Publisher Copyright: © 2026 IEEE.

Vehicular Edge Computing (VEC) is an essential part of the Internet of Vehicles (IoV) due to its low latency by moving the computational resources close to the edge. Although the introduction of network slicing into VEC improves resource utilization through dynamic resource allocation based on real-time demands and priorities, it increases the deployment and operational costs. In view of this, this paper envisions a resource allocation strategy for VEC based on network slicing technique, in which the tasks involved are not only dynamic but also heterogeneous. To minimize the system cost (including resource consumption and computation, network slice maintenance and reconfiguration costs), this paper proposes CST-RL, a confidence-based self-adjusting two-timescale reinforcement learning algorithm. This solution performs resource allocation and activation scheduling for network slices on a large timescale, while allocating slices to heterogeneous tasks on a short timescale to meet dynamic demands. In addition, we innovatively utilize critic in reinforcement learning to predict and compare the expected benefits of network slices with versus without reconfiguration. We introduce the Random Network Distillation (RND) technique to assess the confidence level of these benefits, thus providing guidance for network slices to automatically decide whether and when to undergo reconfiguration. Finally, we demonstrate the effectiveness and superiority of CST-RL through simulations. Results show that CST-RL yields 27.77% lower system cost compared to the scheme without network slicing and 15.15% lower system cost compared to performing constant network slicing configuration, with guaranteed Quality-of-Service.

The integration of intelligent reflective surfaces (IRS) on unmanned aerial vehicles (UAVs), termed UAV-IRS, to bolster wireless communications has emerged as a hotspot of academic research and industrial application. In this paper, we investigate the problem of secure communication in the harsh communication environment assisted by multiple UAV-IRSs, where the UAV-IRSs act as relays to assist the downlink secure communication between the base station and the users. To maximize the security sum rate between the base station and the users, the trajectory planning and phase shift design of multiple UAV-IRS needs to be jointly optimized. To solve this complex non-convex optimization problem, we introduce a distributed collaborative optimization scheme for multiple UAV-IRSs called credit-aware cooperative multi-agent reinforcement learning (MARL), which takes MARL as the base algorithm, and then solves the credit allocation problem among multiple UAV-IRSs by using cooperative game theory to facilitate exploration, and finally constrains non-cooperative behaviors among UAV-IRSs by using the primal-dual optimization algorithm to promote cooperation. Finally, the effectiveness and superiority of the proposed scheme is verified by comprehensive simulation experiments.

Aerial-aided Edge Computing (AEC) promises to provide low-latency computing services as a critical component for future low-altitude intelligent transportation systems. However, the complex edge network environment poses security challenges, as traditional Intrusion Detection Systems (IDS) struggle to handle AEC’s large traffic volume and resource constraints. To address this, we propose a collaborative detection mechanism called Switch IDS. First, Switch IDS adopts a packet-level detection solution to meet real-time detection requirements. Next, Switch IDS is designed for resource-constrained nodes. By introducing the Mixture of Experts (MoE) structure into the traditional Deep Learning (DL) model, it enables seamless and scalable multi-node deployment. Switch IDS establishes a resource status-based capability for each Expert and incorporates steering loss in the loss function to enable autonomous near-optimal traffic steering, ultimately maximizing system processing capacity. Finally, the Switch IDS utilizes parallelized Service Function Chaining (SFC) for practical multi-node deployment. To the best of our knowledge, this is the first realization of multi-node deployment and autonomous traffic steering for DL-based IDS. Experiments on public datasets show that Switch IDS and its multi-node deployment scheme notably boost processing capacity while maintaining high detection performance.

A single-fiber forward transmission distributed vibration sensor (SF-FTDVS) is proposed in this article based on bi-directional structure, which breaks the dependence of traditional FTDVS on the interference loop structure or the multifiber optical path. The proposed SF-FTDVS inherits the advantages of fiber sensors with forward transmission. It can realize the relay-free distributed vibration sensing greater than 210 km since the continuous forward transmission of light waves provides good signal-to-noise ratio (SNR) performance. Besides, the bidirectional transmission structure enhances the consistency of vibration event perception, so that it can achieve a more precise location. Experiments demonstrated the feasibility of vibration event location with a wide-frequency range from 400 Hz to 10 kHz, allowing for an un-repeated sensing link up to 212 km. To the best of our knowledge, this represents the longest perception distance achieved by a relay-free sensor. The average location standard deviation (STD) and the location fluctuation range are estimated to be 15.7 m and ±37 m, respectively, demonstrating an improvement of 14% and 12% compared to the traditional distributed vibration sensors (DVS) scheme. The proposed SF-FTDVS not only offers the advantages of simple deployment, high-positioning accuracy, and wide vibration response bandwidth, but also exhibits good compatibility with other components, demonstrating great potential for applications in Internet of Things (IoT) systems.

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task; ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder.

Uncrewed aerial vehicle (UAV)-assisted integrated sensing, computation, and communication (ISCC) network enables the entire data analysis process for practical applications. The existing works of UAV-assisted ISCC merely consider a single data source, and there still exist gaps in the collection of environmental data via multiple sources. Motivated by this, we envision a novel UAV-assisted air-ground collaborative ISCC network that fully explores the cooperation between aerial UAVs and ubiquitous ground Internet of Things (IoT) devices. To achieve effective, efficient, and fair joint task scheduling and resource allocation, an optimization is established to minimize two novel indicators, i.e., computation offloading and sensing penalty indices, subject to constraints of boundary, anti-collision, and UAV energy consumption. To tackle this problem, a deep reinforcement learning (DRL) framework is proposed where three advanced DRL algorithms are included under centralized and decentralized control schemes. In former scheme, the central controller makes globally optimal decisions. In latter scheme, multiple agents decide independently based on local information. We demonstrate a forest fire monitoring use case simulated in a national forest park. Results show the mutually interfering, competitive, and beneficial relationships among triple functionalities. Besides, our solution outperforms three state-of-the-art baselines in terms of effectiveness and efficiency.

Intelligent Transportation Systems (ITS) urgently require real-time data collection with minimized Age of Information (AoI), yet face critical challenges from high-dynamic traffic environments and unstable wireless channels. By virtue of the low deployment cost and the high-speed mobility, Uncrewed Aerial Vehicle (UAV) bring us a feasible approach to the aforementioned problem. Nevertheless, such a problem is far from trivial due to lot of factors ranging from the highly dynamic communication environment, the dimension-varying input/output for each UAV, to the extremely large solution space for all the UAVs as a whole in a distributed collaborative manner. Although existing Multi-Agent Reinforcement Learning (MARL) solutions are widely used to address the above challenges, they all rely on fixed-dimensional input/output processing (e.g., padding/truncation strategies), leading to redundancy or loss of information due to dimensionality-changing scenarios. To address this gap, we proposed an improvement scheme based on attention-driven MARL, which redesigns the policy and critic network based on the attention mechanism to help UAVs extract critical information from dimension-varying data in an accurate and efficient manner. Finally, we verify the superiority and robustness of the proposed scheme through multiple sets of experiments with multiple different aspects. The simulation results show that the proposed scheme is scalable and efficient, and the weighted average AoI under different scenarios is lower than the existing state-of-the-art schemes by$13.1\%\sim 56.2\%$.

Integrated sensing and communications (ISAC) is emerging as one of the six application scenarios for future wireless networks. Characterizing the Pareto boundary is an urgent issue in multiple-input multiple-output (MIMO) ISAC systems. The lack of unified sensing metrics and the neglect of the instantaneous worst-case sensing requirement in the existing works present challenges to this issue. In this paper, we propose a more universal and operable theoretical limit analysis framework where the high-signal-to-noise ratio (SNR) channel capacity is characterized under instantaneous covariance mismatch constraint. We use the covariance mismatch that implies the distance to optimal covariance as the sensing metric. The optimal covariance can be computed by optimizing any key sensing metric. An MIMO ISAC Pareto boundary can be obtained by computing channel capacity under fine-grained sensing thresholds, below which the mismatch must be constrained. In the experiments, three radar modes are considered, and the results show that different radar modes affect capacity performance and a trade-off exists between communication and sensing. In addition, pure communication capacity is the upper bound of the communication capacity in ISAC. Moreover, capacity under instantaneous constraint approaches that under average one in pure MIMO communications when signal length approaches infinity.

Simultaneously transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) can assist in achieving full-space signal coverage enhancement. Considering eavesdropping channels, a downlink system model with full coverage of STAR-IRS enabled unmanned aerial vehicle (UAV) secure communication is proposed. The aim is to attain the maximal value of the energy efficiency (EE) by exploring the joint resource allocation of the system. To solve this coupling problem, the lower and upper bounds of the sum-rate for legitimate and eavesdropping users are derived respectively, and Lagrange duality theory is employed to deal with the power control problem. Then the reflection/transmission amplitude splitting coefficient optimization of STAR-RIS using the Hybrid whale-bat (HWB) method in energy splitting (ES) mode are considered to fully exploit the performance gains brought by STAR-IRS deployment. Finally, the simulation verifies that the proposed joint design scheme can enormously promote the EE and safety performance of the system.

Human-centric Metaverse services requires novel communication and networking technologies to achieve seamless connectivity for Metaverse users. Reconfigurable intelligent surface (RIS) in 5G and beyond networks can provide highly reliable communication connections, superior user quality of service (QoS), seamless user connections, and extensive signal coverage for Metaverse. Deploying RIS in unmanned aerial vehicle (UAV) networks for Metaverse can enormously improve the signal propagation environment and human-centric communication experiences. Considering the channel uncertainty of the air-ground cascade communication link in Metaverse, an RIS-aided multi-UAV cross-layer network system is proposed. Under the cross-tier interference limitation and the rate outage probability constraint, the system EE improved by maximizing the minimal energy efficiency (EE) of UAV units. Different from the existing RIS schemes, which suffer from the significant channel acquisition cost or power consumption, this paper first proposes a topology design scheme of irregular RIS, which Metaverse user only connects a few RIS elements to obtain high EE. Secondly, with the imperfect cascade channel state information (CSI) error model, the rate outage probability constraint is approximated by Bernstein type inequality to enhance the seamless human-centric connectivity service. Hence a low complexity scheme is invoked to co-design the power control parameter at the UAV transmitter and RIS reflecting phase. Finally, affluent simulation curves verify that the irregular RIS controller deployment combined with low power loss topology design and low-complexity phase shift design contributes to improve human-centric QoS for Metaverse service.

Mobile edge computing (MEC) has attracted widespread attention due to its ability to effectively alleviate the cloud computing load and significantly reduce latency. However, the potential eavesdroppers challenge the security of the MEC systems and the rapid development of artificial intelligence (AI) has made this security situation more severe. In most existing studies, the eavesdroppers are non-intelligent and it is assumed that they are fixed or move in a simple manner. Obviously, there is a gap from such an assumption to the real conditions that the eavesdropping unmanned aerial vehicles (UAVs) may adjust their flight paths intelligently. To better reflect real-world scenarios, we consider a multi-UAV-assisted MEC system in the presence of intelligent eavesdroppers and propose an adversarial multi-agent reinforcement learning (MARL)-based scheme for secure computational offloading and resource allocation. With this scheme, we aim to solve the zero-sum game between the legitimate UAVs and the eavesdropping UAVs, in which the two types of UAVs take turns acting as the agents of MARL to alternately optimize their respective opposing objectives. The simulation experimental results indicate that the proposed scheme significantly outperforms the existing baseline methods in dealing with the intelligent eavesdropping UAVs, and ensures high energy efficiency of Internet of Things (IoT) devices even in the worst-case scenario when dealing with potential eavesdropping threats.

