With the high-speed and low-latency connection capabilities brought by the wireless communications, the performance of unmanned aerial vehicle (UAV) swarm for executing tasks such as emergency response, smart farming, and aerial base station has been significantly improved. As the use of UAVs becomes more prevalent in various tasks, the demand for cross-domain task allocation and operations continues to grow. Cross-domain task allocation enables the UAVs to flexibly execute tasks over a wider range, work together in different task domains, thereby improving operation efficiency. However, due to variations in management policies, authentication protocols, and security standards across various domains, to ensure the secure and reliable interoperability of UAVs across diverse domains, cross-domain authentication has become the primary line of defense for UAVs. In this paper, we propose a lightweight and trusted blockchain-based cross-domain authentication scheme for UAV swarm. To achieve a lightweight authentication process, we apply certificateless authentication and avoid costly operations for resource-constrained UAVs. Additionally, the trustworthiness of UAVs and domains is evaluated through credibility, which is stored on blockchain. We analyze the theoretical security of the proposed scheme, and extensive experiments demonstrate its efficiency.

Toward the sixth generation (6G) Internet of vehicles (IoV) networks, key challenges such as massive connectivity, high mobility and superior energy efficiency have driven the development of advanced wireless technologies. Native artificial intelligence (AI) is expected to support diverse vertical industries and offer numerous emerging AI services for 6G. However, how to efficiently process AI services and improve resource utilization while ensuring quality of service is still a challenging problem. In this paper, non-orthogonal multiple access (NOMA) is applied in the designed three-layer IoV network architecture. Then, an energy efficiency maximization problem is formulated by jointly optimizing NOMA transmission power and AI task deployment decisions. Third, a two-level iterative algorithm is proposed using the Dinkelbac’s method. Simulation results verify that our proposed algorithm outperforms benchmarks in terms of energy efficiency.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Artificial intelligence (AI) integration in communication is crucial for 6G. It optimizes terrestrial communication and computing resource usage in the Internet of Things (IoT) using AI techniques, such as supervised learning for data analysis and reinforcement learning for resource allocation. However, in remote areas, i.e., oceans and deserts, IoT devices lose connection due to limited terrestrial coverage. Low Earth Orbit Satellite (LEOS) offers low-latency, high-bandwidth access in these unconnected regions. However, power and computing limitations on both IoT devices and LEOSs present challenges for continuous service. To this end, we present an LEOS-assisted open radio access network (RAN) Architecture (LO-RAN) where an RAN intelligence controller (RIC) is integrated to provide AI abilities. We formulate a joint Offloading decision, Path selection, and Resource allocation problem (OPR) to minimize the weighted energy consumption and latency of LO-RAN. We proposed a Joint Optimization for the Offloading decision, Path selection, and Resource allocation (JOOPR) algorithm. It selects contact and processing LEOSs for path selection, uses proximal policy optimization (PPO) for offloading decisions, and applies Karush-Kuhn–Tucker (KKT) to solve resource allocation. The outputs from path selection and resource allocation contribute to the reward that feeds into the PPO. We conduct numerical simulations to compare the proposed JOOPR with the state-of-the-art approaches. The results show that JOOPR reduces energy consumption and latency by at most 28.75% and 33.01%, respectively.

The integration of autonomous aerial vehicles (UAVs) and low-Earth orbit (LEO) satellites has become attractive for Internet of Things (IoT) task processing, as it can overcome obstacles in terrestrial network coverage, such as those in oceans or desert areas. However, it lacks a collaborative approach for allocating the communication and computing resources among UAVs and LEO satellites and optimizing the hovering point of UAVs to prolong their endurance. In this article, we investigate energy-efficient resource allocation in LEO-assisted UAV networks for the IoT. A novel optimization algorithm, that jointly IoT tasks’ offloading decision, UAVs’ region selection, hovering point chosen, and communication and computing resource allocation (ORHCC), is proposed to optimize UAV trajectories and hovering points, enhancing endurance and minimizing energy consumption. In particular, the UAVs’ region selection and IoT tasks offloading are under the dueling deep Q-network (DuDQN) framework, the hovering point chosen and communication and computing resource allocation via the convex solution. The results show that the proposed ORHCC reduces 12.5% and 20.76% energy consumption compared with the proximal policy optimization and greedy baseline, respectively.

