Modern satellite networks are transitioning from monolithic designs to microservice architectures, introducing complex spatio-temporal patterns, varied on-board processing capabilities, and high mobility with dynamic topologies. These features require accurate prediction of inter service dependencies for optimal resource allocation and system management. To address these challenges, this paper introduces the Spatio-Temporal Graph Neural Network with Orbital contextual features (STGNN-O). This model incorporates orbital information as contextual features, processes spatial dependencies through multi-head graph attention networks, and captures temporal patterns at three different timescales to complete satellite service performence metrics prediction, which refers to forecasting key performance indicators of microservices running on satellite platforms. Also, due to the lack of real-world relavent dataset, a comprehensive satellite service benchmark dataset is created based on real-world parameters and service patterns across multiple orbital configurations. Experiments demonstrate that STGNN-O significantly outperforms state-of-the-art baselines, achieving substantial improvements in prediction accuracy. Ablation studies confirm that the integration of orbital information and multi-scale temporal features significantly contributes to prediction accuracy across all orbital regimes.

Orthogonal frequency-division multiplexing (OFDM) is widely adopted in narrowband Internet of Things (NB-IoT). Nevertheless, the OFDM system is highly sensitive to the impairments caused by imperfect radio-frequency hardwares, which may greatly jeopardize the orthogonality between different subcarriers and degrade the demodulation performance. Though large efforts have been devoted to the hardware impairment estimation, however, it is highly challenging to jointly estimate multiple hardware impairments due to their coupling effects, especially for the NB-IoT systems which usually work in low signal-to-noise ratio (SNR) regions with limited computing and radio resources. In this article, we propose a parallel multitask learning (MTL) estimator to jointly estimate carrier frequency offset and in- and quadrature-phase imbalance in NB-IoT systems. Specifically, MTL is introduced to extract the inherent correlations between different hardware impairments so as to address the coupling effect and average the noise impact. In addition, we propose a parallel structure and a sliding window scheme to reduce the network complexity and decrease the estimation bias. Numerical results show that our proposed parallel MTL estimator can jointly estimate multiple hardware impairments with short pilot sequences, and outperform the conventional methods in terms of estimation accuracy and computation time in the typical SNR regions of the IoT devices.

The automatic recognition of synthetic aperture radar (SAR) targets has been extensively studied in recent years. Specifically, due to the high cost of SAR image acquisition and the low occurrence probability of high-value targets, it is of great importance to identify SAR targets with only a few available images, which is referred to as few-shot SAR target classification. However, most existing solutions straightly adopt meta-learning and transfer learning methods that are originally designed for optical images, which do not consider the unique frequency information of SAR images. In this paper, we propose a novel hybrid classification network that combines both spatial and frequency information for few-shot SAR target classification. Specifically, we first train the proposed network in a source dataset, which contains a large number of related SAR images without the targets of interest. Then, the pre-trained network is fine-tuned on the target dataset consisting of only a few SAR images of interest. Compared with the conventional method based on convolutional neural networks and model-agnostic meta-learning, the proposed method can achieve superior top-1 accuracy in various settings.

In orthogonal frequency division multiplexing (OFDM) systems, carrier frequency offset (CFO) and in- and quadrature-phase (IQ) imbalance are two critical hardware impairments that may lead to severe amplitude and phase mismatches and therefore greatly degrade the demodulation performance. Due to the noise impact and the coupling effects between different hardware impairments, it is considered to be highly challenging to perform joint estimation of CFO and IQ imbalance. In this paper, we propose a novel multi-task learning-based hardware impairment estimation network (HIENet) to simultaneously estimate the CFO and IQ imbalance in OFDM systems. The proposed HIENet can address the noise impact by averaging the task-dependent noise patterns and overcome the coupling effects by extracting correlated features from different estimation tasks. Numerical results show that the proposed HIENet can achieve comparable estimation accuracy to conventional one-shot and iterative methods, while at the same time having the smallest computation time.