Active reconfigurable intelligent surface (RIS) is deemed a prospective candidate to compensate the double fading attenuation caused by the passive RIS, where each element can reflect and amplify the received signals through its low power integrating amplifiers. This paper investigates the physical layer security (PLS) for a non-orthogonal multiple access (NOMA) system through a deployed active RIS to defend multiple eavesdroppers (Eves), where the practical discrete RIS phase shift designs are taken into account. To characterize the secrecy performance, analytical expressions of secrecy outage probability (SOP) and effective secrecy throughput (EST) for an active RIS assisted NOMA (RIS-NOMA) system are derived based on heuristic approximations, which is in consideration of two scenarios with and without direct links. Furthermore, secrecy diversity orders are attained based on theoretical results, of which the convergence rates and gaps between the discrete and continuous phase shifting are further evaluated. The results reveal that a 4-bit quantization can achieve the secrecy diversity order of continuous phase shifts. Numerical results are furnished to corroborate the analyses, and illustrate that the security performance for active RIS-NOMA outperforms the passive RIS-NOMA and conventional cooperation communications under the same total power consumption. The impacts of phase quantization bit, amplification factor and the number of Eves as well as reflective elements on security performance are also substantiated by simulations.

Since the unmanned aerial vehicle (UAV) network and intelligent reflecting surface (IRS) technology can flexibly change wireless network links signal, the UAV-IRS network system is a potential solution to increase the communication performance gain. Motivated by the practicality of UAV-IRS networks, a non-orthogonal multiple access (NOMA) heterogeneous UAV communication system with simultaneous wireless information and power transfer (SWIPT) is considered, which consists of multiple UAV base stations (UBSs), a macro base station (MBS), and multiple IRSs for auxiliary communications. This paper pursues a goal to receive the system energy efficiency (EE) maximization by resource allocation and reflecting design of IRSs. Due to the strong coupling among multiple parameters in the original problem, this complex non-convex problem is decomposed into three stages. In the first stage, this paper decouples the problem into two subproblems of NOMA subchannel assignment and SIC decoding order to find the optimal solution separately. For the second stage, under the constraints of UAV’s maximum transmit power, users’ quality of service (QoS) requirements, user energy harvesting threshold and cross-layer interference constraints, a beamforming design based on Lagrangian duality is exploited. For the third stage, the power splitting (PS) factors and the reflecting phases of the IRS are jointly optimized using the penalty-SDR algorithm to approximate the suboptimal solution. Finally, the simulation curves exhibit the validity and excellent performance of the co-design scheme in improving the system EE.

Many real-time application scenarios are developed in 6G communications. Driven by the low-latency data processing requirements, multi-tier computing has become an important technology to improve user experience and reduce network overhead. In this paper, we consider a multi-tier computation offloading network structure for 6G applications, in which the cloud computing center and the nearby vehicle edge server (VES) are able to partially calculate the tasks offloaded from the user equipment (UE), and the remaining task is processed locally in the UE. By jointly optimizing user scheduling, cloud offloading ratio, VES offloading ratio, and VES mobility, the objective function is to minimize the delay of the system transmission and computation under the constraints of discrete variables and energy consumption. To solve the problem, a primal-dual deep deterministic policy gradient (PD-DDPG) algorithm based on multi-tier computation offloading is proposed. Simultaneously, compared with baseline algorithms, PD-DDPG algorithm has an obvious advantage in both the speed of convergence and the system delay.

Semantic communication has been regarded as a promising technology to serve upcoming intelligent applications. However, few studies have addressed the problem of resource allocation in semantic communication networks. Most resource allocation mechanisms act fairly to all original data, ignoring the meaning behind the transmitted bits. In this paper, a dynamic resource allocation scheme for the task-oriented semantic communication network (TOSCN) based on deep reinforcement learning (DRL) is proposed, which allows data with richer semantic information to preferentially occupy limited communication resources. This paper aims to design a deep deterministic policy gradient (DDPG) agent at the micro base station to maximize the long-term transmission efficiency of tasks. Firstly, the relationship between semantic information and task performance is investigated. Subsequently, a novel wireless resource allocation model for TOSCN is proposed by taking the image classification task as an example. Then, a joint optimization problem of the semantic compression ratio, transmit power, and bandwidth of each user is formulated. The agent is trained in an interactive learning environment to obtain a decent trade-off between the amount of data delivered to the receiver and the accuracy of intelligent tasks. Simulation results demonstrate that the proposed scheme achieves significant advantages in relieving communication pressure and improving task performance in resource-constrained wireless networks.

Traffic control is regarded as a key issue to alleviate congestion in internet of vehicles (IoV) machine-type communications (MTC). Recently, many traffic control schemes have been studied, such as access class barring (ACB) scheme and back-off (BO) scheme. However, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid scheme, combining the priority dynamic ACB (PDACB) scheme and BO scheme. The IoV devices are classified depending on different delay characteristics, where the delay-sensitive devices are classified as high priority. The target is to maximum the successful transmission of packets with the success rate constraint by adjusting the various ACB factors. Proximal policy optimization (PPO) algorithm as a unique deep reinforcement learning (DRL) method is utilized in this paper, which can obtain continuous action space and solve for the optimal ACB factors without estimating backlog of nodes. A quick convergence is achieved by designing sensible state space, action space and reward. The access capability of the PDACB traffic control scheme is verified by simulations.

In the past few years, satellite communications have greatly affected our daily lives, and the integrated terrestrial-satellite network can combine the advantages of satellite and base stations (BSs) to provide wider coverage and lower cost. Because the resources of terrestrial-satellite network are limited, how to allocate resources of terrestrial-satellite network through effective methods has become a major challenge. This paper proposes a framework for resource allocation of terrestrial-satellite network based on non-orthogonal multiple access (NOMA). Then, a deployment method of local cache pools is given to achieve lower time delay and maximize energy efficiency in terrestrial-satellite network. In the proposed framework, we adopt a multi-agent deep deterministic policy gradient (MADDPG) method to obtain the maximum energy efficiency by user association, power control, and cache design. The MADDPG algorithm is divided into two stages, users and BSs are set as agents to complete the optimization problem in the framework. Finally, the simulation results show that the proposed method has better optimized performance compared with the traditional single-agent deep reinforcement learning algorithm and can efficiently solve the problems of resource allocation and cache design in the integrated terrestrial-satellite network.

Nowadays, the multi-access interference problem in the ISAC systems can not be ignored. The study on interference management in ISAC has been envisioned as one of key technologies to support ubiquitous sensing functions. Different from the current work, a communications-sensing-intelligence converged network architecture is proposed to coordinate interference in this paper. Each base station equips with the individual deep neural networks to allocate power and beamforming. On this basis, the interference management is transformed into a functional optimization with stochastic constraints. An unsupervised learning algorithm is proposed to allocate power for interference management. Furthermore, a transfer learning method is presented to obtain the interference management in terms of transmit beamforming. Finally, the distributed management is obtained from the local channel state information in the multi-cell scenario. Simulation results verify the effectiveness of the proposed unsupervised learning interference management method in the ISAC systems.

With the development of internet of things, latency-sensitive applications such as telemedicine are constantly emerging. Unfortunately, due to the limited computation capacity of wireless user devices, the real-time demands can not be met. Multi-access edge computing (MEC), which enables the deployment of edge access points (E-APs) to support computation-intensive applications, has become an effective way to meet the real-time demands. However, the number of WUDs that E-APs can serve are limited. To increase system capacity, the unmanned aerial vehicle (UAV) assisted computation offloading architecture in the terahertz (THz) band is proposed. In this paper, the problem of UAV placement optimization, resource allocation, and computation offloading is investigated considering the quality of service and resource constraints. The joint optimization problem is non-convex and hard to be solved in time by using traditional algorithms, such as successive convex approximation. Therefore, deep reinforcement learning (DRL) based approach is a promising way to solve the formulated non-convex problem of minimizing latency. Double deep Q-learning (DDQN) and deep deterministic policy gradient (DDPG) algorithms are provided to search for near-optimal solutions in highly dynamic environments. The effectiveness of the proposed algorithms is proved by simulation results in different scenarios.

Reconfigurable Intelligent Surface (RIS) has grown rapidly due to its performance improvement for wireless networks, and the integration of unmanned aerial vehicle (UAV) and RIS has obtained widespread attention. In this paper, the downlink of non-orthogonal multiple access (NOMA) UAV networks equipped with RIS is considered. The objective is to optimize the UAV trajectory with RIS phase shift to maximize the system capacity under the UAV energy consumption constraint. By deep reinforcement learning, a capacity maximization scheme under energy consumption constraints based on double deep Q-Network (DDQN) is proposed. The joint optimization of UAV trajectory with RIS phase shift design is achieved by DDQN algorithm. From the numerical results, the proposed optimization scheme can increase the system capacity of the RIS-UAV-assisted NOMA networks.

With the rapid development of mobile services and applications, the transmitting of massive data makes low-cost communication a challenge. Edge-based wireless communication technology is developed to be a promising approach to satisfy the communication requirements. Edge caching technology is one of effective methods to reduce the overhead of communication system and the pressure of backhauls. In this paper, the joint optimization problem of caching placement and power allocation in virtualized low earth orbit (LEO) satellite-terrestrial networks is proposed, which is based on cooperative caching, by considering cache size limits and power constraints. The optimization problem is solved using an algorithm inspired by the courtship movements and random flights of mayflies. Simulation results show the effectiveness of the proposed scheme in improving system performance and reducing power consumption.

Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have been studied. Nevertheless, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it. Firstly, IoV devices are divided into various classes based on delay characteristics. The target of maximizing the successful transmission of packets with the success rate constraint is established. Then, the optimization objective is transformed into a markov decision process (MDP) model. Finally, the access class barring (ACB) factors are obtained based on the PPO method to maximize the number of successful access devices. The performance of the proposal algorithm in respect of successful events and delay compared to existing schemes is verified by simulations.

Random access schemes in satellite Internet-of-Things (IoT) networks are being considered a key technology of new-type machine-to-machine (M2M) communications. However, the complicated situations and long-distance transmission can make the current random access schemes not suitable for the satellite IoT networks. The random access problem in the satellite IoT networks is studied in this article. A novel random access scheme for machine-type-communication devices (MTCDs) is proposed, to maximize the efficiency of random access for contention-based and contention-free random access. Under the set of random access opportunities (RAOs) and limited delay, the random access control model is designed via maximizing efficiency of random access. The model-free deep reinforcement learning (DRL) algorithm is proposed to tackle the problem based on the random access model. Subsequently, the deep Dyna-$Q$learning algorithm is introduced to deal with the proposed random access control model. In this proposed scheme, the random access model-free DRL algorithm is developed using simulated experience. The proposed algorithms’ performances are discussed, and simulation results show the desirable performance of the proposed DRL methods on different system parameters.

