Symbiotic radio (SR) presents an innovative wireless paradigm that simultaneously supports active primary and passive secondary transmissions. This technology significantly enhances spectrum and energy efficiency in network scenarios that support data transmission from a large number of Internet of Things (IoT) devices. Nonetheless, the received backscatter signal experiences attenuation due to the double path loss effect, thereby constraining the secondary network’s capacity to satisfy the data transmission requirements of IoT applications. To enhance the secondary network capacity with high energy efficiency in SR systems, we synergistically apply two promising technologies—wireless power transmission (WPT) and intelligent reflecting surfaces (IRS). Accordingly, this article explores the optimization of secondary network capacity in an SR system assisted by IRS and WPT, where high-density devices are organized into clusters. We adopt a hybrid access method that integrates time division multiple access (TDMA) for clusters accessing the Base Station (BS) and nonorthogonal multiple access (NOMA) for backscatter devices (BDs) communicating with each other in a cluster. By jointly optimizing active beamforming at the BS, passive beamforming at the IRS, and hybrid transmission time allocation, we maximize the sum data rate of the secondary links while ensuring that the communication requirements of primary links are met. To tackle this complex, high-dimensional, nonlinear problem, we propose a capacity optimization algorithm based on deep reinforcement learning (DRL). We conduct system performance evaluations, and the results validate the advantages of our proposed scheme in optimizing the secondary network capacity of SR systems compared to alternative approaches.

In the Internet of Everything (IoE) era, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) have emerged as crucial enabling technologies to support delay-sensitive and computation-intensive IoE services. Despite the potentials of RISs and MEC, achieving efficient service provisioning in IoE scenarios still faces significant challenges due to interdependencies among different types of resources. To address this issue, we propose a digital twin (DT)-empowered IoE framework that leverages real-time monitoring to virtually replicate network conditions, thereby assisting in decision-making in a physical IoE scenario. Specifically, the IoE scenario comprises a MEC server empowered by prestoring some service programs for task execution and a RIS that assists computation offloading. Taking into account deviations between DT and physical networks, we aim to minimize devices’ total task completion delay by jointly optimizing the service caching at the MEC server, the computation offloading of devices, the computing resource allocation at the MEC server, and the beamforming of the RIS. To handle the problem involving discrete and continuous factors, we develop a hybrid deep reinforcement learning (HDRL) algorithm that integrates the double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG) approaches. In our HDRL algorithm, DDQN plays a crucial role in determining discrete variables representing service caching and computation offloading decisions, while DDPG focuses on optimizing resource allocation and RIS beamforming. We conduct simulations to evaluate the performance of the proposed scheme and compare it with several baselines. Simulation results demonstrate the superiority of our scheme in minimizing the task completion delay.

Periodic data collection from numerous vehicular on-board sensors is necessary for aiding decision making in complex navigation and autonomous driving applications. The temporal freshness of data, represented by the Age of Information (AoI), thus holds critical significance. Integrating Unmanned Aerial Vehicle (UAV) relays with Reconfigurable Intelligent Surface (RIS) emerges as a promising strategy to establish reliable communication links between vehicles and data processing centers. Despite this potential, the current body of literature on the integration of UAV relays and RIS is insufficient, particularly in studying AoI. This paper addresses this gap by achieving a comprehensive optimization of the phase shifts at the RIS, spectrum allocation, and the UAV trajectory. The objective is to minimize the average AoI while adhering to the constraints associated with UAV energy consumption. This joint optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. It is tackled using an approach based on Multi-Step Dueling Double Deep Q Network (MSD3QN). Extensive simulations conducted across diverse scenarios prove the effectiveness of our proposed approach and demonstrate its ability in improving the timeliness of making decisions, reducing average AoI, and enhancing network coverage.

