The escalating demand for network services has prompted the evolution of Service Function Chaining (SFC) within 6G networks to deliver sophisticated, customized services while ensuring robust cybersecurity. This paper introduces an efficient and secure framework for SFC in Mobile Edge Computing (MEC) environments, termed the Federated Learning-based SFC (FL-SFC), which integrates SFC, MEC, and Federated Learning (FL) to enhance service policy decision-making and safeguard user privacy. The FL-SFC framework enables dynamic updating of service policies and optimizes communication efficiency. We propose an anomaly detection model, CNN-GRU, which combines Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to significantly improve anomaly detection performance at the network edge. Additionally, to address the high communication costs associated with service policy models, we have designed a model compression mechanism leveraging sparsification and quantization techniques, which substantially reduces communication overhead during model training. Simulation experiments demonstrated the superiority of the FL-SFC framework and the CNN-GRU model in detection performance over existing methods. Results indicate that our model excels in accuracy, precision, recall, and F1-score while significantly reducing the number of communication bits, thereby validating the effectiveness of our approach.

Integrating large language models (LLMs) with the Internet of Things (IoT) offer great potential for enhancing vehicle personalization and adaptability in autonomous driving (AD), particularly in open-world scenarios. However, the increasing scarcity of high-quality public data poses a challenge, which could hinder the progress of LLMs in AD. To address this, we propose a novel approach, federated instruction tuning (FIT), that leverages federated learning (FL) to enable collaborative training of a shared model across multiple data owners without sharing raw data, thereby preserving privacy and mitigating data scarcity. Complementing FIT, we introduce a feature diversity (FD) strategy that enriches visual and textual diversity and significantly expands AD data by generating new instruction-following data across key dimensions, such as time, weather, and occlusion. Extensive experiments using LLaMA-Adapter as the base model and four FL methods validate the effectiveness of the FIT framework and the FD strategy. Our analysis also compares LLMs ranging from 1.1 to 7B parameters, with results evaluated using GPT score, demonstrating the potential of FIT in AD. Our findings suggest that FIT and FD can support intelligent network operation and optimization in IoT, benefiting both the AD and artificial intelligence (AI) industries.

The rapid growth of the mobile Internet has led to an increasing demand for reliable transmission of various data types, particularly images shared among multiple users over wireless networks. Traditional communication systems face challenges in managing large-scale image transmissions. Semantic communication, focusing on conveying meaning rather than raw bits, offers a promising solution. For semantic communication, reliability hinges on two key factors: successful transmission and successful understanding. Taking image semantic communication (ISC) systems as an example, successful transmission ensures the physical delivery of the image, while successful understanding refers to the correct interpretation of its semantics. To address these requirements, we design a semantic extraction and reconstruction module, called STC-based on Swin Transformer and convolutional neural network that enables parallel semantic processing and incorporates enhanced channel-aware attention mechanism, which is then integrated with residual blocks to form a joint source-channel coding (JSCC) model for semantic extraction, compression, and reconstruction. To optimize ISC reliability, we design a system utility function integrating the impacts of successful transmission and comprehension. We formulate a utility maximization problem for joint semantic compression rate (SCR) selection and resource allocation, solved by a carefully-designed joint SCR selection and resource allocation (JSSRA) algorithm based on the hierarchical soft actor-critic method. Simulation results demonstrate that our JSCC model significantly improves image quality compared to other learning-based methods while maintaining computational efficiency. Meanwhile, the JSSRA algorithm enhances system utility by 15%-50% compared to existing resource allocation methods. These results validate the effectiveness and superiority of our proposed methods in multi-user ISC systems.

Advancements in Augmented Intelligence of Things (AIoT) have made vehicle trajectory anomaly detection essential for road safety and traffic efficiency. However, the privacy-sensitive nature of trajectory data results in regional data silos, limiting model generalization. Federated learning (FL) enables collaborative training without data sharing, but still struggles with data heterogeneity and synchronous update inefficiencies in real-world scenarios. To address these challenges, we propose a personalized FL scheme for trajectory anomaly detection, namedpFedVTAD. It introduces a mutual-distillation module that uses a messenger model to bidirectionally transfer knowledge between the global and personalized models, producing area-aligned personalized models via adaptive local distillation. It also presents a privacy-preserving asynchronous aggregation that combines differential privacy, a model bank, and threshold-triggered merging to balance privacy and communication efficiency under partial participation and asynchronous arrivals. Experiments on a public trajectory dataset show that under asynchronous updates, pFedVTAD yields a well-generalized global model and area-tailored personalized models, demonstrating strong deployability in dynamic cross-area AIoT settings.

Unmanned aerial vehicle (UAV) networks face critical challenges in dynamic environments where conventional approaches treat trajectory optimization and resource allocation as separate problems, failing to capture their intricate interdependencies and leading to suboptimal performance, excessive energy consumption, and processing delays. This paper addresses these limitations through a novel hybrid methodology that uniquely integrates deep reinforcement learning with convex optimization for joint optimization. Our innovation lies in two interdependent algorithms: Deep Reinforcement Learning (DRL)-based relay UAV trajectory optimization algorithm (DRL-RUTOA), which leverages Model-Agnostic Meta-Learning for rapid environmental adaptation, and computation-aware multi-UAV trajectory optimization algorithm (CA-MUTOA), which employs a benefit-cost prioritization mechanism for selective computational offloading. Unlike previous approaches, we formulate a unified multi-objective optimization framework that simultaneously balances network throughput, energy efficiency, and computational task management. Simulation results demonstrate that our integrated approach significantly outperforms conventional methods, achieving a 35% improvement in network throughput, 28% reduction in processing delay, and 42% reduction in energy consumption. Additionally, our framework exhibits superior convergence efficiency, requiring only 15 iterations compared to 32-42 iterations for conventional methods, confirming its practical viability for resource-constrained UAV operations in complex mission environments.

