To enhance the efficiency and quality of communications, it is crucial to employ digital pre-distortion (DPD) technology for linearizing the power amplifiers (PAs) in terahertz/mmWave transceivers. Previous studies have shown that training DPD models within the direct learning architecture (DLA) framework yields superior results, as the Transformer-based PA behavioral model in this framework can directly compute the inverse function. Owing to resource-constrained deployment environments, DPD models trained via DLA typically rely on alternative lightweight models—such as long short-term memory (LSTM) networks. However, in scenarios where only DLA is applicable, lightweight DPD models often suffer from limited linearization performance. To mitigate this limitation, drawing inspiration from related work on iterative learning control (ILC), we propose a simple yet effective cross-architecture knowledge distillation method in the DLA framework (dubbed CAKDDLA). In this method, the lightweight DPD model is trained using two sources: the PA behavioral model and input-output knowledge distilled from a teacher model. To fully verify the proposed method’s effectiveness, extensive experiments conducted on a 1-GHz dataset show that our approach outperforms baseline methods in terms of error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR), while also raising the upper bound of model performance.

To design reliable and efficient terahertz/mmWave transceivers, accurate power amplifier (PA) behavioral modeling is essential. In terahertz/millimeter wireless communication, the bandwidth will be 1 GHz or even more than 1 GHz, where PAs exhibit strong non-linearity and strong memory effects. This necessitates a powerful model capable of capturing long-term signal dependencies while managing strong non-linearity. To this end, we propose multi-scale augmented Transformer (MSAformer), which combines the ability of long short-term memory (LSTM) to capture complex sequence patterns with the self-attention mechanism’s dynamic attention adjustment, allowing it to effectively capture the intricate relationships between PA input and output signals. To fully validate the methods’ effectiveness, we collected and analyzed signals from the physical platform of the D-band system and set the bandwidth from 1 GHz to 4 GHz. Extensive behavioral modeling experiments on the collected datasets demonstrate that our method outperforms the existing methods in terms of normalized mean square error (NMSE). Further application in DPD scenarios proves that our method can effectively help improve the linearization performance of lightweight DPD models in terms of error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR).

Handover (HO) is one of the pivotal technologies for mobility management in highly dynamic mega LEO Earth Orbit satellite constellations (MLSCs). Due to the lack of channel reservation under random access (RA) and the prohibitive overhead incurred by centralized HO scheduling, intense competition among massive connections (e.g., IoRT nodes) results in significant degradation of service continuity. To solve the above challenges, we propose a distributed fairness-guided handover strategy (DHO-F) to dynamically select the best HO target and sub-channel. Specifically, the DHO-F optimization problem is formulated based on max-min egalitarian fairness and further modeled as a multi-objective Markov decision process (MOMDP), where fairness is expressed by the social welfare function (SWF). To address MOMDP, the analytical form of the policy gradient to maximize fairness is derived. Subsequently, Multi-agent Proximal Policy Optimization (MAPPO) with distributed cooperation is exploited to achieve long-term maximization. The fairness-guided MAPPO (FG-MAPPO) features a hybrid network architecture that simultaneously takes into account maximizing individual link rates and fairness among UEs. It reconciles these two conflicting objectives through the collaboration between a throughput-oriented (TO) network and a fairness-oriented (FO) network. Additionally, a distributed training framework is implemented to improve the sample efficiency and data diversity for on-policy FG-MAPPO. FG-MAPPO is fully compatible with 3GPP’s conditional handover (CHO) framework, demonstrating that it can be implemented in real-world. Extensive evaluations demonstrate that DHO-F demonstrates superior performance even compared to centralized algorithms, achieving an average improvement of 3.48% in SWF metric. Moreover, DHO-F establishes new SOTA performance in balancing fairness and rate maximization across medium-to-high load scenarios compared to IDQN, ISAC, and MAPPO.

In non-terrestrial networks, satellite constellations based on the Low-Earth-Orbit (LEO) have become crucial for ensuring seamless global connectivity. The flexible continuity guarantee is demanded for task-oriented user connection requests in satellite networks. In this paper, we propose a task-oriented satellite conditional handover scheme, D-CHO, based on multi-agent game theory. For the problem formalization, this paper focuses on delay overhead, satellite utilization deviation, and load performance to model the multi-objective optimization. According to game theory, a Nash equilibrium exists among the multi-task game strategies that require satellite links. Through exploration and exploitation, the optimal satellite handover sequence scheme can be identified. This paper explores the optimal solution based on the MAPPO algorithm, which enables multiple tasks to make independent decisions based on their partial observations without requiring global information. This approach is beneficial for the adaptive expansion in response to dynamic changes in different satellite networks. The simulation results show that D-CHO improves performance by 22%, 26%, and 11% compared to the SCDP, G-CHO, and MADDPG-CHO algorithms, respectively, and exhibits better scalability while maintaining satisfactory performance.

To ensure high-quality communication, it’s of great value to use digital pre-distortion (DPD) to linearize the core component power amplifier (PA) of terahertz/mmWave transceiver. In this work, we propose transfer learning with Transformer and LSTM for DPD of terahertz/mmWave transceiver, which uses Transformer based PA behavioral model to train effective and lightweight LSTM model for DPD. To collect and analyze the signal data of terahertz/mmWave transceiver, we set up a physical platform for the D-band system. Through experiments, we show that the proposed method is capable of significantly reducing in-band and out-band distortion while avoiding the excessive complexity of DPD models.

