Direct current (DC) is added into intensity modulation and direct detection (IM/DD) signals in time domain to make most negative signals be positive in DC biased-optical orthogonal frequency division multiplexing (DCO-OFDM) systems for optical wireless communication (OWC). To the best of the authors' knowledge, this is the first work to utilize DC inherent in DCO-OFDM signals to allow semi-blind joint synchronization and channel estimation for DCO-OFDM OWC systems. We propose a novel semi-blind DC-based joint estimation (SDJE) approach, where sampling time offset (STO) as one of timing synchronizations and channels can be jointly estimated based on DC inherent in DCO-OFDM signals. The proposed SDJE approach is spectral-efficient, requiring no pilot. This is different from state-of-the-art works that employ a number of pilots to perform synchronization and channel estimation. We formulate a cost function by exploring the difference between the received signals and DC signals. The joint STO and channel estimations can be conducted by minimizing the formulated cost function. Furthermore, we propose a dimensionality reduction approach, and design an offline database to reduce complexity. Simulation results show that the proposed SDJE approach provides bit error rate (BER) performance close to the ideal case with no STO and perfect channel state information (CSI).

Blockchain is a decentralized and secure digital ledger system that ensures data integrity through immutable records and cryptographic consensus mechanisms. However, in mobile blockchain networks, the computation-intensive proof-of-work (PoW) mining process often imposes a significant burden on mobile users (MUs) who serve as miners, particularly given their limited computing resources. Mobile edge computing (MEC) offers a promising solution to alleviate the burden on MUs, by enabling them to offload their mining tasks to nearby edge servers. While existing studies have explored MEC-assisted blockchain networks in both single-server and multi-server scenarios, they often overlook crucial aspects of blockchain networks, such as the transmission and computation delays inherent in the mining process. In this work, we investigate a more realistic MEC-assisted mobile blockchain network, where mining tasks are explicitly modeled with delay constraints to better capture real-world performance challenges. To analyze the strategic interactions between MUs and edge computing service providers (ECPs), we formulate a two-stage multi-leader and multi-follower Stackelberg game, which consists of an ECP Resource Pricing (ERP) game at Stage I, and an MU Resource Competition (MRC) game at Stage II. Specifically, in the ERP game at Stage I, ECPs, acting as leaders, set the resource prices for MUs; and in the MRC game at Stage II, MUs, acting as followers, determine their computing resource demands based on the prices of ECPs. We first prove the existence of Nash equilibrium (NE) for both games, and then derive the closed-form conditions for the NE of the MRC game at Stage II. Based on the above, we further propose a sub-gradient-based resource pricing algorithm that can converge to the NE of the ERP game at Stage I. Simulation results show that, when compared to the centralized cooperative solution, our proposed non-cooperative game approach can significantly reduce the computational complexity, while incurring only a modest performance degradation, e.g., the social welfare loss ranges from 6.64% to 9.96%.

This study examines a network-assisted full-duplex (NAFD) cell-free massive MIMO (CF-mMIMO) system, in which half-duplex access points (APs) simultaneously serve multiple uplink and downlink user equipment (UEs) using the same frequency resources. NAFD technology facilitates full-duplex transmission over existing half-duplex hardware through dynamic scheduling of AP operating modes, resulting in significant improvements in system spectral efficiency (SE). To ensure fairness among all UEs, we aim to maximize the minimum SE across UEs by jointly optimizing the AP operation modes and the uplink and downlink power control of the UEs, which helps mitigate severe cross-link interference for UEs with the lowest SE. We propose a novel joint optimization scheme based on integer linearization techniques to address this strongly coupled mixed-integer non-convex problem and achieve a near-optimal solution. Simulation results demonstrate that our proposed scheme outperforms the benchmark approach, providing a more equitable quality of service throughout the coverage area.

In this study, we consider a more general scenario involving multiple simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Our objective is to maximize the weighted sum rate for users by decoupling the original problem into two components: the active beamforming design at the access points (APs) and the passive beamforming design at the STAR-RIS. We employ fractional programming to optimize these components alternately. The rank-one constraint in the passive beamforming design, which is proven to be an NP-hard problem, represents the primary challenge. To address this issue, we introduce a novel low-complexity algorithm based on sequential rank-one constraint relaxation (SROCR). Instead of entirely eliminating the rank-one constraint, our SROCR algorithm utilizes a progressive relaxation approach to gradually ease the constraint and identify a generic rank-one suboptimal feasible solution, ultimately converging to a solution that satisfies the rank-one condition. Numerical results demonstrate that our algorithm achieves comparable performance to existing algorithms while significantly reducing complexity, thus outperforming other baseline algorithms.