The integration of autonomous driving technologies with vehicular networks presents significant challenges in privacy preservation, communication efficiency, and resource allocation. This paper proposes a novel U-shaped split federated learning (U-SFL) framework to address these challenges on the way of realizing autonomous driving in vehicular edge networks. U-SFL is able to enhance privacy protection by keeping both raw data and labels on the vehicular user (VU) side while enabling parallel processing across multiple vehicles. To optimize communication efficiency, we introduce a semantic-aware auto-encoder (SAE) that significantly reduces the dimensionality of transmitted data while preserving essential semantic information. Furthermore, we develop a deep reinforcement learning (DRL) based algorithm to solve the NP-hard problem of dynamic resource allocation and split point selection. Our comprehensive evaluation demonstrates that U-SFL achieves comparable classification performance to traditional split learning (SL) while substantially reducing data transmission volume and communication latency. The proposed DRL-based optimization algorithm shows good convergence in balancing latency, energy consumption, and learning performance.

Network slicing is a key technology that can provide service assurance for the heterogeneous application scenarios emerging in the next-generation networks. However, the heterogeneity and complexity of virtualized end-to-end network slicing environments pose challenges for network security operations and management. In this paper, we propose a knowledge-driven cross-domain collaborative anomaly detection scheme for end-to-end network slicing, namely HFL-TranWGAN. Specifically, we first design a hierarchical management framework that performs three-tier hierarchical intelligent management of end-to-end network slices, while introducing a knowledge plane to assist the management plane in making intelligent decisions. Then, we develop a knowledge-driven sub-slice anomaly detection model, the conditional TranWGAN model, in which an encoder, a generator, and multiple discriminators perform adversarial learning simultaneously. Finally, taking the sub-slice anomaly detection model as the basic training model, we utilize hierarchical federated learning to achieve inter-slice and intra-slice collaborative anomaly detection. We calculate the anomaly scores through the discrimination error and reconstruction error to obtain the anomaly detection results. Simulation results on two real-world datasets show that the proposed HFL-TranWGAN scheme performs better in anomaly detection performance such as F1 score and precision compared to the benchmark methods. Specifically, HFL-TranWGAN improved precision by up to 8.53% and F1 score by up to 1.88% compared to benchmarks.

Holographic video creates an immersive experience for users with lifelike scene reconstruction, yet it comes with the trade-off of managing massive data volumes. Therefore, to maintain a consistent quality of experience (QoE) in wireless networks with fluctuating channel conditions, it is necessary to develop efficient and adaptive holographic video streaming methods. This paper proposes a coordinated multicast streaming framework for holographic video that integrates coordinated multipoint transmission with transcoding-enabled multicasting techniques. Our framework supports the simultaneous transmission of holographic video tiles at various bitrates from multiple multi-antenna base stations to different multicast groups, significantly enhancing the overall viewing experience. We formulate an optimization problem with the goals of improving the average video quality experienced by all users while reducing the energy consumption for transcoding, which is NP-hard. By employing the proximal policy optimization, a leading-edge deep reinforcement learning algorithm, and convex optimization techniques, we develop a dynamic algorithm for joint bitrate selection and resource allocation. Simulations confirm the effectiveness of our algorithm, showing marked improvements in QoE over existing baselines.

The rapid advancement in wireless communication technology enables the drones to offer flexible and resilient offloading services to the mobile users in the presence of limited terrestrial infrastructure. However, the interaction between users and drones being in an open environment has raised critical security and privacy concerns. In this paper, we propose integrating the proof-of-work (PoW)-based consensus blockchain technology into the network to tackle these challenges. Specially, the drones are tasked with economically incentivizing the edge computing nodes as the miners to compete for the block generation privilege by solving cryptographic puzzles. We formulate a two-stage Stackelberg differential game to allocate edge computing resources between the drones and miners, leveraging the average reputation of miners to solve for dynamic task pricing. Additionally, the drones and miners take on the roles of leaders and followers in this game, respectively. By solving the openloop Stackelberg equilibrium, we derive the evolving trend of the optimal strategies for the system in the dynamic pricing environment. Furthermore, the numerical results substantiate the effectiveness and feasibility of this proposed scheme.

This paper explores task offloading within load-imbalance vehicular multi-access edge computing (MEC) sys-tems. Addressing the uneven distribution of mobile vehicles causing road side unit (RSU) load imbalances, we leverage vehicle mobility and service pricing to redistribute task loads. RSUs strategically set service prices to alleviate congestion and enhance profitability. Meanwhile, vehicles assess these prices to determine task offloading to different RSU s while in motion, to maximize their individual utility. To achieve this, the Karush- Kuhn- Tucker (KKT) condition is applied to determine the optimal RSU ser-vice pricing. Furthermore, a multi-agent reinforcement learning algorithm, Nash Q-Iearning, is utilized to manage the vehicles' offloading decisions. Simulation results substantiate the efficacy of the Nash Q-Iearning-based task offloading scheme, enhancing the utility of mobile vehicles within competitive environments.

In practical status updating systems, most decisions made at monitors are based on diverse data streams. Due to the cask effect, it can be cognised that the effectiveness of decisions is often constrained by the stalest one, i.e., the straggler among all the streams. This work studies the statistical characteristics of age of the stalest information (AoSI) which describes the timeliness of the stalest stream. The AoSI is defined as the time elapsed since the latest successfully received update of the currently stalest stream among all different streams at the monitor was generated. Peak age of the stalest information (PAoSI) is also studied for evaluating the worst cases, i.e., the peaks of AoSI process. We develop an analytical approach to derive the AoSI and PAoSI based on the per-stream age of information (AoI) and peak age of information (PAoI), for the multi-stream single-monitor system with separate status updating. In particular, to comprehensively characterize the timeliness of the stalest stream, the distributions of AoSI and PAoSI are derived in closed-form for the general multi-stream system. Moreover, we concisely derive the explicit expressions of the distributions and averages of AoSI and PAoSI, upon a typical two-stream case with the classical automatic repeat-request protocol. Finally, the accuracy of the theoretical analyses is validated by the numerical results. Appropriateness and advantages of the AoSI (PAoSI) are elaborated by comparing with the maximum average AoI (PAoI), i.e., the maximal one among the averages of all the per-stream AoIs (PAoIs).