This paper investigates the design of integrated detection and communication waveforms with a particular focus on the exploitation of constructive interference (CI) at both the system and symbol levels. We formulate a waveform design problem with the objective of optimizing the detection performance which is characterized by the Kullback Leibler distance (KLD) between the probability density functions (PDFs) under two hypotheses, subject to the constraints of the CI condition and constant modulus. To address the challenging non-convex problem, a low-complexity recursive penalty-based Riemannian conjugate gradient (RE-PRCG) algorithm is proposed. The numerical results demonstrate the effectiveness of the proposed algorithm in improving the detection performance and reducing the computational burden over the benchmark.

To meet the demand for high-speed and high-quality communication in next 6G satellite communication, it is very necessary and urgent to study the behavioral modeling of 6G satellite communication power amplifiers (PAs). In satellite communication, PAs face the situation of high dynamic and wide bandwidth and exhibit strong nonlinearity and strong memory effects. In this case, we need to study Transformer architectures that can better handle long sequence data and further explore the inherent characteristics of the PA signal data. In this article, we propose a behavioral modeling method of PAs named augmented real-valued time-delay transformer (ARVTDform). ARVTDform is an augmented transformer-based method, which can capture long-range dependencies between the PA signal data and has powerful nonlinear modeling capabilities. To simulate the working status of the satellite PAs, we set up two physical platforms and collect and analyze twelve datasets. To the best of our knowledge, this is the first time real satellite data has been used for behavioral modeling. Extensive experiments on the collected datasets further demonstrate that our transformer-based method is more suitable for handing PAs with strong nonlinearity and strong memory effects in terms of normalized mean square error (NMSE). Finally, we discuss the major challenge and list the potential future work that may contribute to the sustained development of high-performance transceivers.

This paper proposes a novel reconfigurable intelligent surface (RIS)-aided integrated communication and positioning design for orthogonal frequency division multiplexing systems in indoor scenarios. A non-geometric strategy is employed to realize accurate positioning. Specifically, location-related information is embedded into channel frequency responses (CFR) and estimated through regular pilot subcarriers. The coefficients of RIS are optimized to maximize the norm of the CFR vector differences among users, exclusively considering physically adjacent users. To enhance positioning accuracy, we propose a two-stage framework that incorporates the prior information about the user in physical space. A unique feature, named “correlation dispersion”, within this framework is leveraged to enhance performance compared to geometric-based methods. By transforming the geometric prior information into the frequency domain capitalizing on Gaussian kernel method, we derive the Cramer-Rao Lower Bound (CRLB) of the proposed framework. A notable gain in CRLB is observed, highlighting the efficacy. Theoretical comparison with the CRLB of conventional methods validates the correlation dispersion property. Simulation results demonstrate a significant improvement in positioning accuracy when meticulously combining prior information with a non-geometric positioning method. Furthermore, our results unveil that the incorporation of rough positioning methods yields exceptionally high positioning performance, provided that the location information depicts different aspects.

This paper investigates an integrated communication and positioning (ICAP) system facilitated by rate-splitting multiple access (RSMA). We propose encoding part of users’ messages into a common stream using a public codebook, which simultaneously facilitates fingerprint-based positioning. To enhance positioning accuracy, we investigate the interplay between geometric and non-geometric spatial consistency, which intriguingly leads to improved quality of imperfect channel state information (ICSI). In particular, we establish a novel strategy to significantly reduce the minimum mean square error of channel estimation via the Bayes pooling principle. We also demonstrate the theoretical equivalence between ICSI enhancement and positioning accuracy. Moreover, we develop a progressive transmission protocol that minimizes training overhead alongside ICSI enhancement strategy while allowing for the reallocation of spare resources without compromising designed performance. Our cooperative ICAP system leverages both reconfigurable intelligent surface and transmit precoding techniques to reconfigure the spatial consistency of the propagation space. Numerical results underscore advantages of proposed system in achieving the broadest Pareto boundary among existing ICAP designs. Furthermore, we reveal that RSMA bolsters communication capabilities while the ICSI enhancement strategy predominantly augments positioning.

This paper investigates the cooperative integrated communication and positioning (ICAP) within multiple-input single-output (MISO) systems. A novel strategy that leverages positioning outcomes is presented to enhance the quality of imperfect channel state information (ICSI), thereby bridging these two functionalities. Initially, a fingerprint-based method is developed to establish positioning function with the channel frequency responses serving as identification features. We focus on the interplay of geometric and non-geometric spatial consistency to improve positioning accuracy. To this end, reconfigurable intelligent surface (RIS) and transmit precoding techniques are employed to promote the non-geometric spatial consistency while ensuring communication quality. Moreover, we refine the distribution of random channel uncertainty through the Bayes pooling principle by leveraging the reshaped spatial consistency, resulting in an updated ICSI covariance matrix. This refinement is proven to significantly enhance the quality of ICSI, as evidenced by a reduction in the minimum mean square error. Furthermore, the study introduces a progressive transmission protocol that reduces training overhead in line with the channel enhancement strategy. Numerical results validate that the proposed protocol enhances the accuracy positioning by adjusting the power allocation without compromising the performance of communication. Moreover, opting for overhead reduction rather than directly utilizing enhanced ICSI quality demonstrates superior communication performance.

The deployment of low earth orbit (LEO) satellites megaconstellations presents a promising way for achieving global coverage and service, attributed to their comparatively low round-trip latency and launch costs. However, this surge in LEO satellite launches exacerbates the scarcity of the limited spectrum resources. Spectrum sharing between satellite constellations and terrestrial networks and beam hopping (BH) technology emerge as viable strategies to mitigate this spectrum shortage. To enhance spectrum efficiency and avoid serious inter-system interference, we investigate the beam hopping scheduling of satellites for interference avoidance. The beam hopping scheduling of the integrated satellite-terrestrial wireless networks system is formulated as throughput-driven beam hopping (TDBH) problem and satisfaction-rate-driven beam hopping (SDBH) problem, respectively. In particular, we decompose the TDBH problem into two sub-problems by relaxation, and a genetic algorithm (GA) is introduced to handle the SDBH problem. The impact of channel conditions and traffic load intensity on the satellite system throughput is analyzed in TDBH simulation. As for SDBH optimization problem, the simulation results show that the proposed GA algorithm improves the average traffic satisfaction rate by 16.96% at least, compared with other benchmarks and suits to scenarios with different traffic demands and fading channel conditions.