In this study, we investigate symbiotic radio-assisted cell-free massive multiple-input multiple-output (SCF-mMIMO) systems in which multiple access points serve primary users and backscatter devices. We propose a semi-blind channel estimation scheme based on the expectation maximization (EM) algorithm to reduce pilot overhead and iteratively achieve performance close to that of maximum likelihood (ML) estimation. Unlike the traditional EM algorithm, we derive a modified EM algorithm by incorporating suitable priors for the channel coefficients to estimate the aggregate channel. Simulation results show that the proposed EM algorithm for the SCF-mMIMO system achieves good performance with fewer pilots, approaching that of the ML estimation. In addition, the modified EM algorithm using channel priors outperforms traditional EM algorithms. These results suggest that semi-blind channel estimation holds considerable promise for SCF-mMIMO systems.

In this paper, a 2-layer rate-splitting multiple access (RSMA) framework with a balanced clustering design is proposed for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Aiming at enhancing spectral efficiency (SE) and meanwhile ensuring low-complexity and scalability in large-scale systems, this work addresses different types of multi-user interferences by utilizing a two-stage optimization scheme with a designed spatial reduction matrix that encompasses the clustering design and joint RSMA. First, a balanced clustering design is developed for 2-layer RSMA to manage intra-and inter-cluster interference more efficiently than conventional user-centric clustering, simultaneously considering AP-user connectivity in CF-mMIMO systems and the impact of cluster similarity on RSMA. By employing the spectral clustering method to solve the bipartite graph partitioning problem with the min-max cut objective, the number of clusters is determined adaptively. Based on the above clustering design, a joint optimization of inner and outer RSMA is proposed to mitigate intra-and inter-cluster interferences simultaneously. Simulation results verify the SE enhancement, low-complexity, and scalability of the proposed 2-layer RSMA framework, compared with benchmark frameworks.

We design an absorbable intelligent reflecting surfaces (IRS)-based wireless communication system with two modes, where a wave-absorption structure is embedded into a wave-reflection structure. This is the vital work to investigate the dual function of IRS to switch between the wave-absorption mode and the wave-reflection mode, which improves the degree of freedom in terms of optimization. We formulate the spectral efficiency problem with respect to discrete phase shifts and wave-absorption function for the designed absorbable IRS-based wire-less communication system with strong interferences. In order to maximize spectral efficiency, we propose an iterative grouping optimization (IGO) algorithm, to enhance the desired signal power and reduce interference, based on the wave-absorption mode and the wave-reflection mode. The proposed algorithm achieve low complexity, requiring no exhaustive search, while providing spectral efficiency higher than the existing method with no wave-absorption.

Unmanned Aerial Vehicles (UAVs), with their high levels of flexibility and maneuverability, can greatly enhance the capabilities of Mobile Edge Computing (MEC) by acting as edge computing servers. In practice, however, it is often challenging to jointly optimize the flying trajectories of UAVs and the offloading decisions of tasks, due to the fast and randomly changing of physical environments. In this work, we investigate an UAV-enable MEC network with the assistance of Digital Twin (DT), where a DT layer is introduced to simulate the Physical Entity (PE) layer, generate different strategies, and evaluate their performances. Specifically, we formulate a joint flying trajectories, task offloading, and resource allocation problem on the DT layer, aiming at minimizing both task delay and energy consumption, under the maximum tolerated delay and resource constraints. To solve the problem in an online distributed manner and implement the derived strategies on the real PE layer, we propose a hierarchical learning approach, which consists of a Deep Reinforcement Learning (DRL) module and a Constrained Optimization (CO) module. First, the DRL module determines the UAVs' flying trajectories. Then, the CO module determines the MDs' task offloading decisions and the associated resource allocations, given the UAV s' flying decisions. Finally, the outputs of both modules are combined together to train the DRL module by using the Deep Deterministic Policy Gradient (DDPG) method. Experiment results show that our proposed DT-assisted scheme outperforms existing benchmark schemes in terms of both task delay and energy cost.