Block propagation is a critical step in the consensus process, which determines the fork rate and transaction throughput of public blockchain systems. To accelerate block propagation, existing block relay protocols reduce the block size using transaction hashes, which requires the receiver to reconstruct the block based on the transactions in its mempool. Hence, their performance is highly affected by the number of transactions missed by mempools, especially in the P2P network with frequent arrival and departure of nodes. In this paper, we introduce Presync, a transaction synchronization protocol that can reduce the difference of transactions between the block and the mempool with controllable bandwidth overhead. It allows mining pool servers to synchronize the transactions in candidate blocks before the propagation of a valid block. Low-bandwidth mode provides a lightweight synchronization by identifying the unsynchronized transactions, so that the missing transactions can be detected with a low redundancy. High-bandwidth mode conducts a full synchronization of the candidate block using short hashes, and the Merkle root is utilized to match the valid block. We study the performance of Presync through stochastic modeling and experimental evaluations. The results illustrate that low and high-bandwidth modes can respectively reduce the end-to-end delay of compact block by 60% and 78% with bandwidth usages 25KB and 63KB, in a network with 5 active pool servers and 2/3 online probability of full nodes.

Blokchain is a promising technology to enable distributed and reliable data sharing at the network edge. The high security in blockchain is undoubtedly a critical factor for the network to handle important data item. On the other hand, according to the dilemma in blockchain, an overemphasis on distributed security will lead to poor transaction-processing capability, which limits the application of blockchain in data sharing scenarios with high-throughput and low-latency requirements. To enable demand-oriented distributed services, this paper investigates the relationship between capability and security in blockchain from the perspective of block propagation and forking problem. First, a Markov chain is introduced to analyze the gossiping-based block propagation among edge servers, which aims to derive block propagation delay and forking probability. Then, we study the impact of forking on blockchain capability and security metrics, in terms of transaction throughput, confirmation delay, fault tolerance, and the probability of malicious modification. The analytical results show that with the adjustment of block generation time or block size, transaction throughput improves at the sacrifice of fault tolerance, and vice versa. Meanwhile, the decline in security can be offset by adjusting confirmation threshold, at the cost of increasing confirmation delay. The analysis of capability-security trade-off can provide a theoretical guideline to manage blockchain networks based on the requirements of data sharing scenarios.

Recently, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a practical paradigm to enable low latency computing offloading for dispersed users in the fifth generation (5G) wireless networks. However, severe security and privacy concerns are associated with the open environment between the UAVs and edge computing nodes. In this paper, we address these challenges by integrating blockchain technology into UAV-enabled MEC networks. We present an innovative Delegated Proof of Stake (DPoS) consensus mechanism where the UAV is a primary node and verification nodes are edge computing nodes selected by the reputation mechanism. To enhance mobile users’ Quality of Service (QoS), edge computing resources need to be allocated among UAV and verification nodes. Based on this, we propose the trading mechanism for resource pricing and allocation based on the two-stage Stackelberg differential game. Meanwhile, dynamic states of user demands and verification node reputations are modeled using differential equations as constraints of the objective function at various stages to simulate adaptive service requests for users and incentivize active participation for verification nodes. Simulation results prove the effectiveness of the proposed resource trading scheme and demonstrate the equilibrium and convergence status of resource pricing and allocation for edge computing.

5G/B5G networks are expected to deliver huge traffic and support various use cases with diverse requirements. With the increasing demand for network capacity, a cost-effective and flexible RAN is urgently needed to provide customized services for users. On this basis, advanced flexible RAN architectures with functional splits are introduced. In this paper, we study the slice-centric fine-grained functional split and resource allocation problem in flexible RAN. We first formulate a multi-objective problem to jointly optimize the functional split selection, processing, and transmission resource allocation for slices, aiming at maximizing the functional split gain while satisfying slices’ requirements. Since a multi-objective problem may have multiple Pareto optimal solutions and is difficult to solve, we mathematically analyze and transform the problem into an equivalent parametric convex problem. Then, we propose an upper bound algorithm and a dual based resource allocation algorithm to find the solution for the optimization problem. Theoretical analysis and simulation results show that the proposed algorithms can effectively solve the functional split gain maximization problem and obtain a trade-off between processing and transmission resource gain. In addition, the proposed algorithms also outperform other benchmark approaches in terms of resource saving and flexibility.

Emerging interaction-based Internet of Things (IoT) applications have stringent demand for timeliness, imposing critical challenges to the design of status update system. Using redundant devices to update the status of the same process is a promising way to improve timeliness, but this approach can result in out of order update arrivals, making it difficult to analyze timeliness. To that end, the present paper conducts a theoretical study toward the Age of Information (AoI) of a multi-queue status update system where multiple sensors observe one physical process and update a common monitor. Based on the stochastic hybrid systems method, the average AoI of the considered system is derived in closed form. The theoretical results are consistent with the simulation results, verifying the correctness of the theoretical analysis. It is shown that the logarithm of the average AoI is linearly decreasing with the logarithm of the number of sensors.

This work studies the average Age of Information (AoI) of a remote monitoring system in which two sensors observe the same physical process and update the status to a common monitor using orthogonal channels. While using redundant devices to update the status of a process can improve the information timeliness at the monitor, the out-of-order arrivals of updates impose a challenge to the AoI analysis. We first model the system as two parallel M/M/1/1 queues. By leveraging tools from stochastic hybrid systems, we obtain analytically the average AoI of the system. In particular, when the arrival or service rates are the same for the two sensors, the average AoI is given in closed form. Our analysis reveals that the average AoI of the considered system is reduced by 16.44% compared to the single-sensor system when the arrival and service rates are equal to 1. Numerical results show that the considered system outperforms the M/M/2 system in average AoI at high arrival rates.