In dynamic spectrum access (DSA), unlicensed secondary users can estimate the idle probability of each primary channel by using historical sensing results and access the channel with the highest idle probability for opportunistic transmission. Most of the existing works assume that each primary channel is associated with a constant idle probability, which can be accurately estimated by sensing the channel multiple times. However, due to the rapid traffic change and irregular user mobility, primary channels can be highly dynamic and the associated idle probability is generally time-varying. In this paper, we consider DSA in non-stationary environments where the idle probabilities of primary channels vary with time. Specifically, we propose a DSA scheme based on the Thompson sampling method with a change-detection technique, which is capable of detecting the variation of channel statistics and adjusting the channel access strategy accordingly. Numerical results show that the proposed algorithm outperforms the existing algorithms in terms of successful transmission ratio in various settings.

Network function virtualization redefines a network service as a softwarized chain of virtual network functions (VNFs), which decouples the specific network service from dedicated devices and greatly reduces the hardware cost. The VNFs are generally deployed on common off-the-shelf servers and statistically share computational resources. Due to the inherent integer constraints, the corresponding VNF mapping and scheduling issue is a highly challenging task. In this paper, we consider the mapping and scheduling of VNFs for a given network service, for which a flexible job shop scheduling problem is formulated to optimize the max-min fairness while ensuring the delay requirements of different service chains. Specifically, we propose a deep reinforcement learning method based on offline proximal policy optimization, which dynamically determines the mapping and scheduling decision based on the state of unfinished service chains. The proposed algorithm is scalable to the number of service chains and can be enhanced by Monte Carlo tree search. Numerical results show that the proposed algorithm outperforms the traditional random forest and the greedy algorithms in terms of both service fairness and acceptance ratio.

Radio access network (RAN) slicing is one of the key technologies in 5G and beyond mobile networks, where multiple logical RANs, also referred to as RAN slices, are allowed to run on top of the same physical infrastructure so as to provide slice-specific services. Due to the dynamic environments of wireless cells and the diverse requirements of RAN slices, inter-slice radio resource management (IS-RRM) is a highly challenging task. In this paper, we propose a novel online convex optimization (OCO) framework for the IS-RRM, where the instant resource allocation is learned by using historical data revealed from previous allocations. Compared with the existing methods, OCO is an online optimization process that can avoid sophisticated modeling and tuning in highly complicated and dynamic environments. Specifically, a low-complexity online ISRRM algorithm is proposed, which employs multiple expert-algorithms running parallelly to keep track of environmental changes. Simulation results show that the proposed method can provide efficient IS-RRM with a comparable performance to the optimal strategies in hindsight.

Radio access network (RAN) slicing is one of the key technologies in 5G and beyond mobile networks, where multiple logical subnets, i.e., RAN slices, are allowed to run on top of the same physical infrastructure so as to provide slice-specific services. Due to the dynamic environments of wireless networks and the diverse requirements of RAN slices, inter-slice radio resource management (IS-RRM) has become a highly challenging task in RAN slicing. In this paper, we propose a novel online convex optimization (OCO) framework for IS-RRM, which directly learns the instant resource allocation from the data revealed by previous allocations, such that sophisticated modeling and parameterization can be avoided in highly complicated and dynamic wireless environments. Specifically, an online IS-RRM scheme that employs multiple expert-algorithms running in parallel is proposed to keep track of the environmental changes and adjust the resource allocation accordingly. Both theoretical analysis and simulation results show that our proposed scheme can guarantee long-term performance comparable to the optimal strategies given in hindsight.

Low Earth orbit (LEO) satellites play an important role to realize personal global communication in future mobile communication networks, where terrestrial users can be covered by multiple satellites due to densely deployed satellites in the constellation. Since the speed of LEO satellites is much higher than that of mobile users, it yields a large amount of satellite handovers, which causes heavy signaling overhead. Also, terrestrial users need to compete for satellite channels while they can only obtain partial information of the satellite system from their individual views. Thus, a distributed satellite handover strategy is required to balance satellite load to avoid network congestion, while at the same time, maintain low signalling overhead. In this paper, we propose a novel satellite handover strategy based on multi-agent reinforcement learning that aims to minimize average satellite handovers while satisfying the load constraint of each satellite. Simulation results show that the proposed strategy outperforms the local handover strategies based on basic criteria in terms of average satellite handover and user blocking rate.