In this paper, symbiotic radio under simultaneous wireless information and power transfer (SWIPT)-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) is investigated, where multiple access points serve all primary users and backscatter devices (BDs). We derive closed-form expressions for the achievable rates of the primary users and BDs, downlink signal-to-interference-plus-noise-ratio (SINR) and the harvested energy of the primary users. We aim to maximize fairness among BDs through joint optimization of the power splitting factor of SWIPT, uplink and downlink power control and backscatter coefficients of BDs. Being different from traditional low-complexity modified bisection search (MBS) schemes applied in other systems where the non-convex issues of the downlink SINR constraints are not considered, a novel MBS-based resource allocation scheme is proposed for symbiotic radio under SWIPT-enabled CF-mMIMO employing successive convex approximation method to address the non-convex issue and thus enhance the applicability of the MBS scheme. Simulation results show that our proposed scheme can obtain a near-optimal solution with low complexity, which could reduce the processing delay of the central processing unit in CF-mMIMO networks.

In this paper, we focus on an ultra-reliable low latency communication (URLLC) scenario and investigate the multi-access services with individual latency constraints. More specifically, the wireless communications between the access point and multiple users are requested to be accomplished while satisfying different maximum allowed delays. Taking the finite blocklength (FBL) impacts into account, we model the individual latency constraints as diverse blocklength consumption limits for users and concentrate on a blocklength allocation problem minimizing the overall decoding error probability. Aiming at achieving the optimal blocklength design in an extremely efficient manner, we start with characterizing the optimal solution features and find out that the error probability derivatives in the optimal solution follow a stepwisely increasing manner. As a result, we are enabled to alternatively determine the derivative step levels for optimally solving the problem. Subsequently, an efficient algorithm is proposed for the optimal step level design and for recovering the optimal blocklength solution. The solution optimality is then verified via both theoretical discussions and numerical evaluations. In addition, our proposed analytical solution based on step level design has also been numerically confirmed with an extremely lower complexity, in comparison with the conventional convex optimization approach.

We propose spatial superimposition (SS) structures to reduce peak-to-average power ratio (PAPR) for unipolar asymmetrically clipped optical-orthogonal frequency division multiplexing (UACO-OFDM) light fidelity (LiFi) systems with multiple light emitting diodes (LEDs) at the transmitter. The traditional μ-law companding method only increases the small amplitudes of signals, while maintaining the maximum value of the signal, which provides the limited PAPR reduction. Hence, we propose an improved nonlinear μ-law companding approach for PAPR reduction by enhancing small-amplitude signals and compressing large-amplitude signals. Linear compression is further used in the transmitted signals to reduce the impact of LED nonlinearity. Multiple LEDs are utilized to compensate for signal distortion caused by joint linear and nonlinear compressions, requiring no decompanding as in the traditional method. However, there are a few negative compensation signals that can be made to be positive by adding a small value of direct current (DC) bias theoretically derived in a closed form. Also, we propose an enhanced SS (eSS) structure, where the turn-on and maximum voltages of LED are jointly considered in the PAPR reduction, requiring no additional DC bias. This is the first work to investigate channel diversity in the proposed structures, while the multiple channels are assumed to be highly correlated with each other due to small LED separation in the previous works. We propose a frequency-domain channel filling (FCF) approach and a time-domain CF (TCF) approach, to mitigate the channel differences, which enables effective equalization of received signals. This is also the first work to investigate the analytical bit error rate (BER) of UACO-OFDM systems with clipping noise. We derive the analytical BERs of the proposed SS and eSS structures, respectively. Simulation results verify the proposed approaches.

In this paper, we study a multi-access mobile edge computing (MEC) network in the industrial Internet-of-Things (IoT) scenario, which aims at providing a joint computation service for a group of sub-tasks offloaded from multiple user equipments (UEs). The whole MEC service, including a communication phase and a computation phase, is required to satisfy both a low latency and a high reliability requirement. We derive the end-to-end reliability (error probability) of the whole MEC service and provide corresponding reliability-optimal design frameworks, where both the perfect channel state information (CSI) and outdated CSI scenarios are considered. In particular, we characterize the low-latency communication behavior with the consideration of the finite blocklength (FBL) impact, and exploit the extreme value theory to study the delay violation probability in the computation phase. Following the characterizations, in the perfect CSI scenario, a design framework minimizing the instantaneous end-to-end error probability is provided, i.e., via optimally choosing the time length for each user’s offloading and the time length for the computation phase. We rigorously prove the convexity of the problem, investigate the relationships among the variables in the optimal solution, based on which a low-complexity method is proposed achieving the optimal solution. In addition, for the scenario with only the outdated CSI, after deriving the expected end-to-end error probability conditioned on the outdated CSI value, a corresponding optimal time allocation design is provided as well, where the convexity of the formulated problem is characterized and the optimal solution is obtained. Via simulations, we validate our analytical model and evaluate the network performance under the design.

