backoff A-BFT (SBA-BFT). The SA-BFT can provide more A-BFT slots and divide the A-BFT slots into two regions by defining a new E-A-BFT Length field compared with the legacy 802.11ad A-BFT, thereby maintaining compatibility when 802.11ay devices are mixed with 802.11ad devices. It can also greatly reduce the collision probability in dense user scenarios. The SBA-BFT performs secondary backoff with very small overhead of transmission opportunities within one A-BFT slot, which not only further reduces collision probability, but also improves the A-BFT slots utilization. Furthermore, we propose a 3-D Markov model to analyze the performance of the SBA-BFT. The analytical and simulation results show that both the SA-BFT and the SBA-BFT can significantly improve BF training efficiency, which is beneficial to the optimization design of dense user wireless networks based on the IEEE 802.11ay standard and mmWave technology.

Sparse code multiple access (SCMA) is a non- orthogonal multiple access scheme, which has been considered as one of the key 5G technologies to improve spectral efficiency. Moreover, device-to- device (D2D) communication is introduced as a vital technology component for 5G cellular communication system to improve spectral reuse. D2D communication combined with SCMA will be a significant part of 5G technology to support higher spectral efficiency. However, these benefits depend on efficiently allocating SCMA layers for cellular users and D2D pairs to share corresponding spectrum resources. In this paper, a mapping scheme of users to SCMA layers for D2D communication is proposed to maximize the system sum rate, in which after assigning SCMA layers to cellular users, a coalition game based scheme is proposed to obtain the solution for mapping of D2D pairs to SCMA layers. Simulation results validate the effectiveness of the proposed scheme and show that the proposed scheme outperforms other schemes, and the D2D communication combined with SCMA can obtain significant gain over the one in LTE networks in terms of system sum rate.

Train passenger communication services have gained more and more attention due to the enormous growth of multi-media services, as well as the development of high-speed railway. In order to deal with the high outage possibilities caused by penetration loss and signaling storm of group handover when communicating with the wayside cellular network directly. The High-Speed Train (HST) WiFi network, which provides passengers` information access within the carriages, emerges at the moment. However, there are still some issues which are not well addressed. With more users accessing to the WiFi network, the onboard relay connecting both of wayside cellular network and carriage WiFi network becomes a bottleneck, which significantly affects the network performance. Furthermore, by abstracting each carriage as a sub-network, the HST WiFi network can be seen as a tandem multi-hop network which will raise other challenges such as the end-to-end error accumulation and the bandwidth progressive loss carriage by carriage. In this paper, we obtain the arrival process of each sub-network by using a decomposition method and a parameter fitting algorithm. Then, we model the sub-network as a discrete-time finite capacity queuing system with packets splitting by a Markov Modulated Bernoulli Process (MMBP)/G/1/K queue. In addition, we use Hybrid Automatic Repeat Request (HARQ) in each sub- network to relieve the end-to-end error accumulation. Through the analysis of the stationary queue length distribution, average delay and packet loss probability, we obtain the optimal buffer size to ease the traffic congestion and give some suggestions on resource allocation of HST WiFi network.

With large available continuous bandwidth, millimeter wave (mmWave) bands hold promise as a carrier frequency for fifth generation (5G) wireless communications. Moreover, mmWave bands also play an important role in radar detections. Based on this observation, we propose an mmWave wireless communication and radar detection integrated network architecture for railways, not only to increase the capacity of railway wireless communication systems, but also to realize train operation environment detection to enhance train operation safety. To overcome the aggravated path loss in mmWave bands, directional beamforming is generally used to concentrate signal radiation energy both in wireless communications and radar detections. Nevertheless, for wireless communications, the signaling blind zone of directional beamforming makes it less effective in wireless link establishment and maintenance. Therefore, in this proposed integrated network, two frequency bands are employed, where omnidirectionally radiated licensed lower frequency bands carry critical signaling and data, and mmWave bands are time-division multiplexed to transmit large-volume communication data for trains, or to perform environment detection for enhancing train operation safety. To mitigate handovers and to increase baseband processing resource utilization in railway scenarios, the proposed integrated network is deployed based on the cloud radio access network (C-RAN) architecture, where both licensed lower frequency band radio remote units (RRUs) and mmWave band RRUs are connected to a building baseband unit (BBU) pool through high-speed backhauls. Besides, physical (PHY) frame structures and up/downlink communication signaling procedures are designed for this proposed integrated network. Performance analysis results have demonstrated that the proposed integrated network can highly increase the capacity for railway wireless communication systems and achieve high distance and angular resolution for radar detections.