In this paper, we investigate the power allocation problem with perfect channel state information (CSI) for downlink sum rate maximization in time duplex division massive MIMO systems. We note that the downlink sum rate is generally a non-convex function of the power allocation vector and the corresponding solution space increases exponentially with the number of simultaneous users, which make the optimal power allocation computationally intractable in general. Here, we introduce an online paradigm to achieve a tradeoff between computational complexity and sum rate performance, which utilizes the state-of-art online learning techniques to iteratively update the power allocation according to continuous CSIs. The computational complexity of the proposed algorithm is highly reduced by using the first order optimization technique and the system sum rate is improved by exploiting the time correlation of wireless channels. Simulation results show that the downlink sum rate can be increased by 15%-100% by using our proposed algorithm, compared with the conventional average and pathlossbased power allocation schemes.

Recently, unmaned aerial vehicle (UAV) assisted wireless network has been recognized as a promising technology for post-disaster communications, in which multiple UAVs are dynamically deployed in the air as cellular base stations to provide public wireless connectivities when the ground infrastructure collapses. However, due to the non- uniformity of post-disaster traffic distribution and the large scale of post-disaster area, trajectory planning is considered as a major challenge for UAV- assisted post-disaster communications in many aspects including coverage, energy efficiency and computational complexity. In this paper, we consider a general trajectory planning problem for multi-UAV assisted wireless networks in a post-disaster scenario. We show that the problem can be formulated as a multi-depot vehicle routing problem. Then, we propose two heuristic algorithms that can efficiently utilize the battery of UAVs to improve the coverage performance. Simulation results show that, compared to the intuitive greedy algorithm, the coverage ratio can be improved by 8% and 28%, respectively, by using the proposed algorithms.

In this paper, we consider a massive multiple-input- multiple-output (MIMO) system, where the base station (BS) is equipped with a large number of antennas while serving a much smaller number of users simultaneously. Though massive MIMO systems can provide significant spectral and energy efficiency via simple signal processing, the required channel state information (CSI) overhead is still a huge challenge, especially for the FDD mode. The basic frame structure of the FDD massive MIMO does not fully exploit the CSI feedback waiting phase since the BS needs to wait some time for the CSI feedback sent by users and then transmit data in downlink with the estimated CSI. The proportion of the CSI feedback waiting phase in the downlink transmission would be high as the MIMO scaling up, which reduces downlink rates for the FDD massive MIMO systems to some extent. In this paper, we propose two novel downlink precoding and transmission (DPT) schemes for FDD systems by exploiting the CSI feedback waiting phase. The corresponding performance comparisons between our proposed DPT methods, the contemporary DPT scheme and the ideal DPT one are also provided based on the COST 2100 outdoor channel model. Numerical results show that one of our proposed DPT schemes can achieve higher downlink rates than the contemporary scheme in relative low-mobility scenarios. The other proposed DPT scheme performs much better and is robust to user mobility.

Content delivery networks (CDNs) have been proposed to improve the performance of large-scale content delivery in communication networks, in which content files are dynamically cached in CDN nodes that are close to end-users so as to decrease the transmission latency and traffic redundancy. We investigate a real CDN that is currently utilized by a large social network company in China. We note that there are two important issues that are not well considered in the existing literature. The first is that end-users can usually access multiple CDN nodes with high quality of experience. The second is that the data service price may vary greatly for different regions, which can highly influence the cost of CDNs for large-scale applications. We reconsider the optimal caching and routing problem in CDNs by jointly considering these two practical issues, and propose a joint caching and routing strategy by using the alternating optimization technique. Simulation results show that the proposed algorithm outperforms the current CDN strategy, in which most popular files are cached and end-users are directed to the nearest CDN nodes, by 30% and 12% in terms of latency and data service cost, respectively.

Cognitive radio is an efficient spectrum sharing mechanism to solve the contradiction between spectrum shortage and spectrum underutility, where secondary users (SUs) are allowed to access the spectrum licensed to primary users (PUs) in an opportunistic manner. In cognitive radio networks with multiple access points (APs), due to the information exchange cost and system flexibility, APs may not cooperate with each other and there usually does not exist a central controller in practice. We propose a distributed user association scheme based on multi-agent reinforcement learning to achieve load balancing for cognitive radio networks with multiple independent APs. In our proposed scheme, APs execute reinforcement learning process independently to derive optimal policies on user association. In each iteration, APs make decisions on choosing SUs for association and then SUs choose the optimal AP for association based on the offers of all APs, the behaviors of APs and SUs is modeled as a dynamic matching game. Simulation results show that the proposed multi-agent reinforcement learning approach can highly improve the system performance with excellent robustness, compared to the conventional max-SINR method.