Unmanned aerial vehicles (UAVs) are vulnerable to interception and attacks when operated remotely without a unified and efficient identity authentication. Meanwhile, the openness of wireless communication environments potentially leads to data leakage and system paralysis. However, conventional authentication schemes in the UAV network are centered on the fixed trust boundary, ignoring potential internal threats and failing to flexibly respond to the dynamic requirements of UAV access and identity authentication. Additionally, UAVs are not subjected to periodic repetitive identity authentication, leading to difficulties in controlling access anomalies. Therefore, in this work, we consider a zero-trust framework for UAV network authentication, aiming to achieve UAV identity authentication through the principle of "never trust and always verify". We introduce a blockchain-assisted zero-trust authentication scheme, namely BAZAM, designed for multi-UAV wireless networks. In this scheme, UAVs follow a key generation approach using physical unclonable functions (PUFs), and cryptographic technique helps verify registration and access requests of UAVs. The blockchain is applied to store UAVs authentication-related information in immutable storage. Through thorough security analysis and extensive evaluation, we demonstrate the effectiveness and efficiency of the proposed BAZAM.

Abstract The sixth-generation (6G) mobile network implements the social vision of digital twins and ubiquitous intelligence. Contrary to the fifth-generation (5G) mobile network that focuses only on communications, 6G mobile networks must natively support new capabilities such as sensing, computing, artificial intelligence (AI), big data, and security while facilitating Everything as a Service. Although 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance (O&M), the addition of external functionalities has resulted in low service efficiency and high operational costs. In this study, a technology framework for a 6G autonomous radio access network (RAN) is proposed to achieve a high-level network autonomy that embraces the design of native cloud, native AI, and network digital twin (NDT). First, a service-based architecture is proposed to re-architect the protocol stack of RAN, which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native services. Second, a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications, AI models, data, and computing power demanded by AI use cases. Third, a digital twin network is developed as a virtual environment for the training, pre-validation, and tuning of AI algorithms and neural networks, avoiding possible unexpected losses of the network O&M caused by AI applications. The combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.

The evolution of communication architecture shifts towards virtualization and cloud-native network functions, setting the stage for the flexibility and integration of emerging technologies. Artificial Intelligence (AI) and Machine Learning (ML) as intrinsic elements in network design are some of the crucial visions and requirements for 6G. This paper, from the perspective of O-RAN, explores how current network architectures should evolve towards the integration of communication and intelligence in 6G. It begins with a comprehensive analysis and comparison of the AI-related work conducted by various standard organizations. Building on this, an end-to-end AI integration framework is proposed, which leverages AI technologies, data services, and digital twin (DT) technologies to achieve an integrated intelligent 6G communication system. After that, the key enabling technologies for cross-domain AI, service-based RAN, programmable RAN and digital twins are discussed. At last, the paper analyzes the challenges and opportunities for O-RAN evolution.

Recently, the three-way decision models based on prospect theory have attracted much attention, because they can well consider the risk attitude of decision-makers when decisions involve gains and losses. However, in terms of reference point selection for prospect theory, these models adopt a unified strat-egy for all alternatives without considering the characteristics of the alternatives themselves. To solve this problem, a reference point selection method based on fuzzy information granules is proposed in this paper. Furthermore, a prospect theory-based generalized intuitionistic fuzzy three-way decision model is established. Specifically, firstly, a reference point selection method based on fuzzy information granules is constructed for the evaluation values of alternatives. Secondly, based on the value function of prospect theory, an intuitionistic fuzzy scoring function is proposed. Meanwhile, based on the difference between the two intuitionistic fuzzy scoring values, a PROMETHEE-based trisecting method is designed. In addition, utilizing the priority relationship existing in three divisions, a generalized ranking strategy using recursive trisecting is established for obtaining the ranking result of alternatives. Finally, based on the priority relationship of the classification attribute, an effectiveness index is proposed to evaluate the effectiveness of the decision-making method in decision-making cases. On this basis, the proposed model is used to process different decision-making cases, verifying its effectiveness, superiority, and feasibility.

Abstract With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.