To support the running of human-centric metaverse applications on mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Mobile Edge Computing (WPMEC) is promising to compensate for limited computational capabilities and energy supplies of mobile devices. The high-speed computational processing demands and significant energy consumption of metaverse applications require joint resource scheduling of multiple devices and UAVs, but existing WPMEC solutions address either device or UAV scheduling due to the complexity of combinatorial optimization. To solve the above challenge, we propose a two-stage alternating optimization algorithm based on multi-task Deep Reinforcement Learning (DRL) to jointly allocate charging time, schedule computation tasks, and optimize trajectory of UAVs and mobile devices in a wireless powered metaverse scenario. First, considering energy constraints of both UAVs and mobile devices, we formulate an optimization problem to maximize the computation efficiency of the system. Second, we propose a heuristic algorithm to efficiently perform time allocation and charging scheduling for mobile devices. Following this, we design a multi-task DRL scheme to make charging scheduling and trajectory design decisions for UAVs. Finally, theoretical analysis and performance results demonstrate that our algorithm exhibits significant advantages over representative methods in terms of convergence speed and average computation efficiency.

Driven by the increasing computational demand of real-time mobile applications, Unmanned Aerial Vehicle (UAV) assisted Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges and constructing high-throughput line-of-sight links for ground users. Most exsiting studies consider simplified scenarios, such as a single UAV, Service Provider (SP) or service type, and centralized UAV trajectory control. In order to be more in line with real-world cases, we intend to achieve distributed trajectory control of multiple UAVs in UAV-assisted MEC networks with multiple SPs providing differentiated services. Our objective is to minimize the short-term computational costs of ground users and the long-term computational cost of UAVs, simultaneously based on incomplete information. We first solve the formulated problem by reaching the Nash Equilibrium (NE) of the game among SPs based on complete information. We further formulate a Markov game model and propose a Deep Reinforcement Learning (DRL)-based UAV trajectory optimization algorithm, where only local observations of each UAV are required for each SP's flying action execution. Theoretical analysis and performance evaluation demonstrate the convergence, efficiency, scalability, and robustness of our algorithm compared with other representative algorithms.

The proliferation of Internet of Things (IoT) applications has led to the interconnection of multiaccess edge computing (MEC) systems through metro optical networks. To cater to these diverse applications, network slicing has become a popular tool for creating specialized virtual networks. However, the uneven utilization of multidimensional resources can result in resource fragmentation, thereby reducing the utilization of limited edge resources. This article focuses on mitigating multidimensional resource fragmentation in virtual network embedding (VNE) to maximize the profit of the infrastructure provider (InP). The problem is converted into a bilevel optimization problem, taking into account the interdependence between virtual node embedding and virtual link embedding. To solve this problem, we propose a nested bilevel VNE approach named BiVNE. BiVNE leverages an ant colony system (ACS) algorithm for the upper layer problem and utilizes the Dijkstra algorithm and an exact-fit spectrum slot assignment method for the lower layer problem. Evaluation results demonstrate that BiVNE can greatly improve the profit of the InP by increasing the acceptance ratio and avoiding resource fragmentation simultaneously.

Virtual reality-embedded digital twin (VR-DT) service integrates digital twin with virtual reality to visualize the digital representation of real-world production, boosting the digital transformation of manufacturing industry in the Industrial Internet of Things (IIoT). Balanced against the advantages of the VR-DT service, its data-driven, computing-intensive, and security-sensitive features bring challenges to the current IIoT. Therefore, we propose a blockchain-based distributed resource allocation scheme to improve the average Quality of Service (QoS) of the VR-DT services with regard to service delay and transaction throughput. We formulate the joint optimization of channel assignment, subframe configuration, computing capacity allocation, and block size adjustment as a mixed-integer nonlinear programming problem. A fully decentralized multiagent compound-action actor–critic algorithm is developed to solve the QoS optimization problem. Simulation results demonstrate that our proposed scheme can efficiently improve the average QoS of the VR-DT services in a realizable way as compared to existing schemes.

Integrating simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technologies into Internet-of-Things (IoT) networks, named SWIPT-enabled cognitive IoT networks, has become an effective approach to resolve the short lifetime of battery-constrained IoT Devices (IoDs) and spectrum scarcity. In this type of networks, IoDs are regarded as secondary users (SUs) being charged with wireless power. To improve the sum throughput of IoDs, we allow IoDs to switch among spectrum sensing, SWIPT and information transmission adaptively. Correspondingly, three-dimensional resources, i.e., time (for performing the three actions), power (including power transmitted from an IoT controller to each IoD and power for receiving information and charging at each IoD) and spectrum, are jointly and adaptively allocated to maximize the sum throughput of IoDs. Since the formulated problem is a mixed-integer nonlinear program (MINLP), we adopt an auxiliary variable to convert the original problem into a tractable problem, which is then solved by an efficient algorithm involving the Lagrangian dual method, the subgradient method and the multiple one-dimensional search algorithm. Simulation results show our adaptive design yields superior performance in terms of the sum throughput of IoDs.