Due to the diversity of traffic scenes and high mobility of vehicles, the propagation environment between moving vehicles and road side access points can be highly dynamic, which causes unstable uplink connectivity and time-varying uplink data rates in vehicular networks. In this paper, we study the uplink performance of heterogenous vehicular networks, which integrate dedicated short-range communications (DSRCs) and Long Term Evolution vehicle-to-everything (LTE V2X) into a single vehicular network to provide high reliability, low latency, and wide area coverage. Here, we adopt a cluster-based approach, in which DSRC and LTE are utilized to provide vehicle-to-vehicle communications between vehicles within a cluster and vehicle-to-infrastructure communications between vehicles and access points, respectively. Specifically, a self-adaptive clustering method is proposed based on the iterative self-organizing data analysis technique algorithm, in which the number of clusters can automatically adjust to the optimal value according to the mobility information. Also, a joint load-bandwidth management scheme is proposed to distribute traffic load and bandwidth resources between DSRC and LTE. Simulation results show that the proposed algorithm outperforms the traditional section-based and ${K}$ -means clustering methods, and a tradeoff between average uplink data rate and signaling overhead can be achieved.

Due to the dense deployment of small cells, the number of mobile users served by each access point is decreasing dramatically, which leads to an increasingly dynamic cell load distribution over time and space. In order to match dynamic traffic load with static infrastructure capacity, a variety of load balancing methods have been proposed. However, the existing load balancing approaches either run self-tuning algorithms to optimize local handover parameters, which may not be optimal for the network-wide performance, or formulate a static optimization problem for a ``snapshot" network, which may require a huge amount of handover signaling in dynamic situations. In this paper, we consider the load balancing problem with dynamic traffic models, in which the average throughput over a certain time period is maximized, while the average packet delay is guaranteed to be below a certain threshold. Simulation results show that our proposed user association and resource allocation algorithm can highly increase the average throughput, compared with the baseline algorithm using the maximum SINR association and equal resource allocation.

Dynamic spectrum sharing (DSS) has been envisioned as a promising approach to address the imminent shortage of spectrum resources caused by the exploding growth of wireless traffic. Contrast to the existing DSS approaches which are either based on individual authorization using a geolocation database, such as Licensed Shared Access and Spectrum Access System, or general authorization using spectrum sensing techniques, such as Collective Use of Spectrum, in this paper, we propose a threetier hierarchical model of DSS based on hybrid authorization, in which primary licensees (PLs) and secondary licensees (SLs) register with a geolocation database to guarantee predictable quality-of-service, and tertiary licensees (TLs) opportunistically access the small spectrum holes in time and space to further improve the spectrum usage. We provide a mathematical analysis of the optimal access strategy for each type of licensees in the proposed hierarchical model, such that the weighted sum throughput of SLs and TLs is maximized while the PL throughput is guaranteed. Simulation results show that the proposed hierarchical approach can highly increase the system performance, compared to the current database-driven approaches.

Low-altitude unmanned aerial vehicles (UAVs), which can act as flying base stations, are proposed as a promising paradigm to meet the ever-increasing traffic demand and enhance the coverage of terrestrial mobile communication system. However, how to determine the position and the service region of each UAV is a problem to demand urgent solutions. In this paper, we deal with this burning issue from a load balancing perspective. First, we propose an efficient subregion partition method to make each UAV serve almost equal traffic demand, where we try to minimize the maximum traffic demand of subregions with constraints of the traffic demand and the shape of subregions. Then, we propose a local search procedure to relocate UAVs using backtracking line search algorithm. The service subregions and the positions of UAVs are updated in an iterative manner until the optimum solution is produced. Numerical results show that our proposed strategy can serve more users compared with other ones. Moreover, the traffic distribution among UAVs is more balanced, as well as and the service areas of subregions, indicating the effectiveness and the efficiency of our proposal.