The use of cell-free massive multiple-input–multiple-output (MIMO) is regarded as a novel technique in the Industrial Internet of Things (IIoT) networks, and many studies have been reported on its cross-layer optimization, including random access and power allocation. Nevertheless, the cooperation of deep reinforcement learning (DRL) and cell-free massive lacks of deep study. In this article, a primal–dual deep deterministic policy gradient (DDPG) algorithm is designed to obtain cross-layer radio resource management, including power allocation in the physical layer and random access in the medium access layer. Different from the current studies, the random access and power allocation is formulated in cell-free massive MIMO IIoT networks, utilized by the stochastic ergodic optimization. In contrast to the stochastic policy gradient algorithm, a primal–dual DDPG algorithm is designed for the cross-layer optimization. Moreover, a multiagent primal–dual DDPG algorithm is proposed to different scenarios in the cell-free massive MIMO IIoT networks. Simulations are presented to verify the effectiveness of the primal–dual DDPG algorithm for random access and power allocation in the cell-free massive MIMO IIoT networks.

In this paper, an IRS-aided integrated sensing and communications (ISAC) system operating in the terahertz (THz) band is proposed to maximize the system capacity. Transmit beamforming and phase-shift design are transformed into a universal optimization problem with ergodic constraints. Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario. Specifically, the actor part generates continuous transmit beamforming and the critic part takes charge of discrete phase shift design. Based on the MISO scenario, we investigate a distributed PPO (DPPO) framework with the concept of multi-threading learning in the multi-user multiple-input multiple-output (MIMO) scenario. Simulation results demonstrate the effectiveness of the primal-dual PPO algorithm and its multi-threading version in terms of transmit beamforming and phase-shift design.

In this paper, an uplink non-orthogonal multiple access (NOMA) terrestrial-satellite network is investigated, where the terrestrial base stations (BSs) communicate with satellite by backhaul link, and user equipments (UEs) share spectrum resource of access link. Firstly, a utility function which consists of the achieved terrestrial user rate and cross-tier interference caused by terrestrial BSs to satellite is design. Thus, the optimization problem can be modeled by maximizing the system utility function while satisfying the varying backhaul rate and UEs’ quality of service (QoS) constraints. The optimization problem is highly non-convex and can not be solved directly. Thus, we decouple the original problem into user association sub-problem, bandwidth assignment sub-problem, and power allocation sub-problem. In user association sub-problem, an enhanced-caching, preference relation, and swapping based algorithm is proposed, where the satellite UEs are selected by the channel coefficient ratio. The terrestrial UEs association considers the both caching state and backhaul link. Then we derive the closed-form expression of the bandwidth assignment. In power allocation sub-problem, we convert the non-convex term of the target function into the convex one by the Taylor expansion, and solve the transformed convex problem by an iterative power allocation algorithm. Finally, a three-stages iterative resource allocation algorithm by joint considering the three sub-problems is proposed. Simulation results are discussed to show the effectiveness of the proposed algorithms.

This paper studies the unmanned aerial vehicle (UAV) based multiple intelligent reflecting surface (IRS) network, where the hovering UAV acts as a base station, and the IRS enhances signal transmission to across obstacle between users and UAV. To achieve the maximum sum rate of proposed communication scenario, a non-convex problem considering IRS association results, hovering altitude of UAV, and the phase shift design of multi-IRS is formulated. From the IRS association problem, we can find that the IRS association results are coupled to the decoding order of non-orthogonal multiple access (NOMA). To tackle this, a mathematical interference expansion scheme is developed to decouple it and transform it to convex by binary relaxation method. The non-convexity of hovering altitude optimization problem is solved by logarithm operation, approximation, and auxiliary matrices. For the phase shift optimization problem of multi-IRS, we propose a gradient approximation based initial scheme and develop a univariate optimization based approach on the basis to achieve the users sum rate improvement in multi-IRS. In the end, we compare the proposed scheme with baseline scheme to present the superiority of this work under various network settings. The internal reasons for the variation of simulation results are also analyzed.

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.

The cell-free massive multiple-input multiple-output (CFmMIMO) aims to provide uniform quality of service (QoS) for all users, and can be used in small-area scenarios such as indoor industrial networks. This paper studies the CFmMIMO system for indoor industrial scenarios. Firstly, an access point (AP) grouping based hierarchical network topology is proposed. Based on this, we propose an effective AP selection method. To reduce pilot contamination, a pilot assignment scheme based on inspection robot (IR) location is proposed. Considering the high reliability requirement of industrial data transmission, the power control and backhaul combining are jointly optimized to maximize the minimum signal to interference plus noise ratio (SINR). The scalability of the proposed CFmMIMO system is analyzed, and a scalable power control method is proposed. The simulations demonstrate the effectiveness of the AP selection method, pilot assignment scheme, and the joint optimization algorithm for power control and backhaul combining. Moreover, the impact of network scale and network load on system performance is evaluated and analyzed in the simulations.

We report downhole microseismic monitoring field test results of a fiber-optic-based seismic sensor (FOSS) array in a multistage hydraulic fracturing stimulation and present, for the first time, its systematic comparison with the conventional moving-coil geophone array deployed on-site. Perforation shots’ analysis demonstrates that the FOSS has ~7.5 dB higher narrowband signal-to-noise ratio and, thus, 2.3 times smaller azimuth calibration error than the commercial moving-coil geophone. These benefits are mainly attributed to the intrinsic immunity to electromagnetic interference and a higher frequency resonance of FOSS. Field test results show that the FOSS can identify P- and S-waves of microseismic events, the temporal and spatial distributions of which are consistent with the geophone. In addition, the FOSS is found preferable to detect microseismic events with higher frequencies from some hundreds of Hz-to-kHz range. This superior ability contributes to distinct signatures in the identified P- and S-waves, including a shorter event duration and more concentrated frequency band, comparing to those collected by the geophone. The fracture interpretation results validate the application of fiber-optic seismic sensors on downhole microseismic monitoring.

Affective services will become a research hotspot in artificial intelligence (AI) in the next decade. In this paper, a novel service paradigm combined with wireless communication in integrated satellite-terrestrial network (ISTN) is proposed. On this basis, an affective services computing offloading and transmission network (ASCTN) with a three-tier computation architecture is proposed, which is able to assist users to obtain affective computing services and regulate emotions. The optimization problem is investigated in the ASCTN, which is a discrete, non-linear, and non-convex problem with the limitation of computation ability of satellite and transmit power. Specifically, with the objective to minimize the cost utility related to latency and energy consumption, a joint affective services tasks computing offloading strategy, sub-channel, and power allocation algorithm based on dueling deep Q-network (Dueling-DQN) is proposed, which is in possession of better stability. The simulation results reveal the effectiveness of the optimization algorithm in terms of the cost utility in the ASCTN system.

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm.

In the past few years, satellite communications have greatly affected our daily lives. Because the resources of terrestrial-satellite network are limited, how to allocate resources of terrestrial-satellite network through effective methods have become a major challenge. We propose a framework for energy efficiency optimization of terrestrial-satellite network based on Non-orthogonal multiple access (NOMA). In our framework, we adopt a multi-agent deep deterministic policy gradient (MADDPG) method to obtain the maximum energy efficiency by user association and power control. Finally, the simulation results show that the proposed method has better optimization performance compared with the traditional singleagent deep reinforcement learning algorithm and can efficiently solve the problems of user association and power control in the integrated terrestrial-satellite network.

As the preferred frequency band for future high frequency communication, the terahertz (THz) band has at-tracted wide attention. In this paper, an energy efficient resource optimization problem in THz band is studied. The massive Multiple-Input Multiple-Output (MIMO) technology and intelligent reflecting surface (IRS) are adopted to improve the capacity and energy efficiency (EE) of proposed network. An IRS assisted THz-MIMO downlink wireless network system is established. The original EE problem is decomposed into phase-shift matrix optimization and power allocation. On this basis, a distributed EE optimization algorithm is designed, which transforms the original nonlinear problem into a convex optimization problem. The simulation results reveal that the proposed distributed optimization method converges rapidly and abtains the maximum EE. This also proves that it is feasible and effective to apply both the IRS and the massive MIMO technology into THz communication network.

The problem of power control in the uplink network of cellular users communicating with Device-to-Device (D2D) is mainly studied. Since cellular users and D2D users share spectrum resources, several serious interference will be caused by them. Reasonable measures are taken to control the interference caused by the sharing spectrum resources, otherwise that influences the quality of service (QoS) of cellular users. The reduction of entire system interference can be achieved by power control, so a method of power control based deep reinforcement learning (DRL), namely Asynchronous Advantage Actor Critic (A3C) Algorithm, is proposed. At the same time, the spectrum resources utilization of the system is improved and QoS of cellular users is guaranteed. The simulation results prove rationality of the proposed algorithm, and have better convergence performance than the traditional DRL algorithm.

Terahertz communications is regarded as a promising technology due to its higher bandwidth and narrower beamwidths, which can improve capacity and coverage for indoor wireless users. In this paper, the intelligent reflecting surface (IRS) technique and non-orthogonal multiple access (NOMA) are utilized to compensate drawbacks of indoor transmission mismatch in the terahertz band. Then wireless resource allocation optimization in indoor terahertz IRS-aided systems is transformed into a universal optimization problem with ergodic constraints. With the aid of parametrization features of deep neural networks (DNNs), proximal policy optimization (PPO) is adopted to train the policy and corresponding actions to allocate power and bandwidths. The actor part generates continuous power allocation, and the critic part takes charge of discrete bandwidths allocation. In the design of a deep reinforcement learning (DRL) framework, primal dual ascent is proposed to realize model-free training. Simulation results demonstrate the effectiveness of the primal dual PPO learning algorithm in different settings.

Terahertz (THz) technology and non-orthogonal multiple access (NOMA) technology have shown a high potential to enhance the spectrum efficiency of wireless communications. This paper aims to study the resource scheduling problem by maximizing energy efficiency (EE) of NOMA two-tier heterogeneous network in THz frequency band, taking a full account of the influence of subchannel and power allocation on the downlink of THZ-NOMA network. In order to achieve the research goal better, it's the first time that a subchannel allocation method and power allocation scheme based on improved Whale Optimization Algorithm (WOA) is proposed in this system. The simulation results indicate that, compared with the existing methods, the proposed scheme has faster convergence speed and has certain advantages in performance.

In current research, reinforcement learning (RL) is widely applied to resource management of wireless communication networks. However, many optimization problems have high computational complexity, and traditional RL fails to solve continuous high-dimensional problems. This paper investigates the sum rate problem in single-cell multiuser multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network. In our scenario, users are separated into two groups, while ensuring the lowest target rate among one group of users, compute the maximum sum rate for the other group of users. For the sake of tackling with the non-convex optimization problem and acquiring the maximum sum rate, we design the joint beamforming and power control algorithm based on deep reinforcement learning (DRL) for deep Q-network (DQN) and double DQN. The final simulation section verifies the convergence and feasibility of the proposed algorithm which can achieve significant sum-rate gains.

During the COVID-19 pandemic, Internet of Medical Things (IoMT) has been playing an important role in controlling the development of the epidemic, including enabling doctors in different grade hospitals to make a diagnosis and treatment, isolating and care for confirmed and suspected cases promptly, and preventing infection of patients with the novel coronavirus. In this article, we investigate the minimization optimization problem for healthcare monitoring in fog computing-based IoMT (FogC-IoMT), which is nonlinear and nonconvex problem, by considering Quality-of-Service requirement, power limit, and wireless fronthaul constraint. In order to solve the problem effectively, three independent subproblems are decoupled, and the suboptimal low-complexity computation offloading and resource management scheme is proposed in FogC-IoMT. The simulation results reveal the effectiveness of the proposed optimization algorithm in terms of cost utility.