The control/user (C/U) plane decoupled railway wireless network is an innovative architecture recently proposed to meet the communication demands of both train control systems and onboard passengers. The core idea is to completely separate the C-plane and the U-plane into different network nodes operating at different frequency bands. Although the system capacity of this network architecture can be highly increased, the forwarding latency of X3 interfaces to link the C-plane and the U-plane becomes a serious problem, particularly for hybrid automatic repeat request (HARQ) protocols that demand frequent interactions between the C-plane and the U-plane. To address this challenging problem, we propose a low-latency collaborative HARQ scheme in this paper. Specifically, we develop a new collaborative transmission framework where the possible spare resources on lower frequency bands of macrocells excluding those used by C-plane transmissions can be utilized to help small cells relay erroneously received data. Compared with the conventional HARQ scheme, the proposed scheme requires fewer retransmissions to reach the same transmission reliability, thereby mitigating the latency problem caused by HARQ retransmissions. Furthermore, channel mapping is also redesigned to conform to the proposed collaborative transmission framework. Through theoretical analysis, we derive the expression of the average number of retransmissions related to the sum of independent Gamma variables. Finally, the results of simulation experiments show that the proposed scheme can largely decrease the retransmission latency for railway wireless networks.

Unlicensed spectra fusion technology for LTE holds the promise of alleviating the licensed spectra scarcity and enhancing capacity. It allows LTE to effectively utilize the unlicensed spectra distributed over high frequency bands with significant different propagation characteristics from its licensed spectra. However, the interference caused by other systems over unlicensed spectra, particularly the public unlicensed spectra, is viewed as the most serious challenge. In this paper, aiming to guarantee the feasibility in existing LTE systems, we design a novel unlicensed spectra fusion scheme based on the popular standard TDD-LTE. To mitigate the interference, we develop an interference coordination scheme which is carried out in two stages: screen the available unlicensed channels for every UE, and allocate unlicensed spectra based on Hungarian algorithm. We have conducted extensive simulation study and demonstrate that our proposed scheme can handle interference coordination effectively and enhance throughput significantly.

Full-duplex, massive antenna and higher frequency bands are core technologies of the fifth generation (5G) wireless communications to boost the throughput to satisfy the future explosively increasing demands of traffic volume. Unfortunately, the implementation of full-duplex is badly limited by the severe self-interference. In this paper, we propose a novel quasi-full-duplex scheme on the basis of separate transceiver arrangement and massive antenna beamforming to achieve future high reliable and efficient professional railway wireless communication systems. To obtain accurate direction of arrival (DOA) of the desired user for beamforming, real-time feedback link and novel reference signal (RS) distribution are designed to adapt to high-speed scenario. The theoretical analysis and numerical simulations demonstrate that the proposed scheme can dramatically improve the spectrum efficiency and transmission reliability, except for the scope nearby the eNodeB where the receiver of eNodeB is overwhelmed by its local signal. The complementary method that employs power control and higher frequency bands has settled this problem entirely.

The high mobility of high‐speed train brings great challenges to the current wireless communication, especially to the handover. How to reduce the interrupt time and enhance the handover performance is becoming a serious problem for high‐speed rail (HSR). In this study, a coordinated of multi‐point (CoMP) and bi‐casting joint soft handover scheme for HSR is proposed, which is based on long‐term evolution network architecture, and without any change to the existing network and the train. In the scheme, CoMP is used on air interface and bi‐casting is used on S 1 interface. With the binding triggering of CoMP and bi‐casting, only one suit of signalling is needed and the system complexity is much simplified. Meanwhile, as much handover related information is stored in corresponding entity during the joining process of CoMP and bi‐casting, the handover process is much simplified and converted into update of serving eNodeB. Compared with traditional handover scheme, it can be seen from theoretical analysis and simulation results that the handover delay is reduced remarkably and the handover performance is improved greatly.

Multihop cellular networks (MCNs) have drawn tremendous attention due to its high throughput and extensive coverage. However, there are still three issues not well addressed. With the existence of relay stations (RSs), how to efficiently allocate frequency resource to relay links becomes a challenging design issue. For mobile stations (MSs) near the cell edge, cochannel interference (CCI) become severe, which significantly affects the network performance. Furthermore, the unbalanced user distribution will result in traffic congestion and inability to guarantee quality of service (QoS). To address these problems, we propose a quantitative study on adaptive resource allocation schemes by jointly considering interference coordination (IC) and load balancing (LB) in MCNs. In this paper, we focus on the downlink of OFDMA-based MCNs with time division duplex (TDD) mode, and analyze the characteristics of resource allocation according to IEEE 802.16j/m specification. We also design a novel frequency reuse scheme to mitigate interference and maintain high spectral efficiency, and provide practical LB-based handover mechanisms which can evenly distribute the traffic and guarantee users' QoS. Our study shows that our scheme not only meets the requirement on coverage, but also improves the throughput while accommodating more users in MCNs.