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.

Collaborative edge computing (CEC) can realize the cooperation and integration of heterogeneous resources distributed in adjacent areas, increasing the overall resource utilization efficiency. In a CEC-supported heterogeneous vehicular network composed of different access solutions, including cellular vehicle-to-everything (C-V2X) and dedicated short-range communications (DSRC), good network connections can guarantee timely access to edge resources. How to maintain stable and high-quality network connections for vehicles is a crucial issue. With traditional received signal strength (RSS)-based handover schemes, vehicles may encounter severe ping-pong effects and even direct handover failures leading to data packet loss. In this article, to overcome the frequent handover problem caused by vehicles’ high-speed motion and the ever-changing network environment, we propose a trajectory prediction-based handover scheme. In this scheme, the sojourn time of a vehicle staying in each candidate network’s coverage can be obtained through a social long short-term memory (social-LSTM)-based prediction model. Together with the signal strength, available bandwidth, and cost, the sojourn time is also taken as a handover decision attribute parameter. Simulation results show that our proposed scheme can reduce the number of handovers effectively.

Edge caching in collaborative edge computing (CEC) is a resource-friendly technique to improve energy efficiency and alleviate backhaul link congestion. Caching diverse contents based on the social features among users at energy-harvesting-powered (EH-powered) small base stations can further save on-grid energy, but it may lead to a longer delay to mobile users (MUs). In this article, we focus on an energy–delay tradeoff (EDT) problem in CEC-assisted and EH-powered ultradense networks and propose an EDT-oriented adaptive cooperative caching (EDT-ACC) scheme. We regard delay and energy as two types of cost and introduce a weighted cost function to transform the EDT problem into a cost minimization problem. An alternating optimization based on an improved quantum genetic algorithm (AO-IQGA) is proposed to solve the cost minimization problem. In AO-IQGA, the alternating optimization is utilized to divide the cost minimization problem into two subproblems (adaptive tuning weight subproblem and caching decision subproblem). We improve the quantum genetic algorithm in terms of repairing unfeasible solutions and adaptively updating quantum genes. Numerical results demonstrate the efficiency of the proposed AO-IQGA and illustrate the fundamental tradeoff between delay and energy consumption under different parameters, such as content popularity, storage size, and battery capacity.

Virtual reality (VR) over wireless has emerged as an important application in future mobile networks. However, it is difficult for the existing mobile networks to meet the requirements of massive data transmissions and ultra-low latency for wireless VR. Multi-access edge computing (MEC) network, providing caching and computing capacities at network edge, emerges as a promising method to support wireless VR. However, mobile VR users' quality of experience (QoE) may be degraded by frequent handoffs. In this paper, we propose a proactive caching, computing and communication (3C) resource allocation method to provide smooth VR videos to handoff users. Specifically, the expected 3-dimensional (3D) video or 2D video for rendering is cached at a target base station (BS) ahead of time, and the size and quality of the video file are decided according to the 3C resources at the BS. Then, we model the the proactive 3C resource allocation as a Markov decision process and an effective allocation policy is obtained by a model-free algorithm based on deep reinforcement learning. Numerical results show that the proposed method can provide VR users with high QoE when they are moving between BSs.