Recently, the in-band full-duplex (FD) capability has been demonstrated at Wi-Fi range. However, the simultaneous uplink (UL) and downlink (DL) transmission may lead to inter-user interference (IUI). In this paper, we propose a hybrid MAC protocol, in which the AP decides the probability of constructing FD transmission. The protocol also adopts a second fold of RTS/CTS mechanism to prevent the constructed transmission from being affected by the IUI. The second fold RTS/CTS mechanism and the probability for FD transmission are optimized respectively to maximize the expected spectrum efficiency. Simulation results show that the proposed MAC protocol achieves higher capacity compared to a half-duplex counterpart in terms of both UL and DL throughput.

With the rapid growth of sensor technology, smartphone sensing has become an effective approach to improve the quality of smartphone applications. However, due to time-varying wireless channels and lack of incentives for the users to participate, the quality and quantity of the data uploaded by the smartphone users are not always satisfying. In this paper, we consider a smartphone sensing system in which a platform publicizes multiple tasks, and the smartphone users choose a set of tasks to participate in. In the traditional non-cooperative approach with incentives, each smartphone user gets rewards from the platform as an independent individual and the limit of the wireless channel resources is often omitted. To tackle this problem, we introduce a novel cooperative approach with an overlapping coalition formation game (OCF-game) model, in which the smartphone users can cooperate with each other to form the overlapping coalitions for different sensing tasks. We also utilize a centralized case to describe the upper bound of the system sensing performance. Simulation results show that the cooperative approach achieves a better performance than the non-cooperative one in various situations.

In a cognitive radio (CR) network, the transmitting signal of a secondary user (SU) is traditionally considered to be harmful for the primary user (PU), since it decreases the capacity of the PU's channel. However, for PU's secrecy capacity, the SUs' interference can be beneficial if it decreases the capacity of the source-eavesdropper channel more than that of the source-destination channel. In this paper, we consider using the SUs' interference to improve the PU's secrecy capacity and providing the SUs the opportunity to access the spectrum as a reward. But, there exists a tradeoff between the SUs' channel capacity and the PU's secrecy capacity. To decide which SUs can share the spectrum with the PU, we present a coalition formation game model with nontransferable utility, and propose a merge and split algorithm. The simulation results verify the efficiency of the proposed algorithm in terms of both the SUs' channel capacity and the PU's secrecy capacity in various scenarios.

In the dynamic spectrum access (DSA) paradigm for cognitive radio networks (CRNs), one of the commonly used Medium Access Control (MAC) schemes is designed on basis of the popular carrier sensing multiple access with collision avoidance. However, this proposal suffers from two major problems that may significantly decrease the system performance: (1) collision among the secondary users (SUs) can hardly be detected, thus leading to the secondary transmission failures, and (2) SUs cannot abort transmission when collision occurs, making the long collision duration possible. In this paper, we propose a new cognitive MAC protocol for efficient DSA based on full-duplex CRNs (FD-CRNs), where SUs are able to perform simultaneous spectrum sensing and data transmission owing to full-duplex techniques. Specifically, SUs can detect the collision during transmission, so as to reduce the collision time and improve secondary network performance. Analytical results include the derivations of key design parameters such as the collision ratio with the PU, spectrum usage ratio, optimal contention window size, and the performance comparisons with the conventional DSA in half-duplex CRNs (HD-CRNs), which are further confirmed by simulation results.

Recently, the development in full-duplex communications has offered a great opportunity to perform simultaneous spectrum sensing and spectrum access in cognitive radio networks. In this paper, we consider a cognitive cellular network, in which the secondary base station (SBS) is a full-duplex device that can simultaneously sense the primary spectrum and transmit to the secondary users. We show that the power allocation of the SBS can affect both the sensing performance and the transmission capacity, and thus, we jointly consider the power allocation problem in the spectrum management process. First, we formulate the considered problem as a 3-dimensional matching problem and prove its NP-hardness. Then, we propose an approximate solution by extending a 2-dimensional matching algorithm. The simulation results show that the proposed algorithm can highly increase the secondary throughput of the SBS, compared with the greedy algorithm and the random algorithm.