The performance of wireless communication systems is fundamentally constrained by random and uncontrollable wireless channels. Recently, reconfigurable intelligent surfaces (RIS) has emerged as a promising solution to enhance wireless network performance by smartly reconfiguring the radio propagation environment. While significant research has been conducted on RIS-assisted wireless systems, this paper focuses specifically on the deployment of RIS in a wideband millimeter wave (mmWave) multiple-input-multiple-output (MIMO) system to achieve maximum sum-rate. First, we derive the average user rate as well as the lower bound rate when the covariance of the channel follows the Wishart distribution. Based on the lower bound of users’ rate, we propose a heuristic method that transforms the problem of optimizing the RIS’s orientation into maximizing the number of users served by the RIS. Simulation results show that the proposed RIS deployment strategy can effectively improve the sum-rate. Furthermore, the performance of the proposed RIS deployment algorithm is only approximately 7.6% lower on average than that of the exhaustive search algorithm.

With the rapid development of the MIoT(mobile Internet of things), the number of MIoT devices has increased rapidly. The collection and management of the status information continuously submitted by MIoT devices has brought great pressure to the network bandwidth of the data center. Existing edge data storage services lack the ability of data range query to support MIoT stream analysis, so we propose a cloud-edge collaborative data service, CEDS, to solve that problem. It uses edge computing nodes to store data sent by devices nearby and uses a central node to manage metadata and query data on edge nodes. In order to reduce the network transmission during data query, we use query splitting and pushdown optimization techniques to make each edge node only return aggregated data within the query range. To improve the query latency of global search, we propose an edge data indexing mechanism based on the compressed Rosetta filter. We test the CEDS performance with the taxi management task. Experiments illustrate that CEDS can efficiently support storing and range query of MIoT data streams with a small network traffic cost.

In the case of explosive content requests being generated by numerous vehicles in rush hour, the cellular downlink resources become insufficient. This paper considers two supplementary schemes for exploiting the uplink resources: One is letting vehicles cache the browsed contents so that the vehicles can share contents with one another; and the other is dispatching a cache-enabled UAV to transmit contents to the vehicles nearby. We formulate a joint optimization problem for maximizing the average data rate of vehicles, and then decompose it into the subproblems of client association and UAV scheduling. The client association aims at matching the vehicles to the resources, and the UAV scheduling is to update the UAV's caching and trajectory to adapt to the environment changing. The two subproblems focus respectively on the current condition and the long term profit, and are solved respectively by a matching-based algorithm and a deep reinforcement learning-based algorithm. Our simulation results based on a real-world traffic data set demonstrate the advantages of the proposed approaches.

In the next generation of cellular communications, ultra-dense networking is a key to ensuring the coverage of millimeter-wave systems. On the other hand, hybrid precoding (HP) has been recognized as a promising technology for millimeter wave-based massive multiple-input multiple-output (MIMO) systems. However, few research efforts have been invested in handling multi-cell interference with hybrid precoding structure. In this paper, we propose a multi-cell interference processing scheme based on the ant colony system (ACS) method for massive MIMO interfering broadcast channels with hybrid precoding structure. Specifically, we first formulate the beam selection problem in the radio frequency (RF) domain as a traveling problem. Then with the goal of minimizing inter-cell signal-to-leakage-ratio (SLR), an ACS-based algorithm is proposed to provide the RF precoder design for each BS. Meanwhile, in each cell, the baseband combiner of each user is obtained by the singular value decomposition (SVD) method to maximize the corresponding channel gain, and the baseband precoder is provided by zero-forcing (ZF) algorithm to eliminate inter-user interference (IUI). Simulation results show that the proposed scheme can achieve effective beam selection to manage the multi-cell interference under massive MIMO broadcast channels, and the advantages of the proposed scheme in improving the sum rate for multi-cell systems compared with existing schemes.

The implementationof computation offloading is a challenging issue in the remote areas where traditional edge infrastructures are sparsely deployed. In this study, the authors propose a unmanned aerial vehicle (UAV)‐enabled edge computing framework, where a group of UAVs fly around to provide the near‐users edge computing service. They study the computation migration problem for the complex missions, which can be decomposed as some typical task‐flows considering the inter‐dependency of tasks. Each time a task appears, it should be allocated to a proper UAV for execution, which is defined as the computation migration or task migration. Since the UAV‐ground communication data rate is strongly associated with the UAV location, selecting a proper UAV to execute each task will largely benefit the missions response time. They formulate the computation migration decision making problem as a Markov decision process, in which the state contains the extracted observations from the environment. To cope with the dynamics of the environment, they propose an advantage actor–critic reinforcement learning approach to learn the near‐optimal policy on‐the‐fly. Simulation results show that the proposed approach has a desirable convergence property, and can significantly reduce the average response time of missions compared with the benchmark greedy method.