Voice over IP (VoIP) in LTE-R (LTE for Railway) will replace the circuit-switched (CS) voice service of the GSMR (GSM for Railway). However, it will face new problems in high speed railway scenario. In overlapping areas, the handover signaling (i.e. handover measurement and decision) is of higher priority than Hybrid Automatic Repeat reQuest (HARQ) retransmission so that the HARQ retransmission will be canceled when conflicts happen. If an user equipment (UE) is handed over to the target eNodeB (eNB), the HARQ retransmission or ACK/NACK of source eNB will not be received by the UE as normal, and the UE will wait for the potential retransmission and ACK/NACK. These particular problems will increase the VoIP packet error rate (PER) significantly. In order to solve these problems, a position assisted coordinate HARQ scheme in LTE system for high speed railway is proposed. With the help of trains position, the source eNB and target eNB share packets through coordination. If the handover is triggered, the UE will stop sending ACK/NACK. When the handover is completed, the UE sends the ACK of the last continuous packet to ensure lossless handover. Simulation results show that our proposal can reduce the VoIP PER in high speed railway scenario, and improve the system reliability, as well as VoIP capacity.

Admission control in Wireless mesh networks (WMNs) and Wireless local area network (WLAN) plays an important role in achieving fair resource schedule and load balance. Both Stations (STAs) and mesh AP (MAP) encounter the problem that how to select a proper access point in the access process. This paper addresses the problem of admission control game from the perspective of the stations and the service provider, and formulates the admission control process between STA and MAP as a nonzero-sum, non-cooperative and mixed strategy game. Three decisive factors and mixed strategy probability function for MAP and STA are defined. The calculated steps of the weight value for each decisive factor using Analytic Hierarchy Process (AHP) are discussed.

In recent years, the wireless operators' revenue touches the ceiling, and the serious asymmetry growth of flow and revenue is facing the biggest challenge. For the interest of network operators, the pricing, user demand and the interaction between system utility and the wireless operators' revenue are investigated, and a utility-based dynamic pricing scheme for improving network operator's revenue is proposed, where the relative optimization between the network operator's revenue and the system total utility can be reached. According to the proposed pricing scheme, operators can adjust system total utility to improve its revenue in the given interval range. The numerical analysis results indicate that this scheme is reasonable and feasible.

Recently, improving energy efficiency has been a new tendency in wireless relay communication. However, the introduction of relay technology may increase the system capacity, but is often followed by a more severe energy‐efficiency problem when compared with traditional single‐hop system. In this study, based on orthogonal frequency division multiplexing‐based frequency‐selective channel of amplify‐and‐forward relay link, a suboptimal two‐step power allocation algorithm of subcarriers is proposed to maximise the energy efficiency. The proposal includes two steps. First, a suboptimal subcarrier matching is achieved for introducing a virtual direct link, which may reduce the dimensionality of the power allocation. Second, the allocation problem of relay network is simplified as that in traditional cellular network. Thus, the optimal power allocation of virtual direct link can be obtained by one‐dimensional search method. Furthermore, the effects of rate and power constraints on the energy‐efficiency problem are analysed. Simulation shows that the suboptimal solution of the proposed algorithm is very close to the optimal solution, and the proposed energy efficient power allocation method can achieve the highest energy efficiency, compared with the rate adaptive and margin adaptive optimisations.

By incorporating relay technologies into cellular systems, multi-hop relay networks (MRNs) can provide higher throughput and wider coverage, but it is impossible to guarantee quality of service (QoS) requirements for users at the cell edge and in hot spots due to severe co-channel interference (CCI) and load imbalance. This paper proposes an adaptive resource allocation scheme with a joint consideration of interference coordination (IC) & load balancing (LB), and analyzes the downlink of orthogonal frequency division multiple access (OFDMA) based MRNs with time division duplex (TDD) mode. We present a novel frequency reuse scheme for MRNs to mitigate CCI and maintain high spectral efficiency. We also provide practical LB-based handover mechanisms to evenly distribute users and guarantee the users' QoS. Extensive simulations demonstrate that our scheme not only satisfies the requirement of coverage probability, but also improves the throughput and accommodates more users in MRNs.

With the expansion of the railway industry and the upgrade of railway speed, the reliability and efficiency of wireless communication systems in high speed railway attract the public attention. The high speed brings about more severe Doppler Effect, the postponement of handover triggering location and more frequent handover. Therefore the influence caused by the high speed decreases the reliability of wireless communication network in high speed railway. In this paper, a scheme based on the train position information and relay power control has been proposed to optimize the system handover performance. When the train is at the handover point, by power control the relay generates an enhanced signal to meet the minimal communication requirement. The results show that the proposed scheme increases the handover success probability effectively, thus improves the reliability and efficiency of wireless communication systems in high speed railway.