Driven by both personal and commercial interests, fast popular content delivery, as one of the key services offered by vehicular ad-hoc networks (VANETs), has recently received considerable attention. Most existing work mainly focuses on the resource allocation such as transmit power or subcarrier assignment from the on-board units (OBUs) to the roadside units (RSUs). However, due to the limited backhaul capacity, great efforts still need to be taken for delivering large-size files such as videos and music to the high speed vehicles. Motivated by the recent work of pre-storing files in the cell-edge base stations, in this paper, we address the efficient content delivery problems in VANET by caching popular files in the RSUs with large storage capacity. The main objective is to minimize the average time that an OBU downloads a file. We propose three algorithms of allocating files to RSUs, in the optimal, sub-optimal, and greedy ways respectively, where the first one can achieve the best performance, and the greedy one has the lowest complexity. We also analyze the average downloading time performance in terms of the number of RSUs, storage capacity, and vehicle speed. Simulation results indicate that the proposed RSU caching methods can significantly reduce the file-downloading time, and thus, improve the content delivery efficiency.

Femtocaching is a caching system to assist the popular content downloading services in heterogenous networks, in which femto base stations (FBSs) utilize their storage capabilities to cache popular files for mobile users (MUs). When the requested files are cached, the content can be downloaded directly from the FBSs through high-rate wireless links, which avoids the backhaul bottleneck to the core network. Previous studies focus on the optimal caching strategy for a given network topology, which is referred to as static femtocaching. However, due to the mobility of MUs, the topology of a practical network rarely stays unchanged and the FBSs need periodically refreshing their caches to adapt to the current network. Limited by the weak backhaul of FBSs, the cache refreshing rate may not catch up with the changing topology, which makes dynamic femtocaching essentially different from the static scenario. In this paper, we first formulate dynamic femtocaching as an optimization problem which is proved to be NP-hard. Then, we propose two dynamic algorithms, centralized and decentralized, to give suboptimal solutions. Simulation results show that the mobility of MUs degrades the performance of dynamic femtocaching for all algorithms, while the proposed algorithms perform 18% ~ 24% better than the traditional algorithm proposed for static scenarios, and 22% ~ 25% better than a simple popular caching system.

Machine‐to‐machine (M2M) communication plays an important role in various kinds of intelligent networks. In this study, a hybrid cooperation scheme for data collection in hierarchical smart building networks (SBN) is proposed under the framework of M2M communications. The hierarchical network structure means that the data collection process is carried out via multi‐layer communications. In the first layer, smart metres organise themselves into clusters and send information to the cluster‐heads. Then all cluster‐heads forward the received information to the base station automatically in the second layer. In particular, the roles of cluster‐head can be acted by either fixed nodes or user terminals in the building, and this endow a hybrid cooperation mode to the data collection process. To construct the network structure and utilise the resources efficiently, the authors first provide some theoretical analysis on the influence of network structure and bandwidth constraints. Then a distributed scheme for joint structure formation and subband allocation is proposed based on coalitional game theory. Furthermore, for the feasibility of this scheme in practical applications, some improvements of the proposed scheme have also been made at last. The advantages of the proposed scheme are verified by simulation results.

Recently, cellular networks have become severely overloaded by social-based services, such as YouTube, Facebook, and Twitter, in which thousands of clients subscribe to a common content provider (e.g., a popular singer) and download his/her content updates all the time. Offloading such traffic through complementary networks, such as a delay tolerant network formed by device-to-device (D2D) communications between mobile subscribers, is a promising solution to reduce the cellular burdens. In the existing solutions, mobile users are assumed to be volunteers who selflessly deliver the content to every other user in proximity while moving. However, practical users are selfish and they will evaluate their individual payoffs in the D2D sharing process, which may highly influence the network performance compared to the case of selfless users. In this paper, we take user selfishness into consideration and propose a network formation game to capture the dynamic characteristics of selfish behaviors. In the proposed game, we provide the utility function of each user and specify the conditions under which the subscribers are guaranteed to converge to a stable network. Then, we propose a practical network formation algorithm in which the users can decide their D2D sharing strategies based on their historical records. Simulation results show that user selfishness can highly degrade the efficiency of data offloading, compared with ideal volunteer users. Also, the decrease caused by user selfishness can be highly affected by the cost ratio between the cellular transmission and D2D transmission, the access delays, and mobility patterns.