With the unprecedented development of smart mobile devices (SMDs), e.g., Internet-of-Things devices and smartphones, various computation-intensive applications are explosively increasing in ultradense networks (UDNs). Mobile-edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of SMDs and decrease service latency for computation-intensive applications. With the benefits of network function virtualization, MEC can be integrated with the cloud radio access network (C-RAN) in UDNs for computation and communication cooperation. However, with stochastic computation task arrivals and time-varying channel states, it is challenging to offload computation tasks online with energy-efficient computation and radio resource management. In this article, we investigate the task offloading and resource allocation problem in MEC-enabled dense C-RAN, aiming at optimizing network energy efficiency. A stochastic mixed-integer nonlinear programming problem is formulated to jointly optimize the task offloading decision, elastic computation resource scheduling, and radio resource allocation. To tackle the problem, the Lyapunov optimization theory is introduced to decompose the original problem into four individual subproblems which are solved by convex decomposition methods and matching game. We theoretically analyze the tradeoff between energy efficiency and service delay. Extensive simulations evaluate the impacts of system parameters on both energy efficiency and service delay. The simulation results also validate the superiority of the proposed task offloading and resource allocation scheme in dense C-RAN.

Unmanned aerial vehicle (UAV)-assisted communication is envisioned as a potential solution to the data traffic explosion in the massive machine-type communications (mMTC) scenario. In this article, we investigate the UAV-assisted cellular networks, where a UAV acts as a flying relay to offload part of the data traffic from the overloaded cell to another. We utilize the practical spatial distribution of data traffic and a convincing air-to-ground channel model. The quality of service (QoS) is defined as a UAV utility function which is designed based on a packet loss ratio (PLR)-related users' cost function to represent the performance improvements brought by the UAV. We formulate a joint optimization problem to maximize the UAV utility function and then decompose it into the subproblems about the access point selection and the UAV path planning, which influence the PLR by influencing the packet collision rate and channel state. Since the access point selection subproblem is NP-hard, a game-theory-based distributed algorithm is proposed, instructing the users to select the base station (BS) or the UAV as the access point autonomously. To achieve the most superior channel state, we solve the UAV path planning subproblem by a deep reinforcement learning (DRL)-based approach, instructing the UAV to take the optimal action in each position. The simulation results show that the proposed access point selection scheme can significantly reduce the average cost of users and the proposed UAV path planning method can achieve a path with smaller average channel pathloss compared with other approaches.

Caching is a very effective way to offload data traffic; and when users participate in the caching game, the costs are greatly reduced. Therefore, this paper proposes a novel scheme that user terminals (UTs) cooperatively cache popular services to the intelligent routing relay (IRR) side. We use reverse auction model to motivate UTs to collaborate and cache, since UTs are rational and selfish. In this model, IRR purchases popular services from UTs and assigns rewards to UTs. UTs use personal data traffic to obtain popular services and then cache them to the IRR side. In order to minimize UTs' waiting time while maximizing total social incomes, we use an online reverse auction strategy, first-come-first-served (FCFS) strategy to allocate winning services to UTs. Simulation results verify the effectiveness of the FCFS strategy and show that the performances of FCFS are better than those of random allocation (RA) strategy in terms of incomes, completion rate and user waiting time. Experimental results further verify the authenticity, feasibility and the efficiency of the proposed scheme.

By employing the exponentially increasing communication and computing capabilities of vehicles brought by the development of connected and autonomous vehicles, vehicular cloud computing (VCC) can improve the overall computational efficiency by offloading the computing tasks from the edge or remote cloud. In this paper, we study the computation migration problem in VCC, where a vehicle transfers unfinished computing missions to other vehicles before leaving a network edge to avoid mission failures. Specifically, we consider a computing mission offloaded from edge cloud to the vehicular cloud. The mission has a linear logical topology, i.e., consisting of tasks which should be executed sequentially. The migration problem is formulated as a sequential decision making problem aiming to minimize the overall response time. Considering the vehicular mobility, communication time, and heterogeneous vehicular computing capabilities, the problem is difficult to model and solve. We thus propose a novel on-policy reinforcement learning based computation migration scheme, which learns on-the-fly the optimal policy of the dynamic environment. Numerical results demonstrate that the proposed scheme can adapt to the uncertain and changing environment, and guarantee low computing latency.

In this paper, a low-complexity detector based on the multi-user sparse Bayesian learning (MSBL) method is proposed for the multi-user spatial modulation (SM) multiple-input-multiple-output (MIMO) system. Firstly, we formulate the multiple access channel SM (MAC-SM) detection as a sparse recovery problem with fixed sparsity constraint. Then, by exploiting the characteristic of the SM transmit signal, we coarsely detect all the potential positions of active antennas. Finally, we select the maximum likely set of the index of active antennas from all user and utilize the zeros-forcing (ZF) estimate to recover the modulation signals. In addition, we theoretically analyze the complexity of proposed algorithm. Experiment and simulation results demonstrate that the proposed detector achieves a good tradeoff between performance and computational complexity.

Hybrid precoding has been recognized as a promising technology for millimeter wave-based massive MIMO systems. As an improved scheme for conventional hybrid precoding structures, the adaptively-connected structure requires low hardware cost and has attractive system performance. In this paper, we propose an adaptive hybrid precoding (AHP) scheme for multi-user massive MIMO systems. The hybrid precoder design is formulated as minimizing the difference between the hybrid precoder and the full digital precoder, and a rough design and revision based AHP (RDR-AHP) algorithm is developed to seek for a near-optimal solution. The proposed RDR-AHP algorithm is achieved by a three-step process including rough design of baseband precoder based on singular value unification, stepwise design of radio frequency precoder and baseband precoder revision. Simulation results demonstrate that the proposed scheme achieves higher spectral efficiency than existing schemes with same hardware cost in millimeter wave channel.