Considering the spectrum sharing system (SSS) coexisting with multiple primary networks, we have employed a well-designed reconfigurable intelligent surface (RIS) to control the radio environments of wireless channels and relieve the scarcity of the spectrum resource. Specifically, the enhancement of the spectral efficiency of the secondary user in the considered SSS is decomposed into two subproblems which are a second-order cone programming (SOCP) and a fractional programming of the convex quadratic form (CQFP), respectively, to optimize alternatively the beamforming vector at the secondary access point (S-AP) and the reflecting coefficients at the RIS. The SOCP subproblem is shown as a concave problem, which can be solved optimally using standard convex optimization tools. The CQFP subproblem can be solved by a low-complexity method of gradient-based linearization with domain (GLD), providing a sub-optimal solution for fast deployment. Taking the discrete phase control at the RIS into account, a nearest point searching with penalty (NPSP) method is also developed, realizing the discretization of the phase shifts of the RIS in practice. The simulation results indicate that both GLD and NPSP can achieve an excellent performance.

5G/B5G networks are expected to deliver a huge traffic and support various use cases with diverse requirements. With the increasing demand for network capacity, a cost-effective and flexible RAN is urgently needed to provide customized services for users. On this basis, the advanced flexible RAN architectures with functional splits are introduced. In this paper, we study the slice-centric fine-grained functional split and resource allocation problem in flexible RAN. We first formulate a multi-objective problem to jointly optimize the functional split selection, processing, and transmission resource allocation for slices, aiming at maximizing the functional split gain while satisfying slices’ requirements. Since a multi-objective problem may have multiple Pareto optimal solutions and is difficult to solve, we mathematically analyze and transform the problem into an equivalent parametric convex problem. Then, we propose an upper bound algorithm and a dual based resource allocation algorithm to find the solution for the optimization problem. Theoretical analysis and simulation results show that the proposed algorithms can effectively solve the functional split gain maximization problem and obtain a trade-off between processing and transmission resource gain. In addition, the proposed algorithms also outperform other benchmark approaches in terms of resource saving and flexibility.

Abundant real-time applications over Internet of Things (IoT) have imperative demands on timely information. Compared to average Age of Information (AoI), distribution of AoI characterizes the timeliness in more details. This article studies the timeliness of an IoT-based multisource status update system. By modeling the system as a multisource M/G/1/1 bufferless preemptive queue, general formulas of violation probabilities and probability density functions (p.d.f.s) of AoI and PAoI are derived based on a time-domain approach. For the case with exponentially distributed service time, the violation probabilities and p.d.f.s are obtained in closed form. To fully characterize the overall timeliness of the multisource system, the maximal violation probabilities of AoI and PAoI are proposed. To improve the overall timeliness under the resource constraint of IoT device, the arrival rate allocation is optimized to control the maximal violation probabilities. It is proved that the optimal arrival rates can be found by convex optimization. In particular, we show that the minimum of maximal violation probability of AoI (PAoI) is achieved only if all violation probabilities of AoI (PAoI) are equal. Finally, numerical results verify the theoretical analysis and show the effectiveness of the arrival rate allocation in improving the overall timeliness.

Reconfigurable intelligent surfaces (RISs) have the capability to change the wireless environment smartly Considering the attenuation of subchannels and crowding users involved in the wideband system, we introduce RISs into the multi-user multi-input single-output (MU-MISO) system with orthogonal frequency division multiplexing (OFDM) for performance enhancement. Maximizing the minimum rate of dense users in an MU-MISO-OFDM system assisted by RIS with an approximate practical model is formulated as the joint optimization problem involving subcarrier allocation, transmit precoding (TPC) matrices at the base station, and RIS passive beamforming. A coalition-game subcarrier allocation (CSA) algorithm is proposed to solve space–frequency resource allocation on subcarriers, which reforms the interference topology among dense users. Fractional programming and convex optimization method are used to optimize the TPC matrices and the RIS passive beamforming, which improves the spectral efficiency synthetically across all subchannels in the wideband system. Simulation results indicate that the CSA algorithm provides a significant gain for dense users. Besides, the proposed joint optimization method shows the considerable advantage of the RISs in the MU-MISO-OFDM system.

Emerging Internet of Things applications pursue both data timeliness and fidelity at the fusion center (FC), raising challenges for network design. This work investigates the optimal node number achieving the best timeliness-fidelity tradeoff. Specifically, we consider a wireless network where homogeneous sensors observe one source simultaneously and deliver their observations to the FC over orthogonal block fading channels. Two scenarios are considered: The FC waits for observations from all nodes and the FC waits for observations from partial nodes. We evaluate the data timeliness and fidelity using the age of information (AoI) and the mean squared error (MSE), respectively. We first present a tight approximation of the average AoI in closed-form and derive a tight lower bound on the MSE of the sensing system. Then, sub-optimal numbers of sensor nodes minimizing a weighted-sum of the average AoI and the MSE are obtained in closed-forms for both scenarios based on high signal-to-noise ratio regime analysis. Iteration algorithms are further provided to approach the optimums. It is shown that the optimal partial number of nodes is proportional to the square root of total number of nodes asymptotically. Numerical results validate the accuracy and effectiveness of the solutions in improving the timeliness-fidelity tradeoff.

In a D2D-enabled MEC system, devices cooperate in task computation by relaying tasks to servers or providing computation capabilities for users. We investigate how nodes choose the roles to join in the offloading process in a dynamic environment, where mobile devices forming a tree-like multihop network can play relays and intermediate executors earning corresponding economic utility. By mathematically modeling the multihop computation offloading, we formulate the task-flow constrained network-wide utility maximization problem as a potential game. Based on the properties of the potential game, we prove the existence of Nash equilibrium and propose two learning-based algorithms, i.e., myopic best response (MBR-CO) and stochastic learning-based computation offloading (SL-CO), to find the equilibrium point in a distributed manner. Theoretical and simulation results show that MBR-CO is dominant in static scenarios, and SL-CO achieves a high utility and stable performance in dynamic scenarios.

We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process through two independent channels. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M system where the service time of two servers is exponentially distributed; the M-D system in which the service time of one server is exponentially distributed and that of the other is deterministic. For the two dual-queue systems, closed-form expressions of average AoI and PAoI are derived by resorting to the graphic method and state flow graph analysis method. Our analysis reveals that when the two servers have the same service rate, compared with the single-queue system with an exponentially distributed service time, the average PAoI and the average AoI of the M-M system decrease by 33.3% and 37.5%, respectively, and those of the M-D system decrease by 27.7% and 39.7%, respectively. Numerical results show that the two dual-queue systems also outperform the M/M/2 single queue dual-server system with optimized arrival rate in terms of average AoI and PAoI.