In traditional cognitive radio networks, secondary users (SUs) typically access the spectrum of primary users (PUs) by a two-stage "listen-before-talk" (LBT) protocol, i.e., SUs sense the spectrum holes in the first stage before transmit in the second stage. In this paper, we propose a novel "listen-and-talk" (LAT) protocol with the help of the full-duplex (FD) technique that allows SUs to simultaneously sense and access the vacant spectrum. Analysis of sensing performance and SU's throughput are given for the proposed LAT protocol. And we find that due to self-interference caused by FD, increasing transmitting power of SUs does not always benefit to SU's throughput, which implies the existence of a power-throughput tradeoff. Besides, though the LAT protocol suffers from self-interference, it allows longer transmission time, while the performance of the traditional LBT protocol is limited by channel spatial correction and relatively shorter transmission period. To this end, we also present an adaptive scheme to improve SUs' throughput by switching between the LAT and LBT protocols. Numerical results are provided to verify the proposed protocol and the theoretical results.

With the fast development of mobile terminals and wireless communication networks, mobile social networks (MSNs) play an important role in everyday lives to access social activities. However, most research on MSNs typically focuses on the relations of the users' physical location, but not make sufficient use of social ties. Consequently, in this paper, we consider a scenario of MSNs with online social networks and offline Device-to-Device (D2D) communication underlaying cellular networks, and study the problem of data dissemination to the mobile users under the constraint of limited spectrum resources. We first present a novel approach to formulate the social relationships for the offline mobiles by comparing the similarity of mobile users' social activities with the Bayesian model. And then we realize efficient data propagation using coalitional graph game. Finally, we provide simulation results to verify effectiveness of our studies.

Device-to-Device (D2D) communications have been proposed recently to improve the spectral efficiency. In this paper, we consider physical-layer security in D2D communication as an underlay to cellular networks with an eavesdropper. Benefiting from the underlaid spectrum reuse, D2D users can contribute to the system secrecy capacity, while D2D users may interfere the cellular users and decrease their secrecy capacity. We formulate this problem as a matching problem in the weighted bipartite graph and introduce the Kuhn-Munkres (KM) algorithm to provide the optimal solution. Simulation results show that the system secrecy capacity can be greatly improved by introducing D2D communications underlaying cellular networks.

Smart meters aided by wireless communications have been widely used to collect the information of the electrical appliances. In this paper, we consider a two-layer heterogeneous smart building network, consisting of a number of cluster-organized smart meters, and a base station (BS). The communication takes two phase: 1) periodical data collection via cluster heads in the first layer, and 2) data transmission from the cluster heads to the BS in the second layer. But, due to the irregular topology of smart meter networks and various types of data traffic of electrical appliances, the data aggregation to the heads and associated spectrum allocation become quite challenging. To solve these problems, we first analyze the relationship between the system performance and the cost of network construction. By using coalition formation game theory, we propose a practical strategy in optimizing channel allocation and cluster-heads deployment. The proposed algorithms are verified through computer simulations.

Cooperative spectrum sensing has been shown to yield a significant performance improvement in cognitive radio networks. In this paper, we consider distributed cooperative sensing (DCS) in which secondary users (SUs) exchange data with one another instead of reporting to a common fusion center. In most existing DCS algorithms, the SUs are grouped into disjoint cooperative groups or coalitions, and within each coalition the local sensing data is exchanged. However, these schemes do not account for the possibility that an SU can be involved in multiple cooperative coalitions thus forming overlapping coalitions. Here, we address this problem using novel techniques from a class of cooperative games, known as overlapping coalition formation games, and based on the game model, we propose a distributed DCS algorithm in which the SUs self-organize into a desirable network structure with overlapping coalitions. Simulation results show that the proposed overlapping algorithm yields significant performance improvements, decreasing the total error probability up to 25% in the Qm+ Qfcriterion, the missed detection probability up to 20% in the Qm/Qfcriterion, the overhead up to 80%, and the total report number up to 10%, compared with the state-of-the-art non-overlapping algorithm.