The limited computing ability of mobile device constrains its performance on complex mobile applications. Mobile cloud computing (MCC) has therefore emerged to migrate computation-intensive tasks to remote clouds or mobile cloudlets. Most strategies allocate tasks with minimal response time yet few consider the load of the nodes. In this paper, we focus on the load balancing problem for nodes when they conduct offloading. We first establish a five-tuple characterized task model to capture the response time of offloaded tasks. Then, we formulate the task allocation problem as an integer linear problem (ILP) under certain conditions. Furthermore, we propose a two-step appointment- driven strategy to solve this problem with minimal task response time. Specifically, a modified genetic algorithm (GA) is adopted to coordinate the load of the nodes. Simulations are conducted to prove the feasibility of our strategy and evaluate the performance of load coordination.

Internet of vehicles (IoV) is an emerging paradigm for accommodating the requirements of future intelligent transportation systems (ITSs) with the overwhelming trend of equipping vehicles with versatile sensors and communications modules, and facilitating drivers and passengers with a variety of innovative ITS applications. However, the implementation of IoV still faces many challenges, such as flexible and efficient connections, quality of service guarantee, and multiple concurrent support requests. To this end, in this paper we introduce the software-defined IoV (SD-IoV), which is able to tackle the above-mentioned issues by adopting the software-defined networking framework. We first present the architecture of SD-IoV and develop a centralized vehicular connection management approach. Then, we aim to allocate dedicated communications resources and underlying vehicular nodes to satisfy each service. We formulate the dynamic vehicular connection as an overlay vehicular network creation (OVNC) problem. A comprehensive utility function is also designed to serve as the optimization objective of OVNC. Finally, we solve the OVNC problem by developing a graph-based genetic algorithm and a heuristic algorithm, respectively. Extensive simulation results are provided to demonstrate the effectiveness of our proposed solution of dynamic vehicular connection management.

Multi-radio multi-channel (MRMC) is a promising approach to relieve the overload caused by the explosive growth of video streaming traffic in wireless mesh networks (WMNs). Previous studies have shown that in MRMC WMNs, network capacity can be increased significantly by proper design of channel assignment and routing algorithm. Multi-path routing can make good use of the network capacity improvement of MRMC WMNs. Multi-path routing has been applied in wired and wireless networks for load balancing or congestion control. However, it remains a challenge in MRMC WMNs. In this paper, we first discuss how to find multiple high-quality paths from source to destination while considering the interference between each other. Then we focus on the rate allocation among multiple paths and formulate it as a max-min problem which can be transformed to a linear programming (LP) problem. Finally, we propose a joint multi-path discovery and rate allocation algorithm. We evaluate this algorithm through simulations. Results show that our algorithm not only increases the network capacity, but also keeps the average end-to-end delay over all video streaming sessions at a low level.

Cellular networks are now facing severe traffic overload problems due to the explosive growth of mobile data traffic. One of the promising solutions is to offload part of the traffic through WiFi. In this paper, we investigate an oligopoly offloading market, where several Mobile Virtual Network Operators (MVNOs) compete to serve end users using the network infrastructure leased from the host Mobile Network Operator (MNO) at the wholesale market. First, we study the competitive interactions among the MVNOs considering the overload problems of the offloading market. Specially, we formulate the interactions as a non-cooperative inventory competition game, where each MVNO determines the amount of cellular traffic it can provide to end users (named as the traffic inventory of each MVNO in this paper) simultaneously. We analyze and derive the existence of the Cournot equilibrium using game theory. Furthermore, we study the impact of the MNO's wholesale price strategy on the market equilibrium. Based on these analysis, we find the optimal initial inventory strategy for these competitors according to the Cournot equilibrium. Finally, our simulations present the process of achieving the market equilibrium and illustrate the impact of the host MNO to the MVNOs.

The TV white spaces (TVWS) enabled infostation has received significant attention due to its wide area coverage for cost-effective and media-rich content dissemination. In this paper, we engineer WhiteFi infostation, which is dedicated for Internet-based vehicular media streaming by leveraging geolocation database. After demonstrating the empirical observations of unique TVWS features and analyzing the real-world TVWS data collected from geolocation database, we first propose an optimal TVWS network planning to deploy WhiteFi infostation with the objective of maximizing network-wide throughput. The proposed TVWS network planning jointly considers the multi-radio configuration and the channel-power tradeoff, which can be realized by decentralized Markov approximation. Furthermore, we introduce a location-aware contention-free multi-polling access scheduling scheme for vehicular media streaming, which considered both the realistic vehicular applications and dynamics of wireless channel conditions. Through extensive simulations with real-world empirical TVWS data and urban vehicular traces, we demonstrate that our WhiteFi infostation solution can well support both the delay-sensitive and delay-tolerant vehicular media streaming services.

Abstract Distributed caching‐empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience (QoE). However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized–reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract In delay and disruption tolerant networks, the contacts among nodes are intermittent. Because of the importance of data access, providing efficient data access is the ultimate aim of analyzing and exploiting disruption tolerant networks. Caching is widely proved to be able to improve data access performance. In this paper, we consider caching schemes for broadcasting and multicasting to improve the performance of data access. First, we propose a caching algorithm for broadcasting, which selects the community central nodes as relays from both network structure perspective and social network perspective. Then, we accommodate the caching algorithm for multicasting by considering the data query pattern. Extensive trace‐driven simulations are conducted to investigate the essential difference between the caching algorithms for broadcasting and multicasting and evaluate the performance of these algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

At present, single network and technology could be impotent when facing: 1) the rocketing proliferation of mobile devices; and 2) the heterogeneity of data services and users' contexts. Therefore, offloading some specific traffic to other networks is a natural yet effective solution. To this end, this paper engineers the cellular offloading oriented vehicular ad hoc networks (VANETs), which concentrate on offloading users' bandwidth-hungry traffic in vehicular environment. Specifically, a two-phase resource allocation process is adopted, where utilization patterns of both wireless and backhaul links are studied for the sake of resource exploitation efficiency, with considerations on practical issues including link quality variety, fairness and caching. The former is formulated as an integer linear programming (ILP) problem aiming at system throughput maximization, and a heuristic algorithm is developed as solution due to the problem's NP-hardness nature. The latter's objective is identified and a simple implementation algorithm is designed correspondingly. Extensive simulations are conducted and the results show the effectiveness and efficiency of the proposed methods.