Federated Learning (FL) is a distributed machine learning approach that enables multiple individual devices (clients) to collaboratively train a global machine learning model without directly sharing their raw data with each other. By keeping the raw data on local devices, FL can effectively preserve data privacy and security. However, the performance of FL system is highly dependent on the amount and quality of client data, as well as the relevance of data from different clients. In this article, we investigate the client and data selection problem in FL system from both system and individual perspectives, while considering the impacts of data quality and data relevance. Specifically, from the system perspective, we establish a centralized optimization problem that aims to optimize social welfare in a centralized manner. That is, a central controller decides on the amount of each client's data to be utilized for the FL system, aiming at maximizing the overall social welfare. From the individual perspective, we formulate a two-stage Stackelberg game for incentivizing clients to contribute their data and resources to the FL system in a decentralized manner. In the first stage, the FL server acts as the game leader and specifies a reward mechanism. In the second stage, each client acts as a game follower and competes for the reward by deciding on the amount of data to contribute to the FL system, aiming at maximizing its individual payoff. We analyze the centralized optimization problem and the Stackelberg game systematically for both low and high data relevance scenarios. In particular, we derive the closed-form optimal solution and game equilibrium for the low data relevance scenario, and propose iterative algorithms that effectively converge to a suboptimal solution and subgame equilibrium for the high data relevance scenario. Simulation results verify that data relevance has a significant negative impact on the system performance. That is, the social welfare achieved through centralized optimization and distributed Stackelberg game approaches in the high data relevance scenario is only 19.7% and 24.0%, respectively, of those achieved in the low data relevance scenario.

Mobile Edge Computing (MEC) is emerging as a promising computing scheme to support AI-enabled applications in vehicular networks, via offloading some tasks to edge servers deployed on Road Side Units (RSUs) that approximates to vehicles. In this work, we consider a general vehicular edge network (VEN), where each vehicle can offload tasks to edge servers or cloud server via a vehicle-to-infrastructure (V2I) transmission link, or to other vehicles via a vehicle-to-vehicle (V2V) transmission link. To characterize different task flows in different transmission links or computing servers, we introduce a V2V transmission queue, a V2V transmission queue, and a local computation queue for each vehicle, and an edge computation queue for each edge server. In such a queue-based VEN, we focus on the joint task offloading and scheduling problem for vehicles, which consists of (i) offloading problem, i.e., whether to offload tasks, and (ii) scheduling problem, i.e., where and how to offload tasks. The problem is challenging due to the online and asynchronous offloading and scheduling decisions for each task. We propose a Multi-layer Deep Reinforcement Learning (DRL) approach, where each vehicle trains three neural networks (called agents) to make different layers' decisions: (i) offloading agent, determining whether to offload each task when tasks arrive, and (ii) V2I and V2V scheduling agents, determining where and how to offloading each task in V2I and V2V transmission queues, respectively. We provide the detailed algorithm design of each agent by using the Double Deep Q-Network (DDQN) approach. Simulation results show that our proposed multi-layer DRL approach outperforms the existing baseline approaches in terms of both the cost performance and the convergence speed.

Enabling ultra-reliable and low-latency communication services while providing massive connectivity is one of the major goals to be accomplished in future wireless communication networks. In this paper, we investigate the performance of a hybrid multi-access scheme in the finite blocklength (FBL) regime that combines the advantages of both non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes. Two latency-sensitive application scenarios are studied, distinguished by whether the queuing behaviour has an influence on the transmission performance or not. In particular, for the latency-critical case with one-shot transmission, we aim at a certain physical-layer quality-of-service (QoS) performance, namely the optimization of the reliability. And for the case in which queuing behaviour plays a role, we focus on the link-layer QoS performance and provide a design that maximizes the effective capacity. For both designs, we leverage the characterizations in the FBL regime to provide the optimal framework by jointly allocating the blocklength and transmit power of each user. In particular, for the reliability-oriented design, the original problem is decomposed and the joint convexity of sub-problems is shown via a variable substitution method. For the effective-capacity-oriented design, we exploit the method of Lagrange multipliers to formulate a solvable dual problem with strong duality to the original problem. Via simulations, we validate our analytical results of convexity/concavity and show the advantage of our proposed approaches compared to other existing schemes.