This paper investigates the performance of cooperative device-to-device (C-D2D) communications in a cellular network, where the simultaneous wireless information and power transfer (SWIPT) technology is adopted by D2D transmitters (DTs). In this network, DTs can act as relays that consume a portion of energy harvested by a time switching (TS) strategy to satisfy the quality of service (QoS) requirements of cellular users (CUs) with poor channel conditions, in exchange for spectrum resources of CUs for D2D communications. To achieve the sum-throughput maximization of the network while guaranteeing the QoS requirements of both D2D and cellular links, we formulate a novel optimization problem that jointly determines user pairing between DTs and CUs, time allocation for energy harvesting and information transmission, and power allocation at DTs for relaying information and performing D2D communications. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) problem which is computationally prohibitive. To overcome this issue, a two-step policy-based algorithm is proposed to solve the problem in polynomial time. Simulation results validate the convergence of the proposed algorithm and the effectiveness of the joint user pairing and resource allocation scheme for improving network throughput.

Wireless virtual reality (VR)-enabled medical treatment (WVMT) system, integrating the VR technology and the platform of the Internet of Medical Things (IoMT), is a promising application in future medical industries. Multiaccess edge computing (MEC) is an effective approach to support the ubiquitous applications of WVMT systems. Due to the high requirements of medical services, the computation efficiency and security are two issues in WVMT systems. In this article, we propose a blockchain-enabled task offloading scheme, where the viewport rendering tasks of VR devices (VDs) can be offloaded to edge access points (EAPs). The blockchain is integrated into the system to reach the consensus of the global information of task offloading and data processing to resist malicious attacks. To reduce VDs’ computation load under the promise of high VR QoE, we formulate the computation offloading and resource allocation to be a Markov decision problem, considering block consensus, content correlation, and fluctuating channel conditions. Then, a novel collective reinforcement learning (CRL) algorithm is proposed to adaptively allocate resources based on the requirements of viewport rendering, block consensus, and content transmission. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. simulation results demonstrate the effectiveness of the proposed scheme.

Wireless virtual reality (VR) is increasingly used in industrial Internet of Things (IIoTs). However, ultra-high viewport rendering demands and excessive terminal energy consumption restrict the application of wireless VR. Pervasive edge computing emerges as a promising method for wireless VR. In this article, we propose an energy-aware resource management scheme for wireless-VR-supported IIoTs. To reduce the energy consumption of VR equipments (VEs) while ensuring a smooth immersive VR experience, we formulate the viewport rendering offloading, computing, and spectrum resource allocation to be a joint optimization problem, considering content correlation between VEs, fluctuating channel conditions, and VR quality of experience. By applying dual approximation, the original problem is transformed to be a Markov decision process and an reinforcement learning (RL)-based online learning algorithm is designed to find the optimal policy. To improve the learning efficiency, the quantum parallelism is integrated into the RL to overcome “curse of dimensionality”. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. Simulation results demonstrate the effectiveness of the proposed scheme.

Small base stations (BSs) equipped with caching units are potential to provide improved quality of service (QoS) for multimedia service in ultra-dense cellular networks (UDCNs). In addition, the Coordinated MultiPoint (CoMP) transmission method, allowing multiple BSs to jointly serve users, is proposed to increase the throughput of cell-edge mobile terminals (MTs). Yet, the combination of content caching and CoMP in UDCNs is still not well explored for future networks. In this paper, we focus on the application of caching in CoMP-integrated UDCNs, where the cache-enabled BSs can collaboratively serve each MT using either joint transmission or single transmission. We propose a multidimensional cooperative caching (MDCC) scheme, supporting storage-dimension and transmission-dimension cooperations for the content placement. In particular, we analyze the delivery delay based on request patterns, transmission method, and the proposed cooperation strategy. Then the content placement problem is formulated as a problem of minimizing the overall expected delay. The problem is a mixed binary integer linear programming (BILP) problem, which is NP-hard. Therefore, we address the problem with approximation and substitution, and design a genetic algorithm (GA) based method to solve it. Simulation results demonstrate that the proposed MDCC scheme contributes performance gain in terms of content delivery delay in both cell-core and cell-edge areas.