Distributed data storage (DDS) provides a promising approach to the reliable recovery of the whole sensor readings in a wireless sensor network (WSN) by visiting a small subset of sensor nodes. Various DDS schemes based on compressive sensing (CS) have been proposed to reduce the number of transmission/receptions to improve network's area energy efficiency. However, these schemes assume that sensor readings are compressible in the discrete cosine transformation (DCT) domain, whereas our experimental results validate that this assumption cannot be established in a real WSN scenario and the performance of the practical sensor readings recovery will be significantly degraded. To address this problem, this paper proposes a novel DDS scheme termed as practical spatiotemporal compressive network coding (P-STCNC). Our idea is to adaptively train the sparse dictionaries to sparsify practical sensor readings as well as optimize corresponding measurement matrices in both spatial and temporal domains to guarantee accurate data recovery. Simulation results based on real datasets demonstrate the effectiveness of the proposed scheme.

Driven by the ever-growing expectation of ubiquitous connectivity and the widespread adoption of IEEE 802.11 networks, it is not only highly demanded but also entirely possible for in-motion vehicles to establish convenient Internet access to roadside WiFi access points (APs) than ever before, which is referred to as Drive-Thru Internet. The performance of Drive-Thru Internet, however, would suffer from the high vehicle mobility, severe channel contentions, and instinct issues of the IEEE 802.11 MAC as it was originally designed for static scenarios. As an effort to address these problems, in this paper, we develop a unified analytical framework to evaluate the performance of Drive-Thru Internet, which can accommodate various vehicular traffic flow states, and to be compatible with IEEE 802.11a/b/g networks with a distributed coordination function (DCF). We first develop the mathematical analysis to evaluate the mean saturated throughput of vehicles and the transmitted data volume of a vehicle per drive-thru. We show that the throughput performance of Drive-Thru Internet can be enhanced by selecting an optimal transmission region within an AP's coverage for the coordinated medium sharing of all vehicles. We then develop a spatial access control management approach accordingly, which ensures the airtime fairness for medium sharing and boosts the throughput performance of Drive-Thru Internet in a practical, efficient, and distributed manner. Simulation results show that our optimal access control management approach can efficiently work in IEEE 802.11b and 802.11g networks. The maximal transmitted data volume per drive-thru can be enhanced by 113.1% and 59.5% for IEEE 802.11b and IEEE 802.11g networks with a DCF, respectively, compared with the normal IEEE 802.11 medium access with a DCF.

The recent advances in wireless communication techniques have made it possible for fast-moving vehicles to download data from the roadside communications infrastructure [e.g., IEEE 802.11b Access Point (AP)], namely, Drive-thru Internet. However, due to the high mobility, harsh, and intermittent wireless channels, the data download volume of individual vehicle per drive-thru is quite limited, as observed in real-world tests. This would severely restrict the service quality of upper layer applications, such as file download and video streaming. On addressing this issue, in this paper, we propose ChainCluster, a cooperative Drive-thru Internet scheme. ChainCluster selects appropriate vehicles to form a linear cluster on the highway. The cluster members then cooperatively download the same content file, with each member retrieving one portion of the file, from the roadside infrastructure. With cluster members consecutively driving through the roadside infrastructure, the download of a single vehicle is virtually extended to that of a tandem of vehicles, which accordingly enhances the probability of successful file download significantly. With a delicate linear cluster formation scheme proposed and applied, in this paper, we first develop an analytical framework to evaluate the data volume that can be downloaded using cooperative drive-thru. Using simulations, we then verify the performance of ChainCluster and show that our analysis can match the simulations well. Finally, we show that ChainCluster can outperform the typical studied clustering schemes and provide general guidance for cooperative content distribution in highway vehicular communications.

Dynamic spectrum access (DSA) can be leveraged by introducing external spectrum sensing for secondary users (SUs) to overcome the hidden primary users (PUs) problem and improve spectrum utilization. In this paper, we investigate the DSA networks with external sensors, i.e., external sensing agents, to utilize spectrum access opportunities located in cellular frequency bands. Considering the diversity of SUs' demands and the secondary bandwidths discovered by external sensors, it is critical to manage the detected spectrum resources in an efficient way. To this end, we formulate the resource management problem in the DSA networks as a dynamic resource demand-supply matching problem, and propose a cooperative matching solution. Specifically, spectrum access opportunities are classified into two types by the resource block size: massive sized blocks and small sized blocks. For the former type, SUs are encouraged to share the whole time-frequency block via forming coalitional groups with a "wholesale" sharing approach. For the latter type, the resource "aggregation" sharing approach is proposed to meet the time-frequency demand of individual SUs. To further reduce the delay in the spectrum allocation and compress the matching process, we develop a distributed fast spectrum sharing (DFSS) algorithm, which can deal with both two aforementioned types of resource sharing cases. Simulation results show that the DFSS algorithm can adapt to the dynamic spectrum variations in the DSA networks and the average utilization of detected spectrum access opportunities reaches nearly 90%.