Light emitting diode (LED) is employed to transmit signals for visible light communication (VLC) systems. Asymmetrically clipped optical-orthogonal frequency division multiplexing (ACO-OFDM) is one of multi-carrier modulations to improve data rates. However, ACO-OFDM signals provide high peak-to-average power ratio (PAPR), and are clipped off to work in a nonlinear LED with a limited range of linearity. In this paper, a non-redundant multi-layer superimposed PAPR reduction approach is proposed for ACO-OFDM VLC systems, where two non-redundant signal streams are superimposed with the ACO-OFDM signal stream for PAPR reduction, requiring no pilot and no side information. A number of multi-layer signals are designed to reduce large-amplitude source signals and enhance small-amplitude source signals. The source signals are not interfered by the designed multi-layer signal streams in frequency domain, as odd subcarriers are occupied by source signals, while even subcarriers are occupied by the designed signals. Simulation results show that the proposed approach outperforms a number of existing methods in the literature, and provides performance better than ACO-OFDM with no PAPR reduction, in terms of bit error rate (BER) and complementary cumulative distribution function (CCDF) of PAPR reduction.

In this article, we investigate the long-term energy consumption and transmission delay (EC-TD) tradeoff in a wireless-powered body area network that consists of a multiantenna hybrid access point and a number of single-antenna sensor nodes (SNs). The beamforming technique and the simultaneous wireless information and power transfer (SWIPT) technique are adopted. Each SN is equipped with a battery and data buffer for storing harvested energy and sensory data. The long-term energy consumption minimization problem is addressed subject to the constraint of transmission delay. Meanwhile, the residual energy constraints of SNs are considered, which enable the setting up of the available energy of the SNs according to requirements. By employing the Lyapunov optimization theory, the original stochastic optimization problem is transformed into an equivalent instantaneous nonconvex problem in which the long-term EC-TD tradeoff can be adjusted using a system control parameter$V$. A joint power and time allocation scheme is then proposed to solve this instantaneous problem. Moreover, based on the derived upper bounds of the long-term energy consumption and data buffer length, we reveal that the proposed resource allocation scheme achieves an EC-TD tradeoff as$[\mathcal {O}(1/V),\mathcal {O}(V)]$. Since the value of$V$can be adjusted to achieve different energy consumption and transmission delay, the flexibility and applicability of the proposed scheme are enhanced. The simulation results validate the theoretical analysis and verify the effectiveness of the proposed scheme.

In mobile Internet ecosystem, mobile users (MUs) purchase wireless data services from Internet service provider (ISP) to access to Internet and acquire the interested content services (e.g., online game) from Content Provider (CP). The popularity of intelligent functions (e.g., AI and 3D modeling) increases the computation-intensity of the content services, leading to a growing computation pressure for the MUs’ resource-limited devices. To this end,edge computing serviceis emerging as a promising approach to alleviate the MUs’ computation pressure while keeping their quality-of-service, via offloading some computation tasks of MUs to edge (computing) servers deployed at the local network edge. Thus, edge service provider (ESP), who deploys the edge servers and offers the edge computing service, becomes an upcoming new stakeholder in the ecosystem. In this work, we study the economic interactions of MUs, ISP, CP, and ESP in the new ecosystem with edge computing service, where MUs can acquire the computation-intensive content services (offered by CP) and offload some computation tasks, together with the necessary raw input data, to edge servers (deployed by ESP) through ISP. We first study the MU's Joint Content Acquisition and Task Offloading (J-CATO) problem, which aims to maximize his long-term payoff. We derive theoff-linesolution with crucial insights, based on which we design anonlinestrategy with provable performance. Then, we study the ESP's edge service monetization problem. We propose a pricing policy that can achieve aconstant fractionof the ex post optimal revenue with an extraconstant lossfor the ESP. Numerical results show that the edge computing service can stimulate the MUs’ content acquisition and improve the payoffs of MUs, ISP, and CP.