In current multi-hop relay networks (MRNs), normally the communication path is not optimized, especially when two mobile stations (MSs) attached to the same relay station (RS) need to communicate with each other. In this paper, a novel data transmission scheme: administrable and controllable local forwarding for OFDMA-TDD based two-hop relay cellular networks, is proposed. In the local forwarding mode, data transmission between two MSs attached to the same RS is forwarded without necessarily being sent to the base station (BS). Thereby the spectral efficiency is improved. And then two cost based relay selection schemes (RSSs) are proposed for the efficient usage of local forwarding mode. The first is a modified traditional RSS (RSS-MT), which implements local forwarding after relay selection. And the second is a local forwarding-based RSS (RSS-LF), which takes the advantages of the local forwarding into account during relay selection. Numerical and simulation results show that the local forwarding transmission schemes can improve the system capacity greatly. Moreover, the performance of RSS-LF is shown to be superior to that of RSS-MT.

Frequency reuse scheme is extremely important in multi-hop relay networks (MRNs), because some extra frequency resource should be elaborately allocated to Relay Stations (RSs). However the time and spatial reuse characteristics of RS should be taken into account in order to mitigate the co-channel interference. This paper presents two adaptive frequency reuse schemes for MRN by using different reuse patterns in different time zones of the same frame. In our schemes, reuse pattern of 1×3×1 is applied in the Relay Zone (RZ), and 1 × 3 × 3 is applied in the Access Zone (AZ). The former allows only the two RSs which are located farthest apart in one cell to share the same frequency portion; while in the latter two adjacent RSs may share the same resources. Analysis and simulation results show that both schemes improve the system throughput greatly. The comparison of coverage and throughput between the two schemes indicates that the latter scheme has better performance and an aggressive full reuse scheme could be utilized to all RSs.

Orthogonal frequency division multiple access (OFDMA)-based multi-hop cellular networks, benefited from the relay and OFDM technologies, have a lot of advantages over traditional wireless cellular networks including better network coverage via relay station (RS), the flexibility and adaptation in radio resource allocation. In this paper, the radio resource allocation for wireless relay links in OFDMA-based multi-hop cellular networks is investigated. The topology of an OFDMA-based multi-hop cellular network is described as a hierarchical tree configuration, upon which a throughput-oriented objective function is formulated and the constraints including link proportional fairness are imposed. In order to maximize throughput with a low complexity, a suboptimal algorithm is proposed to optimize allocation of subcarriers and related powers. Simulation results show that when compared to fix resource allocation schemes such as OFDM-time division multiple access (TDMA), the proposed algorithm can significantly improve the network capacity while maintain the link proportionality of the network.

An admission control algorithm should be properly designed to guarantee the quality of service (QoS) in wireless mesh networks (WMNs). Based on channel business ratio, an admission control algorithm (ACA) is proposed to provide QoS for realtime and non-realtime traffic. For realtime traffic, all the nodes on a route make the admission control decision based on the estimation of available bandwidth. For non-realtime traffic, a rate adaption algorithm is proposed to adjust the sending rates of the source nodes to prevent a network from entering a saturated status. Finally, we demonstrate the effectiveness by simulations in NS-2.

Recent studies indicate that by improving the spatial reuse ratio the throughput of 802.11 wireless networks can be improved. In this paper, we study the impact of physical carrier sensing and channel rate on the throughput of 802.11 wireless networks with chain topology. Firstly, this paper propose

In this paper we introduce a methodology to predict IP traffic in IP-based next generation network (NGN). By using Netflow traffic collecting technology, we've collected some traffic data for the analysis from an NGN operator. To build wavelet basis neural network (NN), we replace Sigmoid function with the wavelet in NN, and use wavelet multiresolution analysis method to decompose the traffic signal and then employ the decomposed component sequences to train the NN. By using the methods, we build a NGN traffic prediction model by which to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN (WNN) is more accurate than that without using wavelet in the NGN traffic forecasting.

The proliferating demands for mobile services propel us to develop more sophisticated techniques to overcome the limitation of the wireless radio resource. With the introduction of cell splitting and sectorization in cellular communications systems, system capacity can be significantly increased. Space division multiple access (SDMA) technology along this line has therefore gained a great deal of attention recently; intensive research on resource allocation for SDMA systems has been undertaken in the last few years. We carry out a more realistic performance analysis for a few resource allocation schemes in SDMA systems and obtain more accurate analytical results for blocking probability.