Asynchronous system provides great performance improvement for wireless ad hoc networks, such as anti-jamming, collision reduction and device simplification. Nevertheless, new media access and routing protocols are required to assist the asynchronous system, e.g., a neighbor discovery algorithm, which is the first step in the initialization of wireless ad hoc networks. In the past few years, a number of algorithms have been proposed for neighbor discovery. However, most of them only consider synchronous system and cannot work efficiently in asynchronous system. In this paper, firstly, we propose an analytical model for an 1-way asynchronous system in wireless ad hoc networks with directional antennas. Then, we compare the time-slot consumption in asynchronous system to complete the neighbor discovery process with that in synchronous system. Finally, in order to improve the performance of the neighbor discovery process, we extend the 1-way asynchronous discovery algorithms to a 2-way asynchronous discovery algorithm. To the best of our knowledge, this is the first practical analytical model of 2-way asynchronous neighbor discovery algorithm with directional antennas.

The recent advances in wireless communication techniques have made possible for vehicles to download from the roadside communications infrastructure, namely drive-thru Internet. However, due to the fast-motions, harsh and intermittent wireless channels, the download volume of individual vehicles per drive-thru is quite limited as observed in real-world tests. This severely restricts the service quality of upper-layer applications, such as file download and video streaming. To address this issue, we take a historical approach by evaluating the integrated download throughput of a cooperative vehicle group in the highway environment. In specific, we first introduce a practical microscopic vehicular mobility model, which takes the randomness of speed update and safety distance requirement into account. Then, we analyze and formulate the number of contending vehicles within the coverage range of access point (AP) in the single-lane highways scenario, which can also be easily extended into the multi-lane highways scenario. Furthermore, we derive the data download volume by a vehicle per drive-thru, and analyze the relationship between the mobility speed and the data download volume. Finally, we derive the number of cooperative vehicles required for completing a download task in our investigated highways drive-thru Internet. The analytical model and evaluation results provide general guidance for cooperative content distribution and protocol design in drive-thru Internet.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system operating with high mobility is very challenging. This is mainly due to the significant Doppler spread, inherent in a time-frequency doubly-selective (DS) channel. Consequently, a large number of channel coefficients must be estimated, forcing the need for allocating a large number of pilot subcarriers. To address this problem, we propose a novel channel estimation method based on basis expansion models (BEMs) and distributed compressive sensing (DCS) theory. To be specific, we develop a two-stage sparse BEM coefficients estimation method, which can effectively combat the Doppler spread and enable accurate channel estimation with dramatically reduced number of pilot subcarriers. The numerical results reveal that, in a typical LTE system configuration, the proposed scheme can increase the spectral efficiency by 40% and achieve a 6 dB gain in terms of normalized mean square error (NMSE), both compared to the conventional scheme.

Carrier-frequency offset (CFO) estimation for orthogonal frequency-division multiplexing access (OFDMA) systems operating in multiuser uplink transmission is very challenging due to the presence of a multiple-parameter estimation problem. In this paper, we propose a novel blind CFO estimation method for interleaved OFDMA uplink based on distributed Bayesian compressive sensing (DBCS) theory. Considering the received signal structure, the new method first constructs a measurement matrix associated with a sparse signal matrix weight, which sets up the stage for the application of CS theory in tackling the original estimation problem. Then, the DBCS theory that exploits a common sparse profile of the sparse signal matrix weight is employed to distributively estimate a sparse hyperparameter vector, whose significant peaks are linked to the correct estimation of the multiple CFOs. Compared with the existing subspace theory based methods, the proposed scheme offers a significant enhancement in estimation accuracy, in specific in the low signal-to-noise ratio (SNR) region. The numerical results validate the effectiveness of the proposed scheme.

Limited feedback precoding (LFP) significantly improves multiple-input multiple-output (MIMO) spatial multiplexing link reliability with a small amount of feedback from the receiver back to the transmitter. One of the key problems linked to LFP is how to select an optimal precoder from a pre- determined unitary codebook. We find that the conventional precoder selection criteria are not applicable to the stream maximization transmission (SMT) mode with linear receivers, including zero forcing (ZF) and minimum mean square error (MMSE) decoders. To solve this issue, a novel singular value decomposition (SVD) based precoder selection criterion is proposed in this paper. This criterion features a unified structure for all the linear receivers such as ZF and MMSE decoders, and is shown by simulation to provide significant coding gains in various SMT systems. With the same complexity as the conventional one, the proposed criterion could find its applications in next generation systems employing SMT spatial multiplexing, significantly improving system performance with affordable feedback requirement.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a doubly selective channel is very challenging. This is mainly due to the significant Doppler shift, which results in a time-frequency doubly-selective (DS) channel. The DS channel features a large number of channel coefficients, which introduces inter-carrier interference (ICI) and forces the need for allocating a large number of pilot subcarriers. To tackle this problem, in this paper we propose a novel channel estimation scheme based on distributed compressive sensing (DCS) theory. Taking advantage of the basis expansion model (BEM) and the channel sparsity in the delay domain, we transform the original DS channel into a novel two-dimensional channel model, where several jointly sparse BEM coefficient vectors become the estimation goal. Then a special decoupling form originating from a novel sparse pilot pattern is designed for such estimation, which results in an ICI-free structure and enables the DCS application to make joint estimation of these vectors accurately. Combined with a smoothing treatment process, the proposed scheme can achieve significantly higher estimation accuracy than the existing ones, although with a much smaller number of pilot subcarriers. Theoretical analysis and simulation results both confirm its performance merits.