This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). Under a business agreement with the InP, a third-party service provider provides computing services to the subscribed mobile users (MUs). MUs compete for the shared spectrum and computing resources over time to achieve their distinctive goals. From the perspective of an MU, we deliberately define the age of update to capture the staleness of information from refreshing computation outcomes. Given the system dynamics, we model the interactions among MUs as a stochastic game. In the Nash equilibrium without cooperation, each MU behaves in accordance with the local system states and conjectures. We can hence transform the stochastic game into a single-agent Markov decision process. As another major contribution, we develop an online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks to approximate the Q-factor and the post-decision Q-factor, respectively. The deep RL scheme allows each MU to optimize the behaviours with unknown dynamic statistics. Numerical experiments show that our proposed scheme outperforms the baselines in terms of the average utility under various system conditions.

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

Industrial scenarios put forward higher demands on data rate, latency and reliability of 5G networks. In order to further promote the integration and applications of 5G with the industrial field, a 5G network performance evaluation scheme based on factory environment is discussed in this paper. Moreover, to further reduce the transmission and circuitous route latency, a local user plane function (UPF) is also proposed. Combined with the wireless channel propagation model considered in factory scenario, latency, reliability performance and the relationship between data rate and distance are evaluated in a specific environment. It is shown through the measurement results, the proposed local UPF solution could reduce latency around 20%. Finally, recommendations on construction of 5G network in a typical factory environment are also given in this paper.

This paper investigates the problem of age of information (AoI)-aware resource awareness in an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, which is deployed by an infrastructure provider (InP). A service provider leases resources from the InP to serve the mobile users (MUs) with sporadic computation requests. Due to the limited number of channels and the finite shared I/O resource of the UAV, the MUs compete to schedule local and remote task computations in accordance with the observations of system dynamics. The aim of each MU is to selfishly maximize the expected long-term computation performance. We formulate the non-cooperative interactions among the MUs as a stochastic game. To approach the Nash equilibrium solutions, we propose a novel online deep reinforcement learning (DRL) scheme, which enables each MU to behave using its local conjectures only. The DRL scheme employs two separate deep Q-networks to approximate the Q-factor and the post-decision Q-factor for each MU. Numerical experiments show the potentials of the online DRL scheme in balancing the tradeoff between AoI and energy consumption.

This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, in which the UAV provides complementary computation resource to the terrestrial MEC system. The UAV processes the received computation tasks from the mobile users (MUs) by creating the corresponding virtual machines. Due to finite shared I/O resource of the UAV in the MEC system, each MU competes to schedule local as well as remote task computations across the decision epochs, aiming to maximize the expected long-term computation performance. The non-cooperative interactions among the MUs are modeled as a stochastic game, in which the decision makings of a MU depend on the global state statistics and the task scheduling policies of all MUs are coupled. To approximate the Nash equilibrium solutions, we propose a proactive scheme based on the long short-term memory and deep reinforcement learning (DRL) techniques. A digital twin of the MEC system is established to train the proactive DRL scheme offline. Using the proposed scheme, each MU makes task scheduling decisions only with its own information. Numerical experiments show a significant performance gain from the scheme in terms of average utility per MU across the decision epochs.

In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.

Network slicing is a proposing technology to support diverse services from mobile users (MUs) over a common physical network infrastructure. In this paper, we consider radio access network (RAN)-only slicing, where the physical RAN is tailored to accommodate both computation and communication functionalities. Multiple service providers (SPs, i.e., multiple tenants) compete with each other to bid for a limited number of channels across the scheduling slots, aiming to provide their subscribed MUs the opportunities to access the RAN slices. An eavesdropper overhears data transmissions from the MUs. We model the interactions among the non-cooperative SPs as a stochastic game, in which the objective of a SP is to optimize its own expected long-term payoff performance. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game using the channel auction outcomes. Then we linearly decompose the per-SP Markov decision process to simplify the decision- makings and derive a deep reinforcement learning based scheme to approach the optimal abstract control policies. TensorFlow-based experiments verify that the proposed scheme outperforms the three baselines and yields the best performance in average utility per MU per scheduling slot.

In this paper, we consider a hybrid user association (HUA) problem for load balancing of multitier cellular networks. The proposed hierarchical HUA approach builds on a combination of decentralized user association (DUA) and auxiliary intervention of a central control unit (CCU). A major challenge with the CCU intervention is the time interval determined by a selected CCU control cycle during which the DUA must accept all users that satisfy the prevailing association criterion while proactively mitigating potential resource depletions. Consequently, the primary focus of this work is on relating the control cycle of the CCU intervention with the incipient resource depletions, according to a maximum allowed resource depletion probability. By uniquely combining a set of mathematical tools from stochastic geometry and queueing theory, we present a novel HUA method which evaluates the association bias values of the DUA according to a CCU-optimized load vector and enables tier-based resource depletion probability provisioning over finite control cycles. The trade-offs between the proposed HUA method and the standard DUA approach are demonstrated via network simulations with flow-level spatiotemporal dynamics.