Mobile Edge Caching is a promising technique to enhance the content delivery quality and reduce the backhaul link congestion, by storing popular contents at the network edge or mobile devices (e.g. base stations and smartphones) that are proximate to content requesters. In this work, we study a novel mobile edge caching framework, which enables mobile devices to cache and share popular contents with each other via device-to-device (D2D) links. We are interested in the incentive-related problem of mobile device users: whether and which users are willing to cache and share what contents, taking the user mobility and cost/reward into consideration. The problem is challenging in a large-scale network. We introduce the evolutionary game theory, an effective tool for analyzing large-scale dynamic systems, to analyze the mobile users' content caching and sharing strategies. Specifically, we first derive the users' best caching and sharing strategies, and then analyze how these best strategies change dynamically over time. Based on the above, we further characterize the system equilibrium systematically. Simulation results show that the proposed scheme outperforms the existing schemes in terms of the total transmission cost and the cellular load. In particular, in our simulations, the total transmission cost can be reduced by 42.5%~55.2% and the cellular load can be reduced by 21.5%~56.4%.

Indoor lighting is achieved by multiple light emitting diodes (LEDs) working together, which forms a multi-input single-output (MISO) Light Fidelity (LiFi) system. Due to small separation, the channels between LEDs are highly correlated, causing performance degradation. This is the first work to apply direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) to reduce the adverse effects of high channel correlations in terms of phase for MISO LiFi systems. We propose an optimal phase rotation (PR) based precoding to achieve the reduction of high channel correlation effects for spatial multiplexing (SMP) and spatial modulation (SM) transmissions, respectively. The optimally rotated phase angles are obtained by maximizing minimum Euclidean distances between all candidate signals. The transmission power is not affected by the proposed PR based precoding, while in the previous works, transmitted signals of some LEDs are allocated less power. The proposed PR based precoding is designed offline, applicable to arbitrary multiple transmitted signals with any M-ary quadrature amplitude modulation (M-QAM). Simulation results show that the proposed approach provides bit error rate (BER) performance better than state-of-the-art methods. Analytical results are also derived to provide BER performance close to numerical results.

In this article, we focus on a wireless-powered body area network in which the simultaneous wireless information and power transfer (SWIPT) technique is adopted. We consider two scenarios based on whether sensor nodes (SNs) are equipped with battery. For the first time, energy consumption minimization with throughput heterogeneity (ECM-TH) problem is addressed for both scenarios. For the battery-free scenario, a low-complexity time allocation scheme is proposed. This scheme solves the ECM-TH problem based on a hybrid method of gradient descent and bisection search algorithms. Consequently, compared with the interior-point method, our scheme has a lower computational complexity for the same energy consumption performance of the network. For the battery-assisted scenario, the nonconvex ECM-TH problem is first transformed into a convex optimization problem by introducing auxiliary variables. Then, a joint time and power allocation scheme based on the Lagrange dual subgradient method is proposed to solve it. Compared with the battery-free scenario, energy consumption and outage probability are both decreased in the battery-assisted scenario. Moreover, we address a special case wherein the feasible set of the above-mentioned ECM-TH problems may be empty owing to poor channel conditions or high throughput requirements of SNs.

Phase synchronization phenomena are directly connected with the underlying neural mechanisms of certain cognitive processes. However, only the amplitude information is utilized in most electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Few of the existing methods can simultaneously measure the amplitude and phase information required for classification. In this study, a novel common amplitude-phase measurement (CAPM) method is proposed. This method is capable of jointly measuring the phase and amplitude information of EEG signals on the Riemannian manifold. The proposed CAPM method comprises a two-step approach. First, a novel Riemannian graph embedding is proposed for dimensionality reduction while performing spatial-spectral filtering. The graph embedding is excellent in capturing the intrinsic features contained by the physiological signal. Second, to enhance robustness, a novel classifier is designed to incorporate the regularized linear regression in the computation of Riemannian distance. Experimental results on two BCI competition datasets demonstrate CAPM can yield high classification performance. The proposed CAPM method is a promising tool in analyzing EEG amplitude-phase characteristics and exhibits great potential in BCI applications.