With the rapid growth of sensor technology, smartphone sensing has become an effective approach to help improve the quality of applications in smartphones. However, the quality and quantity of the sensed data uploaded by the users are not always satisfying due to the lack of incentives for users to participate. In this paper, we design an incentive mechanism in which the users can get satisfying rewards from the platform by efficiently allocating their resources, and meanwhile, the platform can achieve a relatively high social welfare. Specifically, to solve the resource allocation problem in the incentive mechanism, we consider a cooperative game model with overlapping coalitions, in which the smartphone users can self-organize into the overlapping coalitions for different sensing tasks. Then, we propose a distributed algorithm that converges to a stable outcome in which no user has the motivation to change its current resource allocation so as to increase its individual payoff unilaterally. Simulation results show that the proposed scheme achieves a better performance than the average distribution case and the single-task distribution case in various conditions.

In this paper, we address the popular content distribution (PCD) problem in a highway scenario, in which popular files are distributed to a group of on-board units (OBUs) driving through a single roadside unit (RSU). Due to the high speeds, the OBUs may not finish downloading a large file within the limited time for vehicle-to-roadside (V2R) communication and a peer-to-peer (P2P) network consisting of OBUs out of the RSU coverage can be constructed for completing the file delivery process. However, due to fast and unpredictable topological changes of the vehicular ad hoc network (VANET), the static methods in traditional P2P networks can be inefficient. We model this problem as a coalition formation game with transferable utilities, and propose a coalition formation algorithm that converges into a Nash-stable partition adapting to environmental changes. Based on this algorithm, we further propose a distributed scheme for the overall PCD problem. Simulation results show that our scheme presents a considerable performance improvement relative to the non-cooperative case using the carrier sense multiple access with collision avoidance (CSMA/CA).

Collaborative spectrum sensing (CSS) has been shown to be able to highly improve the performance of spectrum sensing in cognitive radio networks. However, most existing works focused on either centralized approaches that rely on a global fusion center, thus requiring significant overhead, or on distributed approaches that rely on disjoint coalitions of secondary users (SUs) in which an SU can only cooperate with a single, selected coalition, hence limiting the performance gains of CSS. In this paper, a novel, coalition-based approach to CSS is proposed in which an SU can share its sensing results with more than one coalition. The problem is formulated using a novel class of cooperative games, known as overlapping coalitional games, which enables the SUs to decide, in a distributed manner, on the number of coalitions in which they wish to cooperate, depending on the associated benefit and cost tradeoffs. To solve this game, a novel, distributed algorithm is proposed using which the SUs can self-organize into a stable overlapping coalitional structure. Simulation results show that our proposed algorithm significantly improves the performance in terms of both the average probability of misdetection and the convergence time, relative to the noncooperative case and the state-of-art cooperative CSS with non-overlapping coalitions.

Driven by both safety concerns and commercial interests, vehicular ad hoc networks (VANETs) have recently received considerable attentions. In this paper, we address popular content distribution (PCD) in VANETs, in which one large popular file is downloaded from a stationary roadside unit (RSU), by a group of on-board units (OBUs) driving through an area of interest (AoI) along a highway. Due to high speeds of vehicles and deep fadings of vehicle-to-roadside (V2R) channels, some of the vehicles may not finish downloading the entire file but only possess several pieces of it. To successfully send a full copy to each OBU, we propose a cooperative approach based on coalition formation games, in which OBUs exchange their possessed pieces by broadcasting to and receiving from their neighbors. Simulation results show that our proposed approach presents a considerable performance improvement relative to the non-cooperative approach, in which the OBUs broadcast randomly selected pieces to their neighbors as along as the spectrum is detected to be unoccupied.

We consider a cognitive radio network in which multiple pairs of secondary users (SUs) communicate by a one-way relay node over orthogonal channels with the existence of an eavesdropper close to the destination. The transmit power of the relay needs efficient distribution for maximizing the sum secrecy rate of the SU pairs, meanwhile satisfying the interference constraint at the single primary user (PU). Specifically, we introduce two multi-object auctions, i.e. the Vickrey auction and the sequential first-price auction, to perform this power allocation problem. We prove the existence and give the general form of the only equilibrium for each auction. We also propose two algorithms based on the equilibriums, respectively. From the simulation results, we see that the system secrecy rate curve of the Vickrey auction gradually coincides with that of the optimal allocation with increasing power units, while the sequential first-price auction reflects more fairness.