We consider two-hop Device-to-device (D2D) relaying in multi-cell downlink networks. D2D relaying is envisioned to be a promising cell coverage extension technique, which can provide improved cell-edge performance without a dense infrastructure deployment. Relaying complicates the resource allocation and interference management in multi-cell networks. We first study the aggregate co-channel interference characteristics when D2D relaying is applied. A fluid network model is used to analyze the inter-cell interference in a multi-cell network with a minimum inter-base station distance. We develop a model for capturing the interaction between relaying decisions made in the own cell and inter-cell interference created to other cells. We investigate network steady state, and optimize key parameters for network-level management. Both simulation and analysis results are provided to help to understand the performance of D2D relaying.

The paper elaborates on the technological and architectural innovations researched and developed by 5G-PPP Phase 1 projects and covering innovation areas such as 5G system design and evaluation, novel air interfaces, network management and security as well as virtualization and service deployment aspects.

We consider a user-centric network-level coordination architecture for 5G heterogeneous Radio Access Networks (RANs), based on RAN softwarization and a centralized coordination framework. We describe the RAN as a set of logical RAN entities, related to cells in a Heterogeneous Network (HetNet), under the control of a central coordination entity. This description allows the creation of Network Functions (NFs) with an abstracted view of the network. We describe a centralized coordination framework, and then develop a NF for InterCell Interference Coordination (ICIC) in a 5G HetNet, optimizing the radio resource usage at network-level. We construct a Network Graph to abstract the problem of resource allocation and cell offloading, with the NF seeking for an optimal solution based on this abstraction. Simulations are performed in a HetNet scenario with a Tabu Search algorithm. Results show the feasibility of performing network-level coordination through a modular NF, with an abstracted view of the network.

Small cell networks are key components in 5G networks to boost the network capacity, improve spectrum and energy efficiency, and enable flexible and new services. Due to the flexible spectrum access among and flexible deployment of small cells, the inter- cell coordination becomes critical for the performance of the network. In this paper, based on the key concept in software defined networking (SDN) for Internet, we first introduce the network graph approach as a tool for the control and coordination among small cells. The network graph is constructed from the abstracted network state information extracted from underlying base stations. It shields the logical centralized control unit from implementation details of the underlying physical layer and thus reduces the control overhead in a centralized solution. We use the network graph for network energy saving in small cell networks, in which network graphs are used to decide the optimal set of small cells in the network. For cells outside this set we can switch them off for energy saving. We propose three types of network graphs with different network state details. Based on these graphs, we formulate the energy saving problem as an integer linear programming (ILP) problem, and propose the practical algorithms to solve the problem. The performance of the algorithms are studied by simulation. It shows the potential of the proposed network graph approach for the inter-cell resource coordination in small cell networks.

This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintaining the quality-of-service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load, which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important, particularly when the state space is huge. In order to solve the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named QC-learning. Moreover, a decentralized version of the QC-learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized QC-learning algorithms in balancing the tradeoff between energy saving and QoS satisfaction.

This paper investigates energy efficiency for the two-tier femtocell networks through combining game theory and stochastic learning. With the Stackelberg game formulation, a hierarchical reinforcement learning framework is developed to study the joint expected utility maximization of macrocells and femtocells. The macrocells behave as the leaders and the femtocells are followers during the learning procedure. At each time step, the leaders commit to dynamic strategies based on the best responses of the followers, while the followers compete against each other with no further information but the leaders' strategy information. In this paper, two learning algorithms are proposed to schedule each cell's transmission power. Numerical results are presented to validate the proposed studies and show that the two learning algorithms substantially improve the energy efficiency of the femtocell networks.