To make full use of space/air resources, a Space-Air Integrated Network (SAIN) which is a hierarchical network with satellites, airships and hovering Unmanned Aerial Vehicles (UAVs) is constructed. Nowadays, Free Space Optical (FSO) links have been widely applied in SAINs since they provide high-rate and large-capacity data transmission. Unfortunately, the FSO links across the atmosphere would perform badly in adverse weather. Besides, the limited number of transceivers brings bottlenecks of link capacity and node energy. To solve these problems, in this paper, we first propose an adaptive RF/FSO switching mechanism based on predictions of atmosphere conditions, so that the high- rate and large-capacity data transmission can be achieved while overcoming the negative influences of bad weathers. Moreover, under the limited number of transceivers, we design a Dynamic Energy & Traffic Balance (DETB) topology control algorithm. Except for transmit power, both residual bandwidth and energy are taken into account for obtaining an optimal topology dynamically. Finally, a hierarchical routing policy combined with our DETB algorithm is proposed. It has been proved that our method extends the network lifetime, and the network throughput remains stable.

An integrated space/air information network is a convergence of satellite communication networks in space regions and aircraft communication networks in air regions. It supports direct internal information interactions. Along with the extensive application of the integrated space/air information network especially in military fields, its routing problem becomes a key point of research. However, the existing routing algorithms are mainly designed for the space or air region separately and there is little study on the generalized routing for the integrated space/air information network. In this paper, we propose a Hybrid time-space Graph based Hierarchical Routing (HGHR) scheme for this integrated network. The hybrid time-space graph includes two subgraphs: a deterministic one and a semi-deterministic one. As satellite orbits are pre- known in the space region, we introduce a deterministic time-space subgraph. While each aircraft has a cyclic movement with the predictable contact probability and contact time in the air region, we construct a semi-deterministic time-space subgraph according to the prediction results of a discrete time homogeneous semi-Markov model. Simulation results show that HGHR has good performance in terms of data delivery ratio and end- to-end delay.

There is growing interest in new physical-layer transmission methods based on known-interference cancellation (KIC). These KIC-based methods share the common idea that the interference can be cancelled when the bit-sequence of it is known, which can improve the efficiency of wireless data communications. Existing work on KIC mainly focuses on single-hop or two-hop networks, with physical-layer network coding (PNC) and full-duplex (FD) communications as typical examples. This paper extends the idea of KIC to multi-hop networks, and proposes a bidirectional end-to-end KIC (BE2E-KIC) transmission method for the scenario where two nodes intend to exchange packets through multiple intermediate nodes. With BE2E-KIC, the involved nodes can simultaneously transmit and receive on the same channel. We first discuss the procedure of BE2E-KIC and provide a theoretical analysis on its feasibility and effectiveness. Then, we propose a medium access control (MAC) scheme that supports BE2E-KIC, which schedules packet transmissions in more realistic cases with the presence of packet-loss. Simulation results illustrate that BE2E-KIC can improve the network throughput and reduce the end-to-end delay compared with other existing transmission methods.

Resource allocation is an important topic with a wide range of applications. In many practical cases, users and resource suppliers are players in the market. As a result, much effort has been made in applying market mechanisms (such as auction and game-theoretic results) to resource allocation. The conventional approach in such studies is to consider cases where users' resource demands are fixed. However, in practice, resource demands are often elastic, which can be related to the quality of experience (QoE) that the user receives. We consider elastic resource demands in this paper, and propose a double auction and negotiation (DAN) scheme, which includes a conventional auction stage as well as a negotiation stage, where the latter allows users to dynamically adjust their demands. The proposed DAN scheme not only allows more users to get access to some amount of resource (thereby avoiding users becoming completely disconnected), but also increases the payoff of resource suppliers, as is confirmed by simulations. We also discuss the conditions of having Nash equilibrium in the users' resource demands and suppliers' pricing in the negotiation stage.

Cooperation among nodes plays an important role in the commercial development of wireless networks. Efficient cooperation should include not only encouraging selfish nodes to forward packets for one another but also selecting optimal transmission methods. Therefore, in this paper, in order to improve network performance, we propose a social-oriented adaptive transmission scheme for wireless ad hoc networks. Firstly, next-hop node for each transmission is decided by a double auction-based social awareness mechanism. Then in the case of relay-aided transmissions, optimal relaying method is selected by jointly considering network coding and spectrum spatial reuse. Simulation results demonstrate that the proposed scheme has significant advantages in social welfare and throughput improvement.