Driven by the practicality of unmanned aerial vehicle (UAV), we consider a dual-UAV based non-orthogonal multiple access (NOMA) scenario, which consists of one communication UAV for services and one jamming UAV against eavesdropping. The goal is to maximize the secrecy energy efficiency through the successive convex approximation based communication resource, UAV trajectory, and artificial noise optimization. Considering the probabilistic constraint of outage probability from imperfect channel state information, we transform it to a non-probabilistic problem by Markov inequality and Marcum$Q$-function, then the problem is decomposed into three subproblems. We apply matching-swapping method to assign subchannel in non-orthogonal multiple access (NOMA) UAV networks before the joint process, then convert the power optimization problem to a standard convex optimization form by upper bound of the concave function. The communication UAV trajectory is studied under the constraints of flying energy consumption, maximum speed, and flying altitude. To track this NP-hard problem, Taylor expansion and various slack variables sets are introduced to transform the non-convex problem to convex one. For the artificial noise optimization problem, we use the lower bound to replace the convex term turning it into an easy-to-solve convex optimization problem. In the end, simulations results reveal that: 1) The reasonable jamming scheme can improve the secrecy energy efficiency of the NOMA UAV networks, even if it can cause interference for legitimate users; 2) UAV will fly to a place where the performance gain from users is high when flying energy consumption permits.

Terahertz (THz) band has attracted considerable interest recently due to its superior high frequency and large available bandwidth. THz could act a vital part in the sixth generation (6G) mobile communication networks. In this paper, we introduce the downlink non-orthogonal multiple access (NOMA) technology into THz band small cell networks, where the total performance is optimized considering the two key enabling technologies. In order to decrease the energy consumption triggered by increasing of wireless services, we pay great attention to energy efficiency (EE) optimization and resource allocation in the THz-NOMA downlink systems by solving the subchannel assignment and power optimization. We first exploit a channel model for the THz-NOMA downlink system by using the key features of THz-NOMA networks. Then we utilize Dinkelbach-style algorithm to solve the resource allocation problem and decompose it into two subproblems. A subchannel assignment algorithm and a power optimization based on alternative direction method of multipliers (ADMM) algorithm are developed to get the solution. Finally, to embody the strengths of THz-NOMA performance, we compare our proposed schemes against the conventional schemes. Simulation results yield substantially higher EE and further prove the availability of our proposed schemes.

Replacing base stations with unmanned aerial vehicles (UAVs) to serve the communication of ground users has attracted a lot of attention recently. In this paper, we study the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirements. To handle the user scheduling problem, a heuristic algorithm based on matching and swapping theory is proposed first to allocate users that access UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem using logarithmic approximation. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem and a joint optimization algorithm is presented with the algorithm’s convergence and computational complexity. Finally, numerical results are provided to support the rationality of the proposed algorithm.

To satisfy future wireless network's requirements of huge capacity, ultra low delay and supermassive connectivity, it is urgent to study novel wireless communication network architecture and technology. Fog-computing radio access network (F-RAN) is a newly developed network paradigm, in which the edge devices perform the storage, communication, control, configuration and management. Meanwhile, non-orthogonal multiple access (NOMA) has been considered to be a hopeful multiple access mechanism for future radio access networks (RANs). The distinctive feature of NOMA is to assign the same channel for various users simultaneously by utilizing the power domain. With the deployment of NOMA, F-RAN can further promote system throughput, time latency, access capability and spectrum efficiency. The coordination between NOMA and F-RAN provides powerful support to extensive application of augmented/virtual reality (AR/VR), vehicular networking, intelligent medical and other emerging applications. In this paper, we focus on a noncooperative game resource optimization to decrease the complexity while the dynamic optimization problem is decoupled to subchannel assignment and power allocation. Matching theory is applied to propose a subchannel assignment scheme. Then, we convert and decouple the power allocation problem into three subproblems and solve them separately at each slot. Simulation results illustrate the potency of the proposed noncooperative resource allocation scheme.

In this paper, we mainly studied the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirement. To track the joint optimization problem, a heuristic algorithm based on matching theory in cellular networks is proposed firstly to allocate users which connect the UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem by logarithmic approximation, and solved to get an optimal solution. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem for its near-optimal solution. Finally, numerical results are provided to support the rationality of the proposed algorithm.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the energy efficiency of the UAVs cells using non-orthogonal multiple access (NOMA) with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we maximize energy efficiency in the downlink NOMA UAV network considering imperfect CSI between the UAV and users. Resource allocation schemes including user scheduling as well as power allocation are designed for system energy efficiency optimization. Because of the non-convexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a nonprobability problem and then decoupled into two convex subproblems by successive convex approximation method. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm. The simulation results prove that our proposed algorithm is more effective compared with existing resource allocation schemes.

This paper intends to study the energy efficiency in caching based UAV networks, where fog radio access network (FRAN) and non-orthogonal multiple access (NOMA) are considered meanwhile. Taking full account of the impact of caching, subchannel assignment, and power allocation in UAV enabled wireless networks, we formulate the problem of maximizing energy efficiency. In order to better solve the proposed non-convex problem, we propose a subchannel assignment algorithm and a power allocation algorithm applying alternating direction method of multipliers (ADMM). The final simulation section verifies the fast convergence of the algorithm and compares the advantages with existing algorithms.

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multiantenna also enable the network to serve more users. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering and hybrid precoding. Based on channel correlation characteristics, a fast convergence scheme for user clustering using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. The simulation results show that the proposed scheme can achieve faster convergence and higher EE.

Fog-computing radio access network (F-RAN) and non-orthogonal multiple access (NOMA) have been recognized as the promising technologies with high mobility and low delay support. In this paper, we propose a new network architecture of the NOMA based F-RAN, and investigate the noncooperative radio resource optimization for time-varying wireless network environment. The subchannel assignment is modeled as the two-side matching issue, and deal with by the matching theory. Then, the dynamic power allocation is deal with the Lyapunov theory, which is decoupled into there subproblems. Simulations results illustrates that the dynamic resource management scheme can obtain the high utility performance gain of communication systems.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the channel errors of low-altitude communication links formed by mobile deployment of UAVs cannot be ignored. And the energy efficiency of the UAVs communication with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we focus on system performance optimization in non-orthogonal multiple access (NOMA) UAV network considering imperfect CSI between the UAV and users. A suboptimal resource allocation scheme including user scheduling and power allocation is designed for maximizing energy efficiency. Because of the nonconvexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a non-probability problem and then decoupled into two convex subproblems. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm by successive convex approximation method. The simulation results prove that the proposed algorithm is effective compared with existing resource allocation schemes.

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multi-antenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering, hybrid precoding and power optimization. Based on channel correlation characteristics, a fast convergence scheme for user clustering in THz-NOMA-MIMO system using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. Considering the fronthaul link capacity constraint, we design a distributed alternating direction method of multipliers (ADMM) algorithm for power allocation to maximize the EE of THz-NOMA cache-enabled system with imperfect successive interference cancellation (SIC). The simulation results show that the proposed user clustering scheme can achieve faster convergence and higher EE, the design of the hybrid precoding of the sub-connection structure can achieve lower power consumption and power optimization can achieve a higher EE for the THz cache-enabled network.

Cellular networks are bound to connect an ever-increasing number of subscribers. The issue of securing both sufficient capacity and reliable coverage remains to be resolved. This paper introduces the maximum coverage problem in wireless cellular networks and gets insight into the metric structure of the solution space for antenna orientation variables. We construct two metric spaces in mathematical views, in which both the deterministic search method (e.g., Nelder-Mead simplex algorithm) and the stochastic search method (e.g., Genetic Algorithm) have been fully discussed without using gradient information. Accordingly, we propose the improved deterministic and stochastic search methods to boost the coverage optimization procedure. Experiments show that the proposed algorithms not only obtain the close-to-optimal solution but greatly improve the convergence speed by reason that redundant exploration is avoided in the tailored solution spaces. Metric structure, as an essential topology in the antenna orientation solution space, therefore, provides a new perspective to settle other antenna orientation-related coverage and capacity optimization problems.

Non-orthogonal multiple access (NOMA) in heterogeneous network (HetNet) is a very promising scheme to meet the exponential growth of mobile data expected in the coming years. However, since wireless networks are becoming denser, the energy consumption of such networks is increasingly severe. Therefore, it is necessary to design novel energy efficiency (EE) maximization technologies under the constraint of limited energy supply. This paper investigates the resource optimization problem of NOMA heterogeneous small cell networks with simultaneous wireless information and power transfer (SWIPT). By decoupling subchannel allocation and power control, a low-complexity subchannel matching algorithm is designed. Furthermore, to maximize the energy efficiency, a power optimization algorithm is proposed using Langrangian duality. Aiming at the power allocation problem, the original non-convex and non-linear energy efficiency optimization problem is transformed into a more tractable one. Simulation results demonstrate the effectiveness and convergence of the proposed optimization scheme in terms of system energy efficiency.

In the current researches, artificial intelligence (AI) plays a crucial role in resource management for the next generation wireless communication network. However, traditional RL cannot solve the continuous and high dimensional problems. To handle these problems, the concept of deep neural network (DNN) is introduced into RL to solve high dimensional problems. In this paper, we first construct an information interaction model among primary user (PU), secondary user (SU) and wireless sensors in a cognitive radio system. In the model, the SU is unable to get the power allocation information of the PU, and needs to use the received signal strengths (RSSs) of the wireless sensors to adjust its own power. The PU allocates transmit power relying on its power control scheme. We propose an asynchronous advantage actor critic (A3C)-based power control of SU that is a parallel actor-learners framework with root mean square prop (RMSProp) optimization. Multiple SUs learn power control scheme simultaneously on different CPU threads, reducing neural network gradient update interdependence. To further improve the efficiency of spectrum sharing, the distributed proximal policy optimization (DPPO)-based power control is proposed which is an asynchronous variant of actor-critic with adaptive moment (Adam) optimization. It enables the network to converge quickly. After several power adjustments, the PU and the SU meet quality of service (QoS) requirements and achieve spectrum sharing.

NOMA is a key technology for future wireless communication, which can improve the spectral efficiency (SE) of mobile network. In this paper, a subchannel assignment algorithm is applied to maximize the energy efficiency of downlink heterogeneous NOMA network. Different from previous works, the subchannel assignment problems and power allocation problem are formulated as non-convex problem, then we transform the original problems to convex optimization problems and solve it using difference of convex functions (DC) programming. The simulation results confirm that the applied scheme not only enhance the sum rate of heterogeneous NOMA network but also energy efficiency (EE) of small cell base station (SBS).

Ultra dense heterogeneous network (UDHN) has become one of the main frameworks of 5G. Traditional user association methods are difficult to satisfy this new scenario for load balancing. On the other hand, the concept of green communication requires the network to increase energy efficiency. Therefore, it is necessary to study power allocation and user association in UDHN. This paper focuses on load balancing and energy efficiency of UDHN. The joint user association and power allocation is modelled as an appropriate optimization problem. Then we introduce reinforcement learning and propose a multiagent Q-learning based algorithm for solving the optimization problem. According to analysis of simulation result, the convergence of the proposed scheme is verified and the proposed approach is effective on achieving load balancing and enhancing energy efficiency in UDHN.