The cross- and co-tier interference creates the challenges to facilitate the concept of heterogeneous cellular networks (HCNs) in practice. In this paper, we establish a combined learning framework to autonomously mitigate the destructive interference. The macrocell is modeled as the leader and protects itself through pricing the interference from small-cells, which are the followers in the stochastic learning process. During each epoch (an epoch consists of T time slots), the leader commits to a pricing policy by knowing the resource allocation policies of all followers, while the followers compete against each other in each time slot only with the leader's price information. In general, for any two consecutive epochs, the HCN states are highly correlated. The previous policy information can thus be leveraged to improve the learning performance. Numerical results support that the proposed study substantially protects the macrocell and at the same time, optimizes the energy efficiency in small-cells.

As energy saving and environmental protection become an inevitable trend, researchers need to shift their focus to “green” oriented architecture design. Recent advances in the area of cognitive radio (CR) have significant potential towards “green” communications. One of the critical challenges for operating CRs in a wireless mesh network is how to efficiently allocate transmission powers and frequency resource among the secondary users (SUs) while satisfying the quality-of-service constraints of primary users. Due to the SUs' intelligent and selfish properties, this paper focuses on the non-cooperative spectrum sharing in cognitive wireless mesh networks formed by a number of clusters. In order to study the competition behaviors of SUs in a dynamic environment, the problem is modeled as a stochastic learning process. We first extend the single-agent reinforcement learning (RL) to a multi-user context, based on which a conjecture based multi-agent RL algorithm is proposed. A rational SU learns the optimal transmission strategy from the conjecture over the other SUs' responses.

The common control channel problem has become one of the main research challenges in a dynamic spectrum access (DSA) based ad hoc network without a global common channel. The basic question lies in how the control channels of the network are aligned in a reliable and distributed way with minimum management overhead. We propose in this paper a novel concept of the control channel cloud to handle this problem. A control channel cloud is a group of connected nodes using the same control channel. The idea is to have clouds evolved by merging so that few control channels are used in the network. In this paper the basic cloud operations are introduced to merge cloud in different situations. The cloud formation algorithms are proposed based on the cloud operations. We prove the convergence of the algorithms and study their performance by simulation.

The purpose of a multi-hop relay in WiMAX system is to enhance cell coverage, throughput and system capacity. IEEE802.16j standard specifies a multi-hop relay and IEEE802.16m standard recently considers adopting a multi-hop relay. Besides, many research groups insist that cooperative communication including transparent relay saves energy as well as brings better performance. However, they don't provide specific data for transparent relay in a realistic communication system such as WiMAX. In this paper, we study on how much power saving it can be achieved when using transparent relay with cooperative strategy. We firstly propose cooperative strategy for transparent relay in order to achieve diversity gain and then investigate how much gain we can achieve and how much power saving we can have in WiMAX model. The spectral efficiency of a distributed Alamouti scheme is N/2N sym/sec/Hz for transparent relay but the one of the proposed scheme is (N-1)/N sym/sec/Hz which is same as original purpose of transparent relay. In addition it is shown that transparent relay with cooperative strategy has up to 60% power saving under the given simulation configuration.

Future wireless access networks, characterized by a large number of small cells densely distributed in metropolitan area, are likely to be the dominating form of future wireless communications. It is expected that the dynamic spectrum access (DSA) enabled by cognitive radio (CR) technologies will act as a key for their success. Considering the high cell density and the large size of network, the spectrum coexistence will be a prominent problem in future wireless access networks. In this paper we study the spectrum self-coexistence of DSA based wireless access networks. The objective is to improve the spectrum utilization among densely distributed cells. To achieve this, topology-aware distributed algorithms are proposed for mobile terminal (MT) association and access point (AP) channel selection. The proposed algorithms enable self-coordination of spectrum in studied networks. The simulation study shows the efficiency of distributed solutions to solve the spectrum coexistence problem in proposed networks.

In this paper, we present an enhanced VoIP scheduling for the high speed downlink packet access (HSDPA) in UMTS, which takes the age of the VoIP packet into account. The downlink capacity can be significantly improved by this way, especially for shorter uplink transmission delay. In order to quantify the achievable performance improvement, we present results obtained from extensive system-level uplink and downlink simulations. Inter alia, it is shown that using the proposed scheme can lead to an increase in the downlink cell capacity of up to 16%. By applying the proposed method, the downlink performance can be improved considerably while the uplink performance remains the same, which ensures the good end-to-end performance.