In this paper, we propose a blind direct current bias (DCB) based timing synchronization and a blind null subcarrier (NS) based timing synchronization methods for direct current biased optical-orthogonal frequency division multiplexing (DCO-OFDM) visible light communications (VLC) systems. This is the first work to investigate blind timing synchronization for DCO-OFDM VLC systems, achieving high bandwidth efficiency, unlike the previous works which require a number of pilots. The two blind approaches are robust against the limited bandwidth of light emitting diode (LED), as the timing synchronization is conducted in frequency domain to mitigate the effect of inter-symbol-interference (ISI) caused by LED limited bandwidth, rather than being performed in time domain as the previous works that are vulnerable to the ISI. The DC bias is utilized by the proposed DCB based approach to perform blind timing synchronization, and the null subcarrier is used by the proposed NS based approach. Simulation results show that the proposed blind DCB and NS timing synchronization approaches significantly outperform the state-of-the-art methods in terms of the probability of false detection and bit error rate (BER), and yield BER performance close to ideal case with perfect synchronization, zero forcing (ZF) equalization and perfect channel state information (CSI).

Mobile edge caching is a promising approach for enhancing content delivery efficiency and alleviating backbone network burden, via caching popular contents at network edge devices (e.g., base stations or WiFi access points). The successful commercial deployment relies on a comprehensive understanding of the economic interactions among different stakeholders involved. In this paper, we study an edge caching system consisting of a Content Provider (CP), an Internet Service Provider (ISP) who provides the backbone network service, a wireless Access Provider (AP) who provides the wireless access service, and a set of mobile End-Users (EUs), where the CP provides contents for EUs either via the remote server (on the Internet) or via the edge cache (purchased from the AP). We formulate their interactions as a three-stage Stackelberg game. In Stage I, the CP decides the edge cache space to purchase from the AP and cache access fee to charge EUs. In Stage II, the ISP and AP determine the backbone and wireless access service prices, respectively. In Stage III, EUs decide whether to subscribe to the CP' s edge cache service, taking the cache hit probability, cache access fee, backbone and wireless access prices into consideration. We analyze the subgame perfect equilibrium of the dynamic game systematically under two different network pricing scenarios: cooperative pricing and competitive pricing, depending on whether ISP and AP cooperate or compete with each other to make their pricing decisions. Our analysis and simulation results show that all profits of the CP, ISP, AP, and utilities of EUs can be increased by adopting edge cache, compared with the case without edge cache.

Mobile Edge Computing (MEC) is a promising solution to tackle the upcoming computing tsunami in 5G era, by effectively utilizing the idle resource at the mobile edge. In this work, we study such an MEC scenario, where mobile devices at edge share their heterogeneous resources with each other, hence forming a multi-dimensional resource crowdsourcing (sharing) framework. We are interested in the problem of how to optimally offload tasks to mobile devices under this framework, aiming at minimizing the total energy cost and maximizing the overall task completion. To study the problem, we first propose a general task model, where each task is divided into multiple sequential subtasks according to their functionalities as well as resource requirements. Then, based on the task model, we propose a Joint Energy Consumption and Task Failure Probability Minimization Problem, which decides when and where each subtask will be offloaded to. The problem is challenging to solve, mainly due to the inherent constraints between the scheduling of different subtasks. Therefore, we propose several linearization methods to relax the constraints, and convert the original problem into an integer linear programming (ILP), which can be solved by many classic methods effectively. We further perform simulations, which show that our proposed solution outperforms the existing solutions (with indivisible tasks or without resource sharing) in terms of both the total cost and the task failure probability. Precisely, our proposed solution can reduce the total cost by 25%~85% and the task failure probability by 10%~35%.

Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision making of different stakeholders in the ecosystem. In this article, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the CP designs the incentive scheme for EDs to cache and share contents, and EDs decide whether to cache and share contents for the CP. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue (as incentives) shared with EDs who choose to cache and share contents, aiming at maximizing its own profit. In Stage II, EDs choose to beagentswho cache and share contents, and meanwhile gain a certain revenue from the CP, orrequesterswho do not cache but request contents in the on-demand fashion. We first analyze the EDs’ best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the nonatomic game theory. Then, we identify the piecewise structure and the unimodal feature of the CP’s profit function, based on which we design a tailored low-complexity 1-D search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. The simulation results show that both the CP’s profit and the EDs’ total welfare can be improved significantly (e.g., by 120% and 50%, respectively,) by using the proposed Crowd-MECS system, comparing with the non-MEC system where the CP serves all EDs directly.