Millimeter wave (mmWave) communication technology has become an attractive solution to meet exponential growth demand for mobile data services. In this paper, we propose a deep neural networks (DNN) based algorithm for user association and power optimization problem in mmWave heterogeneous network on the basis of gradient iterative algorithm. We jointly design the user association and power optimization to maximize energy efficiency (EE) utilizing Lagrange dual decomposition and then approximate it by DNN models. In addition, an asynchronous distributed DNN based scheme is proposed, which divides the large network model into small distributed networks for distributed data processing on each small base station side to reduce computational time. Simulation results show that the proposed scheme can achieve a high EE with low computation time.

In the past few years, it has become increasingly popular to analyze the information obtained to develop services by conducting a decentralized survey of private data for specific populations. Privacy security requirements for data providers force operators to implement reasonable privacy protections. But increasing the investment in privacy protection will also lead to a decline in operator revenue. In this case, operators need to ensure the privacy and security requirements of users while ensuring the sustainability of customized services. To this end, We study the relationship between collecting data quality and operator strategy, quantifying the price of private data, and building a model to maximize operator profitability. Specifically, closed-form solutions for best privacy data prices and subscription fees are designed to maximize the gross profit of service providers. Also includes the collection of data quality factors to ensure that the user perceived quality of service can be guaranteed to a certain extent. Finally, we explored the relationship between spending, subscription fees, and maximum gross profit of carriers during the data collection phase, based on the distribution of different user groups' privacy attitudes. In particular, we also explored the relationship between adding additional noise and collecting data utility in a decentralized privacy protection scenario. The simulation results show that compared with the existing methods, the algorithm can maximize the collected data quality while ensuring the provider's privacy security requirements. In addition, we demonstrate the benefits of our dynamic pricing approach and its applicability to other private data pricing algorithms.

Circular-shift linear network coding (LNC) is a class of vector LNC with low encoding and decoding complexities, and with local encoding kernels chosen from cyclic permutation matrices. When L is a prime with primitive root 2, it was recently shown that a scalar linear solution over GF(2L-1) induces an L-dimensional circular-shift linear solution at rate (L-1)/L. In this paper, we prove that for arbitrary odd L, every scalar linear solution over GF(2mL), where mL refers to the multiplicative order of 2 modulo L, can induce an L-dimensional circular-shift linear solution at a certain rate. Based on the generalized connection, we further prove that for such L with mL beyond a threshold, every multicast network has an L-dimensional circular-shift linear solution at rate φ(L)/L, where φ(L) is the Euler's totient function of L. An efficient algorithm for constructing such a solution is designed. Finally, we prove that every multicast network is asymptotically circular-shift linearly solvable.

We study a class of linear network coding (LNC) schemes, called circular-shift LNC, whose encoding operations consist of only circular-shifts and bit-wise additions. Formulated as a special vector linear code over GF(2), an L-dimensional circular-shift linear code of degree δ restricts its local encoding kernels to be the summation of at most δ cyclic permutation matrices of size L. We show that on a general network, for a certain block length L, every scalar linear solution over GF(2L-1) can induce an L-dimensional circular-shift linear solution with 1-bit redundancy per-edge transmission. Consequently, specific to a multicast network, such a circular-shift linear solution of an arbitrary degree δ can be efficiently constructed, which has an interesting complexity tradeoff between encoding and decoding with different choices of δ. By further proving that circular-shift LNC is insufficient to achieve the exact capacity of certain multicast networks, we show the optimality of the efficiently constructed circular-shift linear solution in the sense that its 1-bit redundancy is inevitable. Finally, both theoretical and numerical analysis imply that with increasing L, a randomly constructed circular-shift linear code has linear solvability behavior comparable to a randomly constructed permutation-based linear code, but has shorter overheads.

Network coverage and capacity optimization is an important operational task in cellular networks. The network coverage maximization by adjusting azimuths and tilts of antennas is focused and the existing approaches are mainly gradient-free methods. A standard gradient descent algorithm and its improved version, namely a Stochastic Gradient Descent (SGD) algorithm are proposed on the basis of a novel coverage indicator, named as the soft coverage indicator, to approximate the hard version of the original coverage indicator. We prove that the gradient vector is sparse, which accelerates gradient calculation, due to the number limitation of base stations within a specific distance from a given sampling point even if there are many decision variables of azimuths and tilts. Also, the SGD algorithm only requires a small amount of computation based on cheap estimates of the gradients, and thus is applicable to large-scale networks in an efficient manner. The experiments show that the proposed approaches perform well both in their near-optimal solutions and in their computation efficiency compared with the meta-heuristic algorithms. The extensibility and practicality of the proposed algorithms are also discussed.

Non-orthogonal multiple access (NOMA) and heterogeneous networks are promising candidate technologies to meet the exponential growth of mobile data. However, because the wireless network is becoming more and more dense, the energy consumption problem has become increasingly prominent and severe. This paper studies the resource allocation problem of NOMA heterogeneous small cell networks with energy harvesting. By decoupling subchannel allocation and power control, a low complexity subchannel matching algorithm is designed, and a power optimization algorithm is proposed based on Lagrange dual method. The simulation results demonstrated the convergence and effectiveness of the proposed algorithms in terms of the system energy efficiency.

The combination of resource allocation and fog computing based radio access network (Fog-RAN) have great potential for future wireless networks. However, the cross-tier interference in the spectrum-sharing deployment of Fog BSs could affect the network performance seriously and most of the solutions focus on the spectral efficiency optimization. In this paper, the user association, caching strategy, and power allocation are investigated in Fog-RAN with consideration of energy efficiency and cross-tier interference mitigation. The user association, caching, and power allocation are formulated as a non-convex optimization problem and then transformed into a convex problem, which is solved by Alternating Direction Method of Multipliers (ADMM). Then ADMM-based resource allocation algorithms are proposed to improve the energy efficiency of Fog-RAN. Simulation results demonstrate the proposed algorithms's convergence and effectiveness by comparing with existing method.

Circular-shift linear network coding (LNC) is a class of vector LNC with low encoding and decoding complexities, with local encoding kernels chosen from cyclic permutation matrices. When L is a prime with primitive root 2, it was recently shown that a scalar linear solution over GF(2L-1) induces an Ldimensional circular-shift linear solution at rate (L-1)/L. In this work, we prove that for an arbitrary odd L, every scalar linear solution over GF(2(m)L), where mLrefers to the multiplicative order of 2 modulo L, can induce an L-dimensional circularshift linear solution at a certain rate. Based on the generalized connection, we further prove that every multicast network has an L-dimensional circular-shift linear solution at rate φ(L)/L, where φ(L) is the Euler's totient function of L and (m)Lis beyond a threshold. Stemming from this, we last prove that every multicast network is asymptotically circular-shift linearly solvable.

In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access (NOMA) amplify-and- forward two-way relay wireless networks. We aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information (CSI) at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming (GP) problem, and then solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms.

Visible light communication (VLC) is considered as a promising candidate to improve the performance of indoor communication as the complement of wireless radio frequency (RF) communications due to the scarcity of RF resources. Combining the VLC with software-defined small-cell networks will substantially improve the user data rates in indoor heterogeneous networks. In this paper, we introduce the software-defined philosophy into orthogonal frequency-division multiple access-based heterogeneous software-defined and twinned VLC and RF small-cell networks. The pivotal issues of energy efficient (EE) subchannel and power allocation are investigated in the context of software-defined VLC and RF small-cell networks. We formulate the EE resource allocation problem as a non-convex optimization problem, and then, transform it into a convex one using Dinkelbach's method. In addition, distributed subchannel and power allocation algorithms for both VLC and RF are proposed for solving the problem based on the powerful alternative direction method of multipliers. Simulation results verify the effectiveness of resource allocation algorithms conceived for the heterogeneous software-defined twinned VLC and RF small-cell networks in terms of its good convergence and overall performance.

Secure communication is a promising technology for wireless networks because it ensures secure transmission of information. In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access amplify-and-forward two-way relay wireless networks, in the presence of eavesdroppers. By exploiting cooperative jamming (CJ) to enhance the security of the communication link, we aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming problem, and then, solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms under scenarios of using and not using CJ, respectively.

Heterogeneous small cell network with energy harvesting is a promising technique in the next generation mobile communications. However, the cross tier and co-tier co-channel interference can be severe due to the spectrum sharing in inter tier and intra tier of a heterogeneous small cell network. This paper investigates the problem of power allocation and subchannel assignment with the consideration of cross tier/co-tier interference mitigation, energy harvesting, and incomplete channel state information. The power allocation problem in heterogeneous small cell network is modeled as a non-cooperative game by introducing a time-varying cross tier/co-tier interference pricing with simultaneous wireless information and power transfer. Subchannel allocation is modeled as a non-cooperative potential game by minimizing the total interferences experienced by users on each subchannel. Iterative algorithms of power optimization and subchannel allocation are proposed to obtain the Nash equilibrium points. Simulation results are presented to verify the effectiveness of the proposed algorithms in the heterogeneous small cell network.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service and the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems, two of which are linear and the rest can be solved by introducing a Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service (QoS), the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems. Two of which are linear and the rest can be solved by introducing Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

In this paper, we investigate the dynamic subchannel and power allocation in licensed/unlicensed spectrum sharing heterogeneous small cell networks with incomplete channel state information (CSI). We explore the formulated problem using the Lyapunov optimization method by considering co-tier interference and cross- tier interference in both licensed and unlicensed spectrums. The constraints of the minimum user quality of service (QoS), the maximum transmit power limit and the unique of subchannel allocation are also considered to achieve the optimal power and subchannel allocation. Based on the framework of Lyapunov optimization, the problem of energy efficient (EE) optimization can be broken down into three subproblems. Two of which are linear and the rest can be solved by introducing Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the EE-delay tradeoff.

Heterogeneous small cell network is a promising technique in the next generation mobile communications. Many works have been studied in small cells, including resource allocation and interference mitigation, but most studies didn't consider the quality-of-service (QoS) and power consumption. This paper focuses on the power allocation based on non-cooperative scheme to mitigate the interference and increase the energy efficiency in small cells. The delay constraint is introduced in small cells to guarantee the QoS. We reconsider the capacity according to Shannon' capacity formula and bring in the concept of effective capacity. We take the total power consumption of the small cells into account and employ energy efficiency metric to formulate the problem of power allocation. The power allocation problem is modeled as non-cooperative supermodular game, and it is shown to converge to Nash equilibrium, and then it is transformed into a convex optimization problem, which is solved by the multi-agent Q-learning algorithm based on conjecture. The effectiveness of the proposed supermodular game based power allocation is verified by the simulations.

We study a class of linear network coding (LNC) schemes, called circular-shift LNC, whose encoding operations at intermediate nodes consist of only circular-shifts and bitwise addition (XOR). Departing from existing literature, we systematically formulate circular-shift LNC as a special type of vector LNC, where the local encoding kernels of an L-dimensional circular-shift linear code of degree δ are summation of at most δ cyclic-permutation matrices of size L. Under this framework, an intrinsic connection between scalar LNC and circular-shift LNC is established. In consequence, for some block lengths L, an (L - 1, L)-fractional circular-shift linear solution of arbitrary degree δ can be efficiently constructed on a multicast network. With different δ, the constructed solution has an interesting encoding-decoding complexity tradeoff, and when δ = (L - 1)/2, it requires fewer binary operations for both encoding and decoding processes compared with scalar LNC. While the constructed (L - 1, L)-fractional solution has one-bit redundancy per edge transmission, we show that this is inevitable, and that circular-shift LNC is insufficient to achieve the exact capacity of multicast networks.