In this paper we propose a novel architecture integrating a fiber optic infrastructure with 60 GHz wireless transmission, capable of delivering a data rate up to 1 Gbps per cell in indoor environment. The system uses a hybrid WDM/TDM PON as the fixed infrastructure and the 60 GHz radio to provide the end user access and mobility. A centralized medium access control mechanism derived from IEEE 802.3ah is adopted for efficient channel access and bandwidth allocation. The use of standard components and the dynamic bandwidth allocation across wireless cells makes the system a strong candidate for future gigabit wireless LANs.

As the radio spectrum usage paradigm shifting from the traditional command and control allocation scheme to the open spectrum allocation scheme, wireless mesh networks meet new opportunities and challenges. The open spectrum allocation scheme has potential to provide those networks more capacity, and make them more flexible and reliable. However, the freedom brought by the new spectrum usage paradigm introduces spectrum management and network coordination challenges. In this paper, we study the network formation problem in cognitive radio based mesh networks. A cluster-based approach is proposed to form a mesh network in the context of cognitive radio scenario. Moreover, a topology management algorithm is developed to optimize the cluster configuration with regard to the network topology. The prominent feature of the proposed approach lies in the capability to adapt the cluster configuration to network and radio environment changes.

In order to further improve the packet performance in the UMTS FDD system, uplink dedicated physical control channel (DPCCH) gating as a scheme is proposed in 3GPP under the work item "continuous connectivity for packet data users". In this paper, the uplink DPCCH gating concept is described and analyzed on both a qualitative and a quantitative level. And the system performance of different gating patterns used in the scheme was investigated by analytical prediction and system level simulations with different channel profiles. As shown, the selection of the gating pattern has a significant influence on the system performance, which is correlated with the uplink data transmission activity and the uplink power control. This contributes to a better understanding of the effects involved with uplink DPCCH gating where the biggest potential for performance optimizations can be found. In summary, the scheme of the uplink DPCCH gating is promising to improve the packet performance.

In this paper, the concept of DPCCH (dedicated physical control channel) gating for HSUPA (high speed uplink packet access) is analyzed. DPCCH gating technique has been recently under consideration within WCDMA to inactivate control channels during no data transmission periods, and hence, not to misuse capacity. In order to study the concept benefit, a concrete real-time service is selected in this paper: Voice over IP (VoIP) for mobile communications. It will be shown that VoIP would be highly beneficed by DPCCH gating inclusion in 3GPP specifications. Both analytical and simulation studies were run to confirm the gain expectations.

This paper presents air interface capacity simulations for VoIP (voice-over-IP) over 3GPP Release 7 high speed packet access (HSPA) networks. The results show that 3GPP HSPA is able to provide VoIP spectral efficiency which is higher than the spectral efficiency for circuit switched voice calls with the same end-to-end quality. The high VoIP efficiency can be attributed to the packet optimization features in 3GPP Release 5, 6 and 7 specifications, to the advanced mobile receiver algorithms and to the VoIP optimized radio network algorithms. The studies also show that HSPA mobility solutions fulfil VoIP requirements with synchronous handover even if HSDPA does not use soft handover

The increasing demand for high bandwidth and mobility has challenged research to design innovative broadband wireless access networks. The 60 GHz band has recently been attracting interest because of the huge bandwidth it can provide from its 3-7 GHz unlicensed spectrum available worldwide. However, until now a mature system design in the 60 GHz band is still lacking. In this paper, we propose a novel architecture integrating a fiber optic infrastructure with 60 GHz wireless transmission capable of delivering a high data rate of 1 Gbps per cell in indoor environments. We use a WDM upgraded EPON as the fixed infrastructure and the 60 GHz band to provide the end user access and mobility. A centralized media access control mechanism derived from IEEE 802.3ah is adopted for efficient channel sharing. The use of standard components and the dynamic bandwidth allocation across wireless cells make the system a strong candidate for future gigabit wireless LANs.

In today's WDM networks the dominating cost factor is the number of transceivers. Light trails have been shown to reduce the total number of wavelengths, however not necessarily the number of transceivers used in the network. Within this paper we explain two mechanisms that deal with reducing the total number of transceivers in light trail networks. While the first method - 'tune in' light trails (TILT) - ensures that this number is at most the one of a corresponding lightpath network, traffic grooming can be applied in light trail networks as well to reduce the number of transceivers even further. Simulation results show that the TILT network has much better performance than the traditional counterpart, especially when the number of transceivers in the network is limited. Also the light trail network with grooming has better performance than that without grooming