In this paper, we study the problem of joint Task offloading and resource Allocation for vehicular edge computing with Result Feedback Delay (TARFD). Specifically, we consider a typical roadside unit (RSU) and vehicles within its coverage area, and optimize the task offloading decisions of vehicles as well as the uplink bandwidth allocation and the computation resources allocation on the RSU. The TARFD problem is formulated as a non-convex mixed integer nonlinear programming (MINLP) to minimize the average delay consisting of task offloading delay, task computation delay, and result feedback delay. We derive a lower bound of the optimum to the TARFD problem, based on which we propose an approximate algorithm of the TARFD problem, called A-TARFD. The A-TARFD algorithm can effectively deliver solutions for small-scale scenarios. To tackle large-scale scenarios, a low-complexity algorithm for the TARFD problem, called L-TARFD, is developed by constructing an iteratively updated sequence of locally tight approximate geometric programming (GP) problems. The L-TARFD algorithm can converge to a Karush-Kuhn-Tucker (KKT) point and forces the offloading decisions arbitrarily close to binary values. By comparison with the lower bound, simulation results show that the proposed two algorithms have near-optimal performance over a wide range of parameter settings.

We study the average age of information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. We capture the state transition characteristics of the considered system by establishing a Markov chain. Using the state flow graph analysis method, we derive closed-form expressions of the average peak age of information (PAoI) and the average age of information (AoI) for the dual-queue update system. The numerical results show that compared with the single-queue update system, the average PAoI of the dual-queue update system is reduced by 33.5% and the average AoI dropped by 37.5%.

Blokchain is a promising technology to enable distributed and reliable data sharing at the network edge. The high security in blockchain is undoubtedly a critical factor for the network to handle important data item. On the other hand, according to the trilemma in blockchain, an overemphasis on distributed security will lead to poor transaction-processing capability, which limits the application of blockchain in data sharing scenarios with high-throughput and low-latency requirements. To enable demand-oriented distributed services, this paper investigates the relationship between capability and security in blockchain from the perspective of block propagation and forking problem. First, a Markov chain is introduced to analyze the gossiping-based block propagation among edge servers, which aims to derive block propagation delay and forking probability. Then, we study the impact of forking on blockchain capability and security metrics, in terms of transaction throughput, confirmation delay, fault tolerance, and the probability of malicious modification. The analytical results show that with the adjustment of block generation rate, transaction throughput improves at the sacrifice of fault tolerance, and vice versa. Meanwhile, the decline in security can be offset by adjusting confirmation threshold, at the cost of increasing confirmation delay.

Pursuing both data freshness and preciseness in emerging Internet of Things applications brings big challenge for network design. This work investigates the optimal node number achieving the best fidelity-timeliness tradeoff. Specifically, we consider a wireless sensor network where multiple sensors observe one source simultaneously and deliver the observations to Fusion Center (FC) over orthogonal channels. We assume that the FC waits for observations from only partial nodes. We evaluate the fidelity and timeliness using mean squared error (MSE) and age of information (AoI) metric respectively. Firstly, explicit expressions of AoI and MSE are derived. Secondly, a closed-form approximate optimal number of sensor nodes is obtained to achieve the minimum weighted-sum of AoI and MSE. Iteration algorithm is further provided to approach the optimum. It is proved that the optimal number of partial nodes is proportional to the square root of number of total nodes. Numerical results verify that the proposed near-optimal node number is accurate and can significantly improve the fidelity-timeliness performance.

To take advantages of the high spectral efficiency of full-duplex (FD) mode and control rate reduction caused by the self-interference introduced to the relay receiver, a novel hybrid duplex scheme is proposed where the relay works in FD mode following a duty cycle, and receives-only for the rest of time. After characterizing the achievable rate, a joint FD duty cycle and source power allocation problem is formulated to maximize the achievable rate. It is proved that the optimal source power allocation follows a water-filling algorithm over time. Moreover, the optimal FD duty cycle is obtained by considering low-, medium-, and high-source power cases. Specially, closed-form approximation of the optimal FD duty cycle for medium-source power case is presented. Besides, it is shown that the proposed hybrid duplex scheme degenerates to half-duplex and FD modes for low-and high-source power cases, respectively. Numerical results demonstrate that the proposed scheme can effectively improve the achievable rate for a wide range of parameters.

This work investigates a remote status updating system where the transmission process is modeled as a multi-stream M/G/1/1 non-preemptive system. We derive the closed-form expression of the average AoI of each stream in a heterogeneous case, where the distributions of service time are different for streams. To obtain more insights, we apply the results in a homogeneous system, where the service time distributions are identical, and find that preemption of packets would not always lead to the reduction of AoI, especially when the variance coefficient of the service time is small. We further optimize the generation rate to minimize the sum of average AoI. The results in heterogeneous cases show that given the same average service time for all streams, a higher generation rate should be allocated to the stream with a small service time variance. For the homogeneous cases with different AoI urgency weights for each stream, a higher generation rate should be reserved for the stream with more urgent AoI requirements for timeliness improvement. Besides, a lower bound on sum of average AoI is also provided, which only depends on the service rate and the number of data streams in homogeneous systems. Numerical results validate our theoretical analysis.

Full-duplex (FD) mode has great potential in improving the spectral efficiency. Mitigating the effect of self-interference becomes one key for performance enhancement of FD system. This work proposes a novel hybrid duplex scheme where the relay receives information for a fraction of time and simultaneously transmits and receives information for the rest, following a duty cycle. First, we formulate the achievable rate maximization of the proposed scheme as a joint FD duty cycle and source power allocation optimization problem. The optimal FD duty cycle, the optimal source power allocation, and the maximal achievable rate are explicitly given for some cases and characterized in detail for other cases. Then, the proposed scheme is applied to two-hop relaying systems. Specifically, the optimal source power allocation is proved to be a water-filling solution over the FD phase and the receives-only phase on the source-relay link. By dividing the system as low-, medium-, and high-source power cases, the optimal FD duty cycle and the maximal achievable rate are obtained in (approximate) closed-form case-by-case, where the source power thresholds among cases are clearly expressed. Numerical results validate that the proposed hybrid duplex scheme outperforms other benchmark schemes and can improve the achievable rate significantly.