Millimeter wave (mmWave) communication technologies have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave technology are expected to increase both energy efficiency and spectral efficiency. In this paper, user association and power allocation in mmWave-based UDNs is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. The joint user association and power optimization problem are modeled as a mixed-integer programming problem, which is then transformed into a convex optimization problem by relaxing the user association indicator and solved by Lagrangian dual decomposition. An iterative gradient user association and power allocation algorithm is proposed and shown to converge rapidly to an optimal point. The complexity of the proposed algorithm is analyzed and its effectiveness compared with existing methods is verified by simulations.

In this paper, we investigate the problem of recovering positive semi-definite (PSD) matrix from 1-bit sensing. The measurement matrix is rank-1 and constructed by the outer product of a pair of vectors, whose entries are independent and identically distributed (i.i.d.) Gaussian variables. The recovery problem is solved in closed form through a convex programming. Our analysis reveals that the solution is biased in general. However, in case of error-free measurement, we find that for rank-r PSD matrix with bounded condition number, the bias decreases with an order of O(1/r). Therefore, an approximate recovery is still possible. Numerical experiments are conducted to verify our analysis.

The family of conventional half-duplex (HD) wireless systems relied on transmitting and receiving in different time slots or frequency subbands. Hence, the wireless research community aspires to conceive full-duplex (FD) operation for supporting concurrent transmission and reception in a single time/frequency channel, which would improve the attainable spectral efficiency by a factor of two. The main challenge encountered in implementing an FD wireless device is the large power difference between the self-interference (SI) imposed by the device’s own transmissions and the signal of interest received from a remote source. In this survey, we present a comprehensive list of the potential FD techniques and highlight their pros and cons. We classify the SI cancellation techniques into three categories, namely passive suppression, analog cancellation and digital cancellation, with the advantages and disadvantages of each technique compared. Specifically, we analyze the main impairments (e.g., phase noise, power amplifier nonlinearity, as well as in-phase and quadrature-phase (I/Q) imbalance, etc.) that degrading the SI cancellation. We then discuss the FD-based media access control (MAC)-layer protocol design for the sake of addressing some of the critical issues, such as the problem of hidden terminals, the resultant end-to-end delay and the high packet loss ratio (PLR) due to network congestion. After elaborating on a variety of physical/MAC-layer techniques, we discuss potential solutions conceived for meeting the challenges imposed by the aforementioned techniques. Furthermore, we also discuss a range of critical issues related to the implementation, performance enhancement and optimization of FD systems, including important topics such as hybrid FD/HD scheme, optimal relay selection and optimal power allocation, etc. Finally, a variety of new directions and open problems associated with FD technology are pointed out. Our hope is that this treatise will stimulate future research efforts in the emerging field of FD communications.

Vector linear network coding (LNC) is a generalization of the conventional scalar LNC, such that the data unit transmitted on every edge is an L-dimensional vector of data symbols over a base field GF(q). Vector LNC enriches the choices of coding operations at intermediate nodes, and there is a popular conjecture on the benefit of vector LNC over scalar LNC in terms of alphabet size of data units: there exist (singlesource) multicast networks that are vector linearly solvable of dimension L over GF(q) but not scalar linearly solvable over any field of size q' qL. This paper introduces a systematic way to construct such multicast networks, and subsequently establish explicit instances to affirm the positive answer of this conjecture for infinitely many alphabet sizes pL with respect to an arbitrary prime p. On the other hand, this paper also presents explicit instances with the special property that they do not have a vector linear solution of dimension L over GF(2) but have scalar linear solutions over GF(q') for someq'L, where q' can be odd or even. This discovery also unveils that over a given base field, a multicast network that has a vector linear solution of dimension L does not necessarily have a vector linear solution of dimension L' > L.

We investigate the joint optimization of the group power allocation and prebeamformer for joint spatial division and multiplexing (JSDM) in massive MIMO downlink systems. In contrast with the approximated block diagonalization (ABD) prebeamformer which is derived by heuristic method in the original JSDM scheme and is restricted to semi-unitary matrix, general prebeamforming matrix together with group power is optimized in the framework of per-user ergodic rate balancing. Thus, both flexibility and optimality are integrated in our work. To ease the difficulty in optimizing the exact ergodic rate, the deterministic approximation is employed. Based on the uplink-downlink duality approach, an iterative algorithm alternatively updating group power and prebeamformers is designed. It is shown that the optimization subproblems of the algorithm can be solved efficiently. Compared with the classic signal-to-noise-and-interference ratio (SINR) balancing algorithm for MISO downlink, the proposed algorithm has similar properties of both convergence and optimality. Numerical experiments are conducted to validate the effectiveness of the proposed algorithm.

In the literature of network coding, vector linear network coding (LNC) is a generalization of the conventional scalar LNC, such that the data unit transmitted on every edge is an L-dimensional vector of data symbols over a base field GF(q). A scalar linear code over GF(q) is simply a vector linear code of dimension 1 over GF(q), and a general network has a scalar linear solution over GF(qL) only if it has a vector linear solution of dimension L over GF(q). Though vector LNC is more powerful in enabling a higher coding diversity, this work will present explicit multicast networks, for the first time in the literature, with the special property that they do not have a vector linear solution of dimension L over GF(2) but have scalar linear solutions over GF(q'), for some q'L. This reveals the fact that although vector LNC can outperform scalar LNC in terms of yielding a solution for a general network, scalar LNC can also outperform vector LNC of dimension larger than 1 in terms of using a smaller alphabet to yield a solution for a multicast network.

Vector linear network coding (LNC) is a generalization of the conventional scalar LNC, such that the data unit transmitted on every edge is an L-dimensional vector of data symbols over a base field GF(q). There are classical exemplifying multi-source networks that have simple vector linear solutions but no scalar linear solutions over any field. For (single-source) multicast networks, a popular conjecture characterizes the following benefit of vector LNC over scalar LNC in terms of alphabet size of data units: there exist multicast networks that are vector linearly solvable of dimension L over GF(q) but not scalar linearly solvable over any field of size q' ≤ qL. This paper introduces a general method to construct such a network, and subsequently constructs the first examples to affirm the positive answer of this conjecture. Moreover, among these exemplifying networks vector linearly solvable of dimension L over GF(q), there are instances with the additional property that even for some extremely large q' > qL, they are still not scalar linearly solvable over GF(q').

In an acyclic multicast network, it is well known that a linear network coding solution over GF(q) exists when q is sufficiently large. In particular, for each prime power q no smaller than the number of receivers, a linear solution over GF(q) can be efficiently constructed. In this paper, we reveal that a linear solution over a given finite field does not necessarily imply the existence of a linear solution over all larger finite fields. In particular, we prove by construction that: 1) for every ω ≥ 3, there is a multicast network with source outdegree ω linearly solvable over GF(7) but not over GF(8), and another multicast network linearly solvable over GF(16) but not over GF(17); 2) there is a multicast network linearly solvable over GF(5) but not over such GF(q) that q > 5 is a Mersenne prime plus 1, which can be extremely large; 3) a multicast network linearly solvable over GF(qm1) and over GF(qm2) is not necessarily linearly solvable over GF(qm1+m2); and 4) there exists a class of multicast networks with a set T of receivers such that the minimum field size qminfor a linear solution over GF(qmin) is lower bounded by O(√|T|), but not every larger field than GF(qmin) suffices to yield a linear solution. The insight brought from this paper is that not only the field size but also the order of subgroups in the multiplicative group of a finite field affects the linear solvability of a multicast network.

As is well known, cooperative sensing can remarkably improve the sensing accuracy by exploiting the spatial diversity of different secondary users. However, a large number of cooperative secondary users reporting their local decisions would induce great detection delay and traffic burden, which degrades the performance of secondary spectrum access. This paper proposes an intelligent cooperative sensing ICS strategy with selective reporting and sequential detection to enhance the sensing reliability as well as reduce the sensing overhead for cognitive radios. The tradeoff in the sensing time allocation is studied for ICS and then two novel fusion rules are developed to efficiently obtain the optimum sensing time allocation with different objectives. The performance of ICS is analyzed in terms of miss detection probability and average sensing time, where their closed-form expressions are derived over Rayleigh fading channels. Simulation results reveal that ICS achieves higher sensing reliability with less sensing overhead than the traditional strategy. It is also shown that the miss detection probability and average sensing time of ICS can be minimized by optimizing the sensing time allocation. Copyright © 2013 John Wiley & Sons, Ltd.

The problem of robust relay beamforming for the multiple-antenna amplify-and-forward (AF) relay network in the presence of an eavesdropper is studied in the paper, with the partial eavesdropper's channel side information (ECSI) scenario being considered. In our work, the uncertainty of ECSI is modeled by using a bounded region, which imposes independent constraints on the channel gain and direction. We propose a new rank-2 relay beamformer with a special singular value decomposition (SVD) structure, whose optimal solution for the worst-case secrecy rate maximization problem can be derived via simple line searching. Furthermore, the asymptotic optimality of the proposed rank-2 beamformer is proved in the high-relay-power region under certain condition, with the performance of the proposed beamformer being verified by using numerical experiments.

The performance analysis of optimal relay decode-and-forward (DF) selection for both the full-duplex (FD) and half-duplex (HD) relaying modes is studied, with some important factors such as the distributions of the received signal-to-noise ratio (SNR), the outage probability and the average channel capacity, etc., being taken into account. Different from the conventional relay selection schemes, the trade-off between the FD and HD modes is studied, with the former suffering from the impact of residual self-interference while the latter consuming more channel resources than the former by allocating two orthogonal channels for transmission and reception. The optimal power allocation (OPA) subject to individual power constrains (IPC) and sum power constrains (SPC) are also analyzed in the proposed FD scheme. In particular, the exact closed-form expressions for outage probability of the proposed FD relay selection scheme over independent and identically distributed (i.i.d.) Rayleigh fading channels are derived in this paper, with the validity of the proposed analysis being proven by simulation. It is also shown that the proposed FD scheme outperforms the HD mode in terms of average channel capacity by about 33.1%, provided that the self-interference can be successfully suppressed below the noise power level.

The design of a wireless sensor network is introduced for the metallurgical gas monitoring, in which the sensor node support a slot-based configurable gas sensor array both for metallurgical-specific and general purposes at the factory or far regions. The key technologies to compress the multisensor vector data is discussed with an amendatory LBG vector quantization and Huffman coding algorithms. Both the proposed network design and data compression schemes are verified with the hardware prototype and practical data. To the best of our knowledge, it is a novel exploration and practice for the applications of wireless sensor networks in the metallurgical industries, especially for the metallurgical gas monitoring.

In an acyclic multicast network, it is well known that a linear network coding solution over GF(q) exists when q is sufficiently large. In particular, for each prime power q no smaller than the number of receivers, a linear solution over GF(q) can be efficiently constructed. In this work, we reveal that a linear solution over a given finite field does not necessarily imply the existence of a linear solution over all larger finite fields. Specifically, we prove by construction that: (i) For every source dimension no smaller than 3, there is a multicast network linearly solvable over GF(7) but not over GF(8), and there is another multicast network linearly solvable over GF(16) but not over GF(17); (ii) There is a multicast network linearly solvable over GF(5) but not over such GF(q) that q > 5 is a Mersenne prime plus 1, which can be extremely large.