This paper proposes an algorithm of data subcarrier designing to apply the tone reservation(TR) peak-to-average power ratio (PAPR) reduction algorithm in OFDM-based wireless communication systems and overcome the high computational cost issue. Different from the existing works, the proposed algorithm focuses on designing data subcarriers, controlling both the iteration times and the number of subcarriers. The new algorithm exhibits similar performance as the traditional TR algorithm with lower computational complexity and the ability to control the number of used subcarriers. Simulation results show that the proposed algorithm can significantly reduce the PAPR by only 2 or 3 iterations.

Low-density parity-check (LDPC) codes are playing an important role in modern wireless communication systems such as WiMAX due to their Shannon limit approaching error correction performance. To lower the decoding threshold of LDPC codes, this paper develops a novel multi-layer iterative decoding scheme using deterministic bits for multimedia communication systems. These deterministic bits serve as known information in the LDPC decoding process to reduce the redundancy during data transmission. Unlike the existing work, our proposed scheme addresses the controllable deterministic bits, such as MPEG null packets, rather than the widely investigated protocol headers. Simulation results show that our proposed scheme can achieve considerable gain in WiMAX networks.

Directional antennas provide great performance improvement for wireless networks, such as increased network capacity and reduced energy consumption. Nonetheless new media access and routing protocols are required to control the directional antenna system. One of the most important protocols is neighbor discovery, which is aiming at setting up links between nodes and their neighbors. In the past few years, a number of algorithms have been proposed for neighbor discovery with directional antennas. However, most of them cannot work efficiently when taking into account the collision case that more than one node exist in one directional beam. For practical considerations, we propose a new neighbor discovery algorithm to overcome this shortcoming. Moreover, we present a novel and practical mathematical model to analyze the performance of neighbor discovery algorithms considering collision effects. Numerical results clearly show our new algorithm always requires less time to discover the whole neighbors than previous ones. To the best of our knowledge, it is the first complete, practical analytical model that incorporates directional neighbor discovery algorithms.

Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. CS enables the recovery of high-dimensional sparse signals from much fewer samples than usually required. Further, quite a few recent channel measurement experiments show that many wireless channels also tend to exhibit sparsity. In this case, CS theory can be applicable to sparse channel estimation and its effectiveness has been validated in point-to-point (P2P) communication. In this work, we study sparse channel estimation for two-way relay networks (TWRN). Unlike P2P systems, applying CS theory to sparse channel estimation in TWRN is much more challenging. One issue is that the equivalent channels (terminal-relay-terminal) may be no longer sparse due to the linear convolutional operation. On this basis, novel schemes are proposed to solve this problem and effectively improve the accuracy of TWRN channel estimation when using CS theory. Extensive numerical results are provided to corroborate the proposed studies.

Directional antennas offer great performance improvement for mobile ad hoc networks, but this improvement requires new mechanisms at medium access and networking layer. One of the most important protocols is neighbor discovery aiming at setting up links between nodes and their neighbors. This paper proposes a mathematical framework of a scan-based algorithm (SBA) for neighbor discovery, taking into account the case that more than one node exist in one directional beam. The analysis adopts detailed model for the discovery process and derives expression for the average number of slots required to discover all the neighbor nodes. Numerical results indicate the superiority of the proposed model.

Time efficiency is a basic characteristic for spectrum management and sharing in cognitive radio networks (CRN). In this paper, we introduce an incremental metric time into dynamic spectrum management and sharing problem, which could make the spectrum resource management more reasonable. On the one hand, to motivate the idle spectrum holders to share the spectrum more actively and regulate the spectrum leasing markets better , a spectrum management rule (SMR) is introduced. Accordingly, a spectrum lease rule (SLR) is proposed from the perspective of factual application requirements of unlicensed users (UUs). One the other hand, we formulate the spectrum sharing process as a two-dimensional packing problem, in which UUs utilize the spectrum resource through forming cooperative groups (CG). Finally, a distributed fast spectrum sharing algorithm (DFSS) is proposed to realize the process of CG forming promptly. Simulation demonstrates that DFSS algorithm can adapt to the cognitive radio networks effectively, and the average spectrum utilization ratio can reach about 83.42%.

In this paper, we propose a modification to Belief Propagation (BP) decoding algorithm for LDPC codes. The modification is to attenuate the check to bit extrinsic logarithm likelihood ratio by a factor α, when sudden sign change happens. This modification can be applied to both the standard BP algorithm and the joint row and column (JRC) BP algorithm. Simulation results show that the BER and WER performance of both traditional BP and JRC BP algorithms is improved by this method. The expense of the proposed modification is a slight increase in the average number of decoding iterations.

This paper investigates the design of LDPC codes over mobile DTV multipath channels. Most of the existing LDPC codes are optimized for additive white Gaussian noise (AWGN) channel, and not feasible to encounter the long burst error occurring in mobile DTV channel. Accordingly, we study the error propagation statistics of decision feedback equalizer (DFE) and formulate it into a gated noise model. To achieve good error correction in burst error channel, we proposed a class of dual-degree IRA codes to balance the metrics of decoding threshold and robustness. Extensive simulation results are presented in this paper to justify the performance of dual-degree IRA codes over gated noise model.

This paper investigates the use of ATSC M/H digital television (DTV) signal for location finding. In comparison to satellite based location finding system, DTV signals have higher field strength, wider bandwidth, lower frequency band, and DTV transmission towers are pervasively available everywhere. They can be used for indoor and mobile location finding in major cities where satellite based system might not function well. The ATSC receiver can obtain the multiple transmitter impulse responses and signal arrival times using the embedded RF watermark (RFWM) signal, and then derives its geographic coordinates based on the position of ATSC transmitters. As a critical step of this process, the transmitter identification in mobile environment has significant impact on the overall accuracy of location finding. In this paper, we present extensive analytical and simulation results to demonstrate the performance of RFWM technology over mobile channels.