The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm.

For timeliness-sensitive applications in Internet of things (IoT) systems, it is critical to efficiently allocate transmission resources such that the freshness of information updates can be improved. This paper focuses on timely status updating in an orthogonal frequency division multiplexing-based IoT systems, in which all devices update status to one data center by sharing available bandwidth. To improve the timeliness of updates, a resource allocation optimization problem is formulated, based on finite blocklength (FBL) transmission and the age of information (AoI) metric. Two suboptimal policies, namely, fixed time slot policy and fixed blocklength policy, along with an iterative optimization algorithm, and an approximate optimal policy are presented for addressing the optimal resource allocation. By comparing the performance of different policies, it is shown that the iterative algorithm and the approximate optimal policy outperforms the other two suboptimal policies, and closely approaches the global optimal resource allocation.

The age of information (AoI) becomes a fashion and effective measurement for evaluating the timeliness and freshness of state updates in Internet of Things (IoT). The majority of existing works provide abundant insights for optimizing age through packet management. In this paper, the average AoI of a remote data transmission system in which the transmission process is modeled as a multiple stream M/M/1/1 non-preemptive queue process is considered. We first derive the exact theoretical expression of the average AoI of multiple stream M/M/1/1 non-preemptive queue and then extend this result to more general M/G/1/1 queues. Results suggest that the M/G/1/1 non-preemptive queue strategy can effectively improve the system performance. The comparison of preemption strategy and non-preemption strategy under different service processes shows that preemption of packets does not always lead to reduction of AoI, especially for the system with small coefficient of variance of service time. Moreover, it is found that given the same average service time, the M/G/1/1 queue with small coefficient of variance of service time performs better.

Vehicular edge computing (VEC) has been envisioned as an important application of edge computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VEC. They cooperate with edge severs to process offloading tasks at the vehicular network edge, leading to a new paradigm called parked-vehicle assisted edge computing (PVEC) in the Internet of Vehicles (IoV). However, recent researchers mostly focus on how to optimize the total cost of requesting vehicle (RV), and rarely pay attention to the optimization of the utility of PVs that provide services, including the reward from RV and the overhead of executing task. In this paper, we study a task assignment problem with computing delay constraints for PVEC in IoV. Specially, extra performance loss caused by offloading subtasks to PVs is taken into the cost function of RV. The optimal task assignment problem is formulated and solved with the Stackelberg game framework and a ternary search-based algorithm to minimize the cost of RV and maximize the utility of PVs. Finally, extensive numerical results are provided to demonstrate that our scheme is more efficient in deducing the total cost of RV and increasing the reward for PVs than other two existing schemes.

The Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. The reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the information freshness in IoT systems. By modeling the IoT networks as M/M/1 and M/D/1 queuing systems, this work focuses on characterizing the violation probability of peak AoI and AoI in IoT systems, where a sensor delivers updates to a monitor under M/M/1 and M/D/1 queues with first-come-first-served policy. From a time-domain perspective, we explore the correlation between interdeparture time and system time, by which the closed-form expressions of peak AoI distribution and the violation probability for any AoI constraint are derived. The obtained results induce accurate characterizations for probability distribution functions of peak AoI and AoI. Consequently, accurate characterizations of average AoI and the variance of AoI are obtained. Then, for peak AoI and AoI, the optimal generation rate of the status update that induces the minimal violation probability is also found. The numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints. The theoretical findings and predictions are verified by numerical simulation results as well as provide guidance for the design of IoT networks.

As an important metric of transmission performance, the maximum overall throughput of two-hop half-duplex relaying (HDR) in Ultra-Reliable and Low-Latency Communications (URLLC) is still not fully understood. In particular, an expression which can be evaluated straightforwardly is not available, and the explicit blocklength (BL) and coding rate configurations of source and relay which achieve the maximum throughput are not known either. In this paper, first we derive a closed-form expression of optimal BL configuration, then a closed-form expression of suboptimal coding rates configuration is obtained, finally we derive the closed-form expression of maximum overall throughput. Numerical results validate our theoretical analysis, and show that the maximum throughput of two-hop HDR in URLLC is far superior to conventional relaying and close to the corresponding Shannon capacity.

Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. Reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the data freshness in IoT systems. This work focuses on characterizing the violation probability of AoI in IoT systems where a sensor delivers updates to a monitor under M/M/1 queue with first-come-first-served (FCFS) policy. By exploring the correlation between inter-departure time and system time, the closed-form expression of the violation probability for any AoI constraint is derived. The obtained result induces an accurate characterization of the probability distribution function of AoI. The optimal generation rate of the status update that induces the minimal violation probability is also found. Numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints.

Applications with more sensitive delay and larger data volumes, such as interactive gaming and augmented reality, have become popular recently. Computation offloading is expected as a promising technique to meet low latency for mobile users. However, computation offloading requires communication between mobile users and the mobile edge computing (MEC) server, the delay and energy consumption caused by the transmission are considerable expenses for users. Motivated by this, we consider joint computation offloading and task caching optimization in a cellular network where users can proactively cache and offload their tasks at the MEC server. The objective of this paper is to minimize the system cost, which is defined as the weighted sum of task execution delay and energy consumption for all users. By formulating the problem as mixed-integer non-linear programming, we propose to find the optimal solution by three steps. Through which we have obtained the optimal computing resource allocation, the optimal task caching scheme and an algorithm which yields the optimal computation offloading scheme. Simulation results show that in comparison to the other three benchmark methods, the proposed scheme can effectively reduce the system cost.