High concurrent Cloud Server needs high-density CPUs and each CPU needs its own dedicated local disk. In order to save disk space, a virtualized storage fabric is designed to share multiple CPUs attached to one storage device. This paper presents a new storage virtualization strategy based on a Shared Storage Controller (SSC). SSC is a bridge between multiple CPUs and one disk. It is implemented in hardware level using FPGA. It virtualizes disk resource into volumes and provides private and public volumes for each CPU according to their requirements. With SSC, Cloud Server can be more scalable and storage fabric can be safer. In this paper, the architecture of SSC is demonstrated and some preliminary results are presented.

With the development of the cloud computing, the servers we use today are not suitable for data centers very well because of high power consumption and low density. In this paper, we propose a cloud server of low power consumption, high density and good scalability. We employ a reconfigurable architecture to build the cloud server and we can change or update the architecture of the cloud server from remote easily. Furthermore, we build a LAN upon a 2D torus interconnection network using distributed DHCP servers and distributed ARP proxies cooperated with an integrated switch. The experimental result of the prototype shows the availability and high throughput of the network in the cloud server system.

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.

The shrew distributed denial of service (DDoS) attack is very detrimental for many applications, since it can throttle TCP flows to a small fraction of their ideal rate at very low attack cost. Earlier works mainly focused on empirical studies of defending against the shrew DDoS, and very few of them provided analytic results about the attack itself. In this paper, we propose a mathematical model for estimating attack effect of this stealthy type of DDoS. By originally capturing the adjustment behaviors of victim TCPs congestion window, our model can comprehensively evaluate the combined impact of attack pattern (i.e., how the attack is configured) and network environment on attack effect (the existing models failed to consider the impact of network environment). Henceforth, our model has higher accuracy over a wider range of network environments. The relative error of our model remains around 10% for most attack patterns and network environments, whereas the relative error of the benchmark model in previous works has a mean value of 69.57%, and it could be more than 180% in some cases. More importantly, our model reveals some novel properties of the shrew attack from the interaction between attack pattern and network environment, such as the minimum cost formula to launch a successful attack, and the maximum effect formula of a shrew attack. With them, we are able to find out how to adaptively tune the attack parameters (e.g., the DoS burst length) to improve its attack effect in a given network environment, and how to reconfigure the network resource (e.g., the bottleneck buffer size) to mitigate the shrew DDoS with a given attack pattern. Finally, based on our theoretical results, we put forward a simple strategy to defend the shrew attack. The simulation results indicate that this strategy can remarkably increase TCP throughput by nearly half of the bottleneck bandwidth (and can be higher) for general attack patterns.

HTTP-flooding attack disables the victimized Web server by sending a large number of HTTP Get requests. Recent research tends to detect the attacks with the anomaly-based approaches, which detect the HTTP-flooding by modeling the behavior of normal Web users. However, most of the existing anomaly-based detection approaches usually cannot filter the Web crawling traces of the unknown search bots mixed in the normal Web browsing logs. These Web-crawling traces can bias the detection model in the training phase, thus further influencing the performance of the anomaly-based detection schemes. This paper proposes a novel anomaly-based HTTP-flooding detection scheme (HTTP-sCAN), which can eliminate the influence of the Web-crawling traces with the cluster algorithm. The simulation results show that HTTP-sCAN is immune to the interferences of unknown search sessions, and can detect all HTTP-flooding attacks.

Although user cooperation improves sensing accuracy, a large number of secondary users (SUs) reporting decisions may cause significant overhead. In this paper, we propose a distributed scheme, called adaptive cooperative sensing (ACS), to reduce the sensing overhead while satisfying sensing accuracy requirements. In ACS, an anchor SU requires cooperative sensing only when it does not detect the presence of primary user (PU) by itself. When performing cooperative sensing, both selective reporting and sequential detection are employed. We derive the generalized-form expressions of false alarm and detection probabilities over Rayleigh fading channels with considering reporting errors for ACS. The sensing overheads are also analyzed. Then, we study overhead minimization problems and show that the sensing time allocation can be optimized to minimize the miss detection probability and sensing overhead, respectively. By simulations, the effectiveness and efficiency of ACS are validated.

The impact of an imperfect channel estimation on the amplify-and-forward (AF) mode cooperative communications systems is studied, with some important factors, including the probability characteristic of the secondary user's end-to-end signal-to-noise ratio (SNR), the outage probability, the symbol error probability (SEP) and the lower bound on the capacity, etc, being analyzed. As compared to the conventional relay selection schemes, less channel state information (CSI) feedback is required in the proposed method due to an outdated channel estimation being tolerable. The proposed scheme is validated by carrying out both theoretical analysis and numerical simulation, and the theoretical closed-form expressions for some figures of merit, including the outage probability, the SEP and the lower bound on the capacity, are consistent with the numerical results. The simulation results also prove that the performance of the proposed scheme is impacted considerably by some other critical parameters, including the number of relays, the channel correlation coefficient and the interference threshold. In the presence of multiple candidate relays, an optimum solution in terms of either outage probability or SEP performance can always be found within the SNR range of [0dB, 10dB].

The impact of outdated channel state information (CSI) on the capacity of amplify-and-forward (AF) partial relay selection systems is studied in this paper. The closed-form expressions for the distribution of received signal-to-noise ratio (SNR) in a multi-relay cooperative communications system is first derived, with independent and identically distributed (i.i.d.) Rayleigh fading channels being assumed in each wireless link. After that, the theoretical closed-form expressions for both outage probability and channel capacity of partial relay selection are derived, with four classical adaptive transmission techniques, including the constant power with optimal rate adaption (ORA), the optimal power and rate adaption (OPRA), the channel inversion with fixed rate (CIFR) and truncated channel inversion with fixed rate (TIFR), being considered. Numerical analysis proves that the channel capacity of partial relay selection is impacted considerably by some critical parameters, including the number of relays, the channel correlation coefficient and the end-to-end SNR, etc. It's also exhibited in the numerical results that among the four adaptive transmission techniques, the diversity order of OPRA is larger than that of TIFR, and the OPRA outperforms TIFR with about 0.15 bits/s/Hz in terms of average channel capacity.

Energy is one of the most crucial issues in Wireless Sensor Networks (WSNs). Hierarchical routing protocols are best known for energy efficiency. In this paper, we propose an energy-efficient multi-hop hierarchical routing protocol which balances the load of network and reduces the cluster heads energy dissipation. According to the density of sensor nodes in monitoring area, the protocol can dynamically determine the size of the cluster. Furthermore, we propose a weight function to dynamically select the next hop appropriately and transmit data by multi-hop manner. Simulation results show that the new protocol can reduce energy consumption and obviously improve the network lifetime.

In this paper, we design a QoS Guaranteed Dynamic Bandwidth Allocation (QGDBA) scheme for a unique OFDMA-PON architecture. In the scheme, we apply statistical method to dynamically allocate bandwidth, iteration algorithm to fully utilize the frames and United-Priority to guarantee QoS of differentiated services. From the simulation results, we find the scheme can provide efficient bandwidth allocation for the next-generation OFDMA-PON.

Energy detection is the most popular method among spectrum sensing techniques due to its low implementation complexity. However, its performance will be severely degraded by the interference from other secondary users (SUs). In this paper, we first analyze the interference impact on the energy detection in a heterogeneous cognitive wireless network (HCWN). Then, we propose an interference cancellation based energy detection method, referred to as ICED, to combat the interference. Based on ICED, closed-form expressions of the false alarm probability and detection probability are derived for the local detection and the results are extended to the cooperative detection as well. Our analysis is validated by numerical and simulation results which show that the proposed ICED method can significantly improve the performance of energy detection when there exists an interference SU in the HCWN. We also observe that ICED can achieve higher detection probability than the energy detection without interference under certain conditions.

Frequency offset and channel estimation in cooperative orthogonal frequency division multiplexing (OFDM) systems is studied in this paper. We consider the scenario of two or more source nodes sharing the same relay, i.e., corelay cooperative communications, and a new preamble, which is central-symmetric in time-domain, is proposed to perform the frequency offset and channel estimation. The non-zero samples in the proposed preamble are sparsely distributed with two neighboring non-zero samples being separated by μ >; 1 zeros. As long as μ >; 2L - 1 is satisfied, the multipath interference can be effectively eliminated, where L stands for the channel order. Unlike [1], the proposed preamble has a much lower Peak-to-Average Power Ratio (PAPR). The interference among the multiple source nodes can also be eliminated by using a backoff modulation scheme on the proposed preamble in each source node, and the mean-square error (MSE) of the proposed Least-Square (LS) channel estimator can be minimized by ensuring the orthogonality among the source nodes. The Pairwise Error Probability (PEP) performance of the proposed system by considering both the frequency offset and channel estimation errors is also derived in this paper. For a given Signalto-Noise-Ratio (SNR), by keeping the total power consumption to the source nodes and the relay to be constant, the PEP can be minimized by adjusting the ratio between the power allocated to the source nodes and the total power.

Cell search as well as synchronization in the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) system is performed in each User Equipment (UE) by using both the Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS), and the overall synchronization performance is dominated heavily by a robust PSS detection. Conventional non-coherent detector can achieve a reliable PSS detection based on the near-perfect auto-correlation and crosscorrelation properties of Zadoff-Chu (ZC) sequences [1], but at the cost of a relatively high computational complexity. This paper proposes two improved PSS detectors, i.e., Almost Half-Complexity (AHC) and Central Self-Correlation (CSC) detectors, by exploiting the central-symmetric property of ZC sequences. The AHC detector has exactly the same detection accuracy as that of the conventional detector but with 50% complexity being saved, and the proposed CSC detector can further reduce its complexity to 50% that of the AHC detector, however, at a cost of a slight accurate degradation. In order to mitigate the potential failure risk due to a large frequency offset in the proposed algorithms while at the same time keep the PSS detection accuracy un-degraded, an improvement of CSC, i.e., CSCIns, is also proposed. The performance of CSCIns detector is independent of the frequency offset, and numerical results show that the 90% PSS acquisition time of CSCIns is well within a 55ms duration with Signal-to-Noise Ratio (SNR) of -10 dB.

In cognitive radio (CR) networks, the cooperative spectrum sensing can greatly improve the sensing performance. However, several existing cooperative spectrum sensing methods have time asynchronization assumption, which will inevitably bring the waste of waiting time, and will cause many limitations in the practical application. In this paper, we propose an enhanced asynchronous cooperative spectrum sensing framework based on the Dempster-Shafer (D-S) theory. Within such a framework, each SU calculates the trust functions with the double threshold spectrum sensing method, which improves the reliability of the local sensing results. In fusion center (FC), it uses the sliding-window method to ensure the real- time performance and the asynchronism of the fusion data. In addition, to reduce the amount of data fusion in FC, we propose a node selection algorithm using the correlations of trust functions. Our analysis and simulation results show that this method can reduce the number of sensing nodes remarkably and improve the spectrum sensing efficiency significantly.