Physical-layer network coding (PNC) is an effective strategy for increasing the throughput of wireless networks. In the current literatures, PNC without rate and power adaptation is mainly focused. Realizing that the transmission efficiency can be improved through rate and power adaptation in wireless networks, this paper focuses on developing a rate and power adaptation scheme for PNC. Through formulating how the data rate and transmission power affect the bit error rate (BER) of involved links in PNC, we observe that with a given data rate, the transmission power has to satisfy some constraints. Using these power constraints, we obtain a candidate set of optimal transmission power. By traversing the candidate set and the data rates supported by nodes, a rate and power adaptation scheme is developed. To test its performance, we apply the proposed scheme into an existing PNC-supported MAC protocol. Simulation results demonstrate that the proposed scheme can improve the throughput and delay performance in various scenarios.

We study scheduling and transmission of packets with deadline constraints in cooperative wireless networks. The packets which miss their deadlines become useless and have to be dropped. To minimize packet dropping probability, we consider multiple transmission methods and integrate packet scheduling with adaptive transmission method selection. We first introduce an exhaustive search method to obtain the optimal scheduling sequences and the corresponding transmission methods, under different channel conditions. Through observing the optimal results, we propose a heuristic method based on a dynamic graph. Simulation results show that the proposed heuristic method can obtain results which are similar to those achieved with the exhaustive search method, but with low computational complexity.

Physical-layer network coding (PNC) is a promising approach for wireless networks. It allows nodes to transmit simultaneously. Due to the difficulties of scheduling simultaneous transmissions, existing works on PNC are based on simplified medium access control (MAC) protocols, which are not applicable to general multihop wireless networks, to the best of our knowledge. In this paper, we propose a distributed MAC protocol that supports PNC in multihop wireless networks. The proposed MAC protocol is based on the carrier sense multiple access (CSMA) strategy and can be regarded as an extension to the IEEE 802.11 MAC protocol. In the proposed protocol, each node collects information on the queue status of its neighboring nodes. When a node finds that there is an opportunity for some of its neighbors to perform PNC, it notifies its corresponding neighboring nodes and initiates the process of packet exchange using PNC, with the node itself as a relay. During the packet exchange process, the relay also works as a coordinator which coordinates the transmission of source nodes. Meanwhile, the proposed protocol is compatible with conventional network coding and conventional transmission schemes. Simulation results show that the proposed protocol is advantageous in various scenarios of wireless applications.

Recently, physical-layer network coding (PNC) attracts much attention due to its ability to improve throughput in relay-aided communications. However, the implementation of PNC is still a work in progress, and synchronization is a significant and difficult issue. This paper investigates the feasibility of synchronous PNC with M-ary quadrature amplitude modulation (M-QAM). We first propose a synchronization scheme for PNC. Then, we analyze the synchronization errors and overhead of potential synchronization techniques, which includes phase-locked loop (PLL) and maximum likelihood estimation (MLE) based synchronization schemes. Their effects on the average symbol error rate and the goodput are subsequently discussed. Based on the analysis, we perform numerical evaluations and reveal that synchronous PNC can outperform conventional network coding (CNC) even when taking synchronization errors and overhead into account. The theoretical throughput gain of PNC over CNC can be approached when using the MLE based synchronization method with optimized training sequence length. The results in this paper provide some insights and benchmarks for the implementation of synchronous PNC.

Physical-layer network coding (PNC) brings throughput improvement for wireless networks. However, its synchronization requirement is widely recognized as an obstacle to its implementation. In this paper, we focus on phase-level synchronization and propose a time-slotted carrier synchronization scheme for PNC. We then analyze the phase error tolerance of PNC under different bit error rate (BER) requirements, and the synchronization overhead for obtaining synchronous signals below the phase error margin. We also consider the impact of different hardware (in particular, the phase-locked loop) parameters on the overhead in our analysis. Afterwards, we evaluate the performance of the proposed synchronization scheme with simulations. The results show that the proposed scheme is feasible with some typical hardware parameters. The throughput gain of PNC when using the proposed scheme is only slightly lower than the theoretical gain.