Caching popular contents at radio access networks is a promising approach to improve the content delivery efficiency. Most of the existing content delivery schemes focus on the perspective of content providers, paying less attention to the service demand of content requesters. In this paper, we investigate the content delivery policy of a mobile device with service delay constraint in a cache- enabled heterogeneous network (HetNet), where a macro base station (MBS) is overlaid with some small base stations (SBS) with caches. In the considered network, the mobile device needs to make content delivery decisions based on the time, cache state, and signal-to-interference-plus-noise ratio (SINR) state. The problem of solving an optimal content delivery policy is modeled as a Markov decision process (MDP), where the objective is to minimize the delivery cost of the mobile device under the constraint of content service deadline. In order to address this problem, we propose a reinforcement learning (RL) algorithm to learn the optimal policy. The simulation results demonstrate that our proposed RL-based policy achieves a significant improvement in content delivery cost compared with other benchmark solutions.

Caching popular contents at the mobile devices is a promising technique to alleviate the backhaul data rate demand. Since both file placement and data exchange among mobile devices consume energy, the energy status of devices has a significant effect on the caching utility of the whole system. This work considers the caching optimization in a cellular network where mobile devices are served by one base station (BS). As the devices can collect the file segments from the local storage, via device-to-device (D2D) links, and via a cellular link, we aim at minimizing the percentage of file segment that should be collected from the BS by optimizing the file placement scheme at devices to improve caching performance. Due to the difficulty of solving the optimal caching problem, we propose a residual energy-aware file placement algorithm based on the popularity distribution of contents and causality of energy arrival. Simulation results show that in comparison to other two conventional caching methods, the proposed algorithm can effectively reduce the percentage of file segments that collected from the BS.

Nash noncooperative power game is an effective method to implement interference cooperation in downlink multiuser multiple-input multiple-output (MU-MIMO). Power equilibrium point of Nash noncooperative power game can achieve a satisfactory tradeoff between self-benefits of Internet of Things (IoT) users and interference between IoT users which largely enhance the edge IoT user throughput. However, either power strategy space, i.e., the enabled range of power allocation for IoT users, or overall BS transmit power in the existing Nash noncooperative power games is generally static. This limits the performance of systems, especially in IoT systems, etc., in 5G. As an effort to address these problems, we design a novel framework of Nash noncooperative game with iterative convergence for downlink MU-MIMO. We first decompose the MU-MIMO into multiple virtual single-antenna transmit-receive pairs with a stream analytical model. Afterwards, based on streams, we propose a noncooperative water-filling power game with pricing (WFPGP) where the power strategy space of each stream can be dynamically determined byiterative water-filling. We derive the sufficient condition for the existence and uniqueness of WFPGP game, in which the verification of the sufficient condition can be executed in a distributed manner. By simulations, we verify the performance of WFPGP compared to other Nash noncooperative games.

With the emergence of mobile edge computing (MEC), mobile users are able to process various tasks by offloading large-computation-demanding tasks to MEC server located at the edge of the network. As computation offloading requires communication between mobile users and the MEC server, an efficient computation offloading scheme which decreases both task executive delay and transmission energy consumption of mobile users plays a key role in MEC. Motivated by this, we study the computation offloading scheme in a novel MEC system where mobile users can offload tasks to the MEC server or a distributed computing node (DCN). As mobile users' offloading scheme affects the delay and energy consumption each other, we show that the offloading decision-making problem of users can be formulated as a sequential game. In particular, we demonstrate that the Nash equilibrium of the game exists which manifests that the system can converge to a stable status. A multi-user and multi-destination computation offloading scheme is also proposed to achieve the Nash equilibrium. Simulation results show that the proposed computation offloading scheme can significantly decrease the task execution delay as well as the energy consumption of mobile users.

In future mobile networks, more and more machine-type communication (MTC) devices with different service requirements will be deployed. To meet the massive access needs of MTC, this paper develops a cluster-based congestion-mitigating access scheme (CCAS), with aim to mitigate the severe collision of MTC devices (MTCDs) that access to the base station (BS) concurrently. To this end, we first design a modified spectral clustering algorithm to group MTCDs into different clusters based on their locations and service requirements. Then, a device called MTC gateway (MTCG) is chosen by two steps to assist transmitting data for MTCDs in each cluster. In the data transmission process, MTCG is in charge of aggregating packets generated by MTCDs in a cluster and forwarding them to BS when the number of buffered packets reaches a certain threshold. To model the aggregation and forwarding process of each MTCG, we use queuing theory to analyze the access performance in terms of collision probability and access delay. In addition, we also implement simulations to further validate the accuracy of our analytical model and the effectiveness of CCAS. Numerical results, which are consistent with the theoretical values, show that the proposed CCAS can significantly decrease collision probability, and increase the number of successfully received packets of the system without increasing average access delay.

The large amount of data among billions of devices deployed for Industrial Internet of Things (IIoT) cause a massive energy consumption. Driven by the pursuit of green communication, this paper presents a space-reserved cooperative caching scheme for IIoT in the fifth generation mobile heterogeneous networks, where the cache space in a base station is divided into two parts, one is used to store the prefetched data from the servers ahead of the device request time and the other is reserved to store the temporarily buffering data in the wireless transmission queue at the device request time. With the constraint that the quality of service is guaranteed, we propose an algorithm to obtain the optimal proportion between the two parts of the cache space for the purpose of reducing the average energy consumption. Simulation results verified that the proposed caching scheme is more efficient than the conventional one with respect to the average energy consumption.

Wireless network visualization is envisioned as a promising framework to provide efficient and customized services for next-generation wireless networks. In wireless virtualized networks (WVNs), limited radio resources are shared among different service providers for providing services to different users with heterogeneous demands. In this work, we propose a distributed resource allocation scheme for a wireless virtualized small cell networks. The SBSs in the considered system are equipped with self-backhaul and energy harvesting capabilities in order to reduce the operation cost. In particular, with the objective to obtain the utility maximization, a joint user association, time, spectrum and power allocation problem is presented. To tackle the formulated mixed combinatorial and non-convex optimization problem, the original problem is divided into three low-complexity subproblems and we propose an alternating direction method of multipliers (ADMM)-based distributed algorithm to address them efficiently and effectively. Simulation studies are conducted to demonstrate the advantages of our presented system architecture and proposed schemes.