Distributed denial-of-service (DDoS) attack seriously threatens the survivability of web services. It attempts to exhaust a server's resources (e.g., I/O bandwidth, CPU, and memory resources) to the extent that no resource is available for requests from legitimate users. Recently, some attackers launch web DDoS attack from the application layer (i.e., web app-DDoS), which can evade most of the existing detection approaches that mainly focused on Bandwidth-Flooding DDoS and TCP SYN-Flooding DDoS. This paper discusses the detection of web app-DDoS, and present two different models to characterize user's web access behavior, i.e., click-ratio based model and Markov process based model. With these characterizations as reference, we adopt large deviation theory to estimate the probability that each ongoing user's access behavior is "consistent" with the corresponding reference characterization, and propose two different detection schemes, LD-IID and LD-MP, respectively. We also validate our schemes with simulations, and the simulation results show that LD-IID can detect attackers accurately, yet LD-MP has high false negatives.

Femtocells, as known as home base stations, are destined to be an effective approach that mobile operators build their cellular networks and grow their coverage and capacity. They are small and inexpensive, and transmit at such low power, that they are meant to be placed in individual homes and backhauled in order to deliver a high-quality service to the users at home and at work. However, the transmissions in the existing schemes for the femtocell are easy to be interfered by the ambient noise. In this paper, we propose a new model with virtual multiple input and multiple output (VMIMO) technology that introduces cooperative communication between the different users in a femtocell network. In this model, the legal users in the femtocell network not only can effectively reduce interference via the cooperation, but also they can maximize the data throughput by multi-path effects. The analytical results match very well with the simulation results. Simulation results also indicate that the proposed scheme has the better outage and capacity performance, compared with traditional femtocell models.

Optimal pilot design and placement for both the frequency offsets and channel estimation in Orthogonal Frequency-Division Multiplexing (OFDM) modulated Single Frequency Network (SFN) are treated in this paper. Unlike the conventional frequency-domain filter-based algorithms, the proposed pilot of each transmitter can always be demodulated even if the received pilots of multiple transmitters are totally overlapped. Although the channel state information (CSI) is needed in designing the proposed pilot for carrier frequency offset (CFO) estimation, the CFO estimation performance is robust to the channel estimation error. The optimal pilot as well as the Least-Squares (LS) channel estimator is also proposed, and the pilot for channel estimation is always constant-modulus. Simulation results demonstrate the performance of the proposed algorithm in terms of frequency offset and channel estimation errors. In SFN, some combining technologies such as equal gain combining (EGC) or maximal ratio combining (MRC) can be applied at the receiver to improve the receiving diversity gain. Numerical results show that in a scenario of two transmitters with a Signal-to-Noise Ratio (SNR) of 20 dB, the Bit Error Rate (BER) of 1.5 × 10-4(or 1 × 10-4) can be obtained by using EGC (or MRC), and the BER can be improved to be 4 × 10-6for EGC (or 2 × 10-6for MRC) by considering three co-receiver transmitters.

Cooperative spectrum sensing for cognitive radio is recently being studied to minimize uncertainty in primary user detection. In order to improve the detection probability under a sustainable false alarm probability, a reliable scheme for cooperative spectrum sensing based on double threshold energy detection and Dempster-Shafer (D-S) theory is proposed in this paper. In the algorithm, the double threshold method is used to calculate the local spectrum sensing requirements, which is more accurate than using a single threshold. The D-S theory, which is similar to human reasoning, is adopted to combine different sensing decisions from each cognitive user. A final decision is then made in the fusion center, which decides whether the primary user is present or not. To reduce the redundant nodes, we propose the use of node selection. The analytical results match very well with the simulation results. Simulation results show that the reliability of decision is improved due to the accurate calculation of the local sensing information.

In cognitive radio networks, since any cognitive terminal can access the wideband frequency spectrum, interference from hostile terminals can be hard to detect and eliminate. Since such interference are supposedly wideband compared to other signals, it would be difficult to avoid them. In order to handle hostile terminal interference, signal spectrum sensing and separation is a promising approach that allows the application of proper strategies against hostile terminals. In this paper, we propose a novel signal separation algorithm for spectrum sensing and signal separation, which locates and separates signals occupying the wideband frequency spectrum. The proposed algorithm consists of two steps: 1) wavelet edge detection algorithm is adopted to locate the signal spectrum edge; 2) wideband signal separation approach deals with signal separation and recovery. The analytical results indicate that this novel spectrum sensing algorithm is able to separate wideband signals accurately. Simulation results show that occupying signals can be successfully separated and recovered from wideband spectrum.

Distributed Denial of Service (abbreviated DDoS) attack is a serious problem to the network services. This paper analyzed some solutions to the application layer DDoS (abbreviated app-DDoS) attack, and proposed a relative entropy based app-DDoS detection method. Our scheme includes two stages: learning stage and detection stage. Firstly at the learning stage, it extracts main click features of web objects with the cluster methods. Then at the detection stages, it computes the relative entropy for each session according to the learning result. The greater the session's relative entropy, the more suspicious the session is. At last, simulation results suggest that this method can differentiate the attack session with high detection rate and low false negative ratio.

In optical burst switching (OBS) networks, the data channel scheduling algorithm is one of the most important issues, which have a great impact on network performances. Currently, there are various data channel scheduling algorithms. Among them, the rescheduling algorithm is more attractive because it could adaptively reallocate the data channels even when they have been occupied by some data bursts (DB), and release some channel resource for the latter DB in most situations. However when the traffic load is heavy, it is not effective any more, and would worsen network performance. Therefore, this paper proposes a new rescheduling algorithm, namely VB-Rescheduling algorithm. According to the state of the data channels, it reschedules data blocks on demand by three granularities (i.e., virtual burst, child-burst cluster and normal burst). Compared with other rescheduling algorithms, it has some advantages as follows. Firstly, it could keep the same sequence of the arriving data bursts at a node as the corresponding control packets. Secondly, it is more flexible to reschedule data blocks. Finally, simulation results show that it can greatly improve OBS network performance in terms of the overall packet loss probability and the link utilization, compared with traditional OBS rescheduling algorithm (whose rescheduling granularity is normal burst) and the native virtual burst scheduling scheme.

While throughput maximization and service differentiation are two critical issues in wireless local area networks (WLANs), both are separately investigated in most existing work. This paper, from a different angle, addresses how to maximize saturation throughput of a WLAN conditioned that bandwidth differentiation is supported too. A novel model is established for this problem assuming IEEE 802.11e is used. We calculate the optimal values of minimum contention window for stations to maximize the saturation throughput and provide differentiated service as well. The simulation results validate our new model.

While most existing work focuses separately on how to improve WLAN saturation throughout and how to provide differentiated service, few attention is put to study their relationship. In this paper, we investigate the impact of service differentiation on saturation throughput maximization in IEEE 802.11e WLANs and theoretically prove that it is contradictory and impossible to achieve both of them simultaneously. In other words, saturation throughput is maximized without service differentiation or service differentiation reduces the maximal achievable saturation throughput more or less.

The "pseudo-orthogonal carrier interferometry orthogonal frequency-division multiplexing" technique proposed by Wiegandt can be obtained by simple rearrangement of the parallel data. Thus, the resulting system is not orthogonal frequency-division multiplexing any more, and can be regarded as a cyclic-prefix-based single-carrier system, and the reduction of the peak-to-average-power ratio is not a result of the proposed complex technology

This paper presents a new model to analyze the channel access delay of 802.11 DCF. Based on this analytical model, the average channel access delay of 802.11 DCF is derived. By means of simulation, the correctness of the analysis is validated, and the channel access delay of 802.11 DCF is further evaluated.

In this paper, we explore how to make RWBO+BEB support service differentiation. An analytical model is proposed to analyze how to choose the minimum contention windows according to the bandwidth ratios of stations. Based on the analysis, a novel service differentiated backoff algorithm for IEEE 802.11 DCF, named DS-RWBO, is proposed. The simulation results indicate that DS-RWBO can allocate the wireless bandwidth according to the bandwidth ratio of each station.

IP differentiated services (DiffServ) has been standardized by the IETF and is considered as a promising IP QoS solution due to its scalability and ease of implementation. In this paper, we present a novel framework for IP differentiated services (DiffServ) over optical burst switching (OBS), namely, DS-OBS. We present the network architecture, functional model of edge nodes and core nodes, the control packet format, a novel burst assembly scheme at ingress nodes and scheduling algorithm of core nodes. The basic idea is to apply DiffServ capable burst assembly at ingress nodes and perform different per hop behavior (PHB) electronic treatment for control packets of different QoS classes service at core nodes. Simulation results show that the proposed schemes can provide the best differentiated service for expedited forwarding (EF), assured forwarding (AF) and best effort (BE) service in terms of end-to-end delay, throughput and IP packet loss probability.

This paper has first presented an in-depth analysis on the distributed coordination function (DCF) access mode of IEEE802.11 protocol. Based on the result of our study, we have concluded a new self-adapt wireless LAN MAC algorithm. Numerous simulation results have shown that the new algorithm can achieve the self-adapt character with the growing of node number and is superior to the original DCF algorithm in the characters concerned (e.g. throughput, fairness).

Being a part of IEEE project 802, the 802.11 medium access control (MAC) is used to support asynchronous and time bounded delivery of radio data packets. It is proposed that a distributed coordination function (DCF), which uses carrier sense multiple access with collision avoidance (CSMA/CA) and binary slotted exponential backoff, be the basis of the IEEE 802.11 WLAN MAC protocols. This paper proposes a throughput enhancement mechanism for DCF by adjusting the contention window (CW) resetting scheme. Moreover, an analytical model based on Markov chain is introduced to compute the enhanced throughput of 802.11 DCF. The accuracy of the model and the enhancement of the proposed scheme are verified by elaborate simulations.

IEEE 802.11 medium access control (MAC) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is a distributed coordination function (DCF), which is a carrier sense multiple access with collision avoidance (CSMA/CA) with a binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Compared with other models, this model is shown to be able to predict the behavior of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.

IETF proposes the differentiated services (DiffServ) architecture to implement QoS in the Internet. However, recent studies have shown that under various conditions, the existing DiffServ mechanism may have problems of unfairness and inefficient resource utilization, thereby failing to achieve the desired QoS for TCP flows running over assured services. This paper considers that the idea of TCP friendly congestion control should be used in traffic conditioning (TC) for AF PHB (assured forwarding per-hop behavior), especially when the customer expects to achieve better performance by out-of-profile traffic. This paper also proposes using a direct congestion control scheme (DCCS) and dynamic traffic conditioning (DTC) to achieve fairness between responsive and unresponsive aggregates as well as better allocations for responsive flows (TCP) with different packet lengths, micro-flow number, round trip time (RTT) etc. Moreover, a better resource utilization can be achieved at the same time.

In this paper, we present an effective scheduling algorithm based on the RPS model, called simple weighted fair queueing (SWFQ). In SWFQ, computation of the system potential function does not require such division or multiplication operations as in MD-SCFQ. Compared with MD-SCFQ, SWFQ has lower complexity and can be easily implemented in chips. We verify the effectivity of proposed SWFQ through strict theoretical analysis.

As an important mechanism to provide QoS guarantee in packet-switched networks, queueing scheduling algorithms have been widely researched. The rate-proportional server (RPS) is a good fluid model that covers the general processor sharing (GPS) models. We can design corresponding packet-level queueing scheduling algorithms through choosing different system potential correction function of RPS. We present an enhanced rate-proportional server (ERPS), which gives the upper and lower bounds of system potential correction function and compute corresponding fairness index. Thus, we can easily devise the needed packet-level queueing scheduling algorithm based on ERPS. We also give a packet-level queueing scheduling algorithm based on ERPS.