The denoise-and-forward (DNF) method of physical-layer network coding (PNC) is a promising approach for wireless relaying networks. In this paper, we consider DNF-based PNC with M-ary quadrature amplitude modulation (M-QAM) and propose a mapping scheme that maps the superposed M-QAM signal to coded symbols. The mapping scheme supports both square and non-square M-QAM modulations, with various original constellation mappings (e.g. binary-coded or Gray-coded). Subsequently, we evaluate the symbol error rate and bit error rate (BER) of M-QAM modulated PNC that uses the proposed mapping scheme. Afterwards, as an application, a rate adaptation scheme for the DNF method of PNC is proposed. Simulation results show that the rate-adaptive PNC is advantageous in various scenarios.

Recent theoretical studies of physical-layer network coding (PNC) show much interest on high-level modulation, such as M-ary quadrature amplitude modulation (M-QAM), and most related works are based on the assumption of phase synchrony. The possible presence of synchronization error and channel estimation error highlight the demand of analyzing the symbol error rate (SER) performance of PNC under different phase errors. Assuming synchronization and a general constellation mapping method, which maps the superposed signal into a set of M coded symbols, in this paper, we analytically derive the SER for M-QAM modulated PNC under different phase errors. We obtain an approximation of SER for general M-QAM modulations, as well as exact SER for quadrature phase-shift keying (QPSK), i.e. 4-QAM. Afterwards, theoretical results are verified by Monte Carlo simulations. The results in this paper can be used as benchmarks for designing practical systems supporting PNC.

In order to support seamless mobility in next-generation overlapping wireless networks, we develop a novel network selection scheme, which decides an optimum network through discovering a tradeoff among users' preferences, operators' benefits, network conditions and application requirements. A merit function is defined to select the best possible network for a mobile user. A negotiation process between the user and the selected network operator is developed to guarantee the operator can obtain benefits from accepting the user's handoff request. The network environment is formulated as a semi-Markov decision process (SMDP) in the negotiation process. An optimal policy that maximizes the network revenue without violating any quality-of-service (QoS) constraints is found by resolving the SMDP problem using Q-learning. The simulation results demonstrate that the proposed network selection scheme enhances the performance in terms of handoff call-dropping probability (HCDP) and network revenue.

This paper presents a time-adaptive vertical handoff decision scheme for overlapping wireless networks. The proposed scheme discovers all the reachable networks and then selects the most suitable one as the handoff target through performance evaluation that relies on user preferences and service requirements. In order to guarantee the right decisions can be made timely with the minimum battery power consumption on interface activation, two algorithms of producing dynamic activating intervals are developed. The simulation results reveal that the proposed method can effectively balance the spent time and consumed power for making handoff decisions

The predominant objective of next-generation networks (i.e. 4G) is to support high bandwidth with high mobility. 3G technologies provide low bandwidth at a cellular level, and wireless local area network (WLAN) supports much higher bandwidth at a local level. The two types of technologies are seamlessly integrated in 4G networks. In this paper, we present an efficient network selection mechanism for next-generation networks to guarantee mobile users being always best connected (ABC). Two mathematical techniques are combined in the mechanism to decide the optimum network for mobile users through finding the tradeoff among user's preference, service application, and network condition.

Abstract The success of broadband service has attracted more people to surf internet with the demand of high mobility and high bandwidth. The ongoing wireless local area network (WLAN) standardization and development activities allow WLAN to provide high data rate in unlicensed spectrum. It motivates cellular network operators to adopt WLAN as a complement to cellular 3G systems in hot spot areas. Consequently, it is imperative to develop a network selection technique to assure quality‐of‐service (QoS) for the integrated cellular/WLAN system. In this paper, an optimization scheme for mobile users to select a network in an integrated WLAN and universal mobile telecommunication system (UMTS) system is proposed. In order to provide users a prospect of being always best connected, analytic hierarchy process (AHP) and grey relational analysis (GRA) techniques are integrated to make decision. The former defines selection criteria and the latter evaluates network alternatives. The network selection module is built at the link layer, and collects the time‐varying QoS information through cross‐layer message signaling. Simulations conducted in a heterogeneous system with UMTS and WLAN show that the proposed network selection technique can successfully satisfy QoS requirements for a variety of applications through a fair decision on the optimum network for mobile users at any time. Copyright © 2005 John Wiley & Sons, Ltd.