System fairness has been regarded as an important performance index related to qualities of services in mobile networks. Most of researches evaluate the fairness of a cellular system in terms of the cumulative distribution function (CDF) of user throughputs. However, it's difficult to treat the CDF as a parameter to set, adjust and compare. This paper proposes Gini coefficient, which is a primary measure of the inequality of income in economics, can be developed to represent the system fairness in mobile networks. Furthermore, we present a scheme with modified genetic algorithm (GA) to achieve certain level of the system fairness by adjusting the almost blank subframe (ABS) arrangement in LTE-Advanced heterogeneous networks (HetNets). Validations and numerical analysis on the relationship between the system throughput and fairness are performed by computer simulations.

Caching content of small base stations (SBSs) is a promising approach in the fifth generation (5G) wireless networks for decreasing the transmission delay and energy consumption. The key issue in caching is effective cooperation among SBSs for full usage of the caching to reduce the systems cost. In this paper, we first introduce the architecture of wireless cellular networks with caching in 5G. We divide the caching space into two parts: One caches the fixed content purchased by telecom operators from the remote service providers, and the other one caches contents for wireless transmission. Then, we propose the cooperative caching scheme based on content-exchanged, where each user can get its demanded data from either local SBS or purchasing the contents stored in the neighboring cooperative SBSs. We further prove the concavity of system costs regarding to the proportion between the two parts. We also propose a mechanism to search for the minimal system costs. Finally, by simulations, we prove that the performance of our proposed cooperative caching scheme is better than that of the traditional cache schemes without purchasing the contents between SBSs.

Speech feature learning is very important for the design of classification algorithm of Parkinson's disease (PD). Existing speech feature learning method for classification of PD just pays attention to the speech feature. This paper proposed a novel hybrid feature learning algorithm which puts the features of all the speech segments of each subject together, thereby obtaining new and high efficient features without feature transformation. Firstly, hybrid features was constructed by combining features and segments. Secondly, high efficient hybrid feature selection was conducted by various criteria. Thirdly, the selected hybrid features were applied for classification of PD. Besides, various evaluation criteria are introduced into feature selection in this manuscript. Experimental results show that this proposed algorithm can obtain new features (hybrid feature) with satisfactory classification accuracy. The selected features are very stable and meaningful.

The Global Influenza Surveillance Network is crucial for monitoring epidemic risk in participating countries. However, at present, the network has notable gaps in the developing world, principally in Africa and Asia where laboratory capabilities are limited. Moreover, for the last few years, various influenza viruses have been continuously emerging in the resource-limited countries, making these surveillance gaps a more imminent challenge. We present a spatial-transmission model to estimate epidemic risks in the countries where only partial or even no surveillance data are available. Motivated by the observation that countries in the same influenza transmission zone divided by the World Health Organization had similar transmission patterns, we propose to estimate the influenza epidemic risk of an unmonitored country by incorporating the surveillance data reported by countries of the same transmission zone. Experiments show that the risk estimates are highly correlated with the actual influenza morbidity trends for African and Asian countries. The proposed method may provide the much-needed capability to detect, assess, and notify potential influenza epidemics to the developing world.

The explosive growth of smart mobile user equipments (UEs) boosts the emerging of numerous mobile applications. Most of these applications require an always-online connectivity, which incurs overly-frequent Radio Resource Control (RRC) state transitions, leading to signaling storm and user access failure. To address this issue, many researches focus on avoiding frequent transitions between RRC states by maintaining UEs in the RRC connected state for longer time. However, these researches bring up substantial energy consumption. In this paper, we propose an energy-efficient system signaling control method, by which each UE adjusts its RRC release timer adaptively according to the traffic patterns of mobile applications. Numerical results show that in comparison to the conventional signaling control method, the proposed method can save 27.5% average energy consumption with well-controlled signaling load. Meanwhile, the disparity of user experience is significantly lower.

Time-domain inter-cell interference coordination, so called eICIC, has been introduced in Long Term Evolution-Advanced (LTE-Advanced) system as a throughput improvement method for heterogeneous network, where a lot of low power nodes (LPNs) are located in the coverage area of high power base station (Marco-BS). eICIC enables Macro-BSs to configure almost blank subframe (ABS), in which Marco-BSs reduce their transmit power or mute data transmission so as to reduce interference to the users served by LPNs. However, the improvement of the overall throughput is affected by the ABS setting. This paper proposes a novel user throughput estimation method for LTE-Advanced system where ABS and proportional fairness scheduler are applied. Numerical results show that the proposed method accurately estimates the user throughput for arbitrary ABS ratio with the estimation error of 1-7% for cell edge user throughput and around 1% for average user throughput, respectively. As a result, using the proposed method, Macro-BSs can decide proper ABS ratio to maximize cell edge or average user throughput.

Multiple base station (Multi-BS) cooperation has been considered as a promising mechanism to suppress cochannel interference and boost the capacity for cellular networks. However, the large feedback and signaling overhead hinder it from practice. Therefore, limited cooperation among BSs is recognized as a good tradeoff between the performance gain and the relevant cost. In this paper, the whole network is divided into small disjointing BS cooperation groups, namely, clusters. A decentralized framework is proposed to facilitate the BS cluster formation on the downlink, in order to maximize the sum-rate of the scheduled mobile stations (MSs) under the cluster size constraint. Moreover, an efficient BS negotiation algorithm is designed for cluster formation, of which the feedback overhead per MS is irrelevant to the network size, and the number of iteration rounds scales very slowly with the network size. Simulations show that our strategy leads to significant sum-rate gain over static clustering and performs almost the same as the centralized greedy approach. With its low signaling overhead and complexity, the proposed framework is well suited for implementation in large-scale cellular networks.

The paper proposes a TDD-SDMA/TDMA system with a multi-slot assignment to deal with asymmetric traffic in wireless networks, where the packet-based data service is supported. The proposed system assigns more than one time slot for the downlink to each terminal so as to deal with the asymmetric traffic. We also present an efficient algorithm to decide the priority of time slots for the downlink, and by which the maximum potential transmission rate can be obtained for the downlink. The performance of the proposed system is evaluated by computer simulation in terms of the average delay and the average transmission rate and compared with those of a conventional system with a symmetric time slots assignment. It is verified that a significant improvement on system performance can be achieved if the TDD-SDMA/TDMA system employs the proposed multi-slot assignment scheme.