We propose a low-complexity and highperformance joint peak-to-average power ratio (PAPR) reduction and light emitting diode (LED) nonlinearity mitigation approach for unipolar asymmetrically clipped optical-orthogonal frequency division multiplexing (UACO-OFDM) based light fidelity (LiFi) systems. This is the first reported work to apply nonlinear compression based on μ-law companding for UACO-OFDM LiFi systems to reduce PAPR. In order to avoid the effect of the nonlinearity of LED, the transmitted signals are compressed further linearly. The signal distortion caused by nonlinear and linear compression is compensated for using multiple LEDs for simultaneous transmission. The proposed joint PAPR reduction and LED nonlinearity mitigation approach provides low complexity, searching is not required. Also, it is spectrum- and energy-efficient, requiring no pilot and consuming no extra transmission power. Simulation results show that the proposed approach significantly outperforms the state-of-the-art methods in terms of complementary cumulative distribution function (CCDF) and bit error rate (BER), and that it is more robust against the LED nonlinearity than conventional UACO-OFDM LiFi systems and other methods in the literature.

Combining non-orthogonal multiple access (NOMA) with coordination techniques among base stations (BSs) is a promising solution to enhance spectral efficiency and alleviate inter-cell interference of cell-edge users in 5th generation (5G) networks. In this paper, we consider a multi-cell NOMA network with coordinated BSs in the downlink, and investigate its user coordination mode selection and resource allocation to achieve a green system. A fuzzy logic (FL) based multi-criterion scheme is proposed for user coordination mode selection. It is more robust against shadowing and fading than the previous selection schemes that classify coordinated and non-coordinated users by a single criterion. Also, the FL ranking list is fed into subcarrier allocation, leading to significant reduction in searching complexity of subcarrier allocation. A dramatic performance enhancement is achieved over the previous schemes based on single-criterion, in terms of transmission power and energy efficiency.

Ride-sharing systems enable individual car owners with idle time to provide commercial taxi-like services via an online platform. By crowdsourcing a large population of individual car owners, it can provide more flexible services with a lower serving cost, comparing with the traditional taxi system. Due to the autonomous nature of car owners (drivers), a decentralized driver dispatching algorithm that can achieve a stable (self-motivated) and optimal passenger-driver matching is highly desired for a ride-sharing system. In this paper, we will study such a driver dispatching algorithm systematically. We first show that the optimal passenger-driver matching achieved by the centralized driver dispatching algorithm is often not stable, in the sense that some drivers and passengers may break with their matched partners and form new matching pairs. To this end, we introduce a virtual order fee on each passenger (which the platform will charge the drives who want to serve the passenger) to motivate the behaviors of drivers. Specifically, we propose a novel auction-based decentralized driver dispatching algorithm, where each driver proposes the most profitable passenger that he wants to serve, considering the potential profit that he can achieve and the order fee that he needs to pay from/to serving each passenger. The virtual order fee on a passenger will be gradually increased when multiple drivers want to serve the passenger, until there exists only one driver who is willing to serve. We analytically show that such a decentralized driver dispatching algorithm will converge to an equilibrium (stable) outcome, which achieves the optimal passenger-driver matching (i.e., that maximizes the social income of the whole system). Simulation results further show how the converging speed and the achieved social income change with the system parameters such as the step size of order fee increasement. Moreover, it is easy to implement the proposed distributed algorithm in a practical system.

An operator-assisted crowdsourced WiFi community network can provide high-speed wireless data services in an inexpensive way, by encouraging a set of individual users to form a community and share their private home WiFi access points (APs) with others. Such a novel paradigm has shown great promise in achieving the ubiquitous and full coverage networks. In this paper, we perform a systemic analysis for such a community network, where users are heterogeneous in terms of both the network evaluation and the home location popularity. We formulate the interactions between the network operator and users as a non-cooperative game, and focus on the operator's pricing scheme design and the users' behavior analysis. Specifically, we propose a hybrid pricing scheme combining both the fixed price (e.g., the monthly fee) and the usage-based price (proportional to the WiFi connection time) for AP sharing among users. After analyzing users' best response towards the given pricing scheme, we characterize the dynamic changes of the membership distribution over time and indicate the market equilibrium. Simulation results show that under the different pricing schemes and different roaming qualities, the equilibrium social welfare can be increased to 137% to 147%, comparing with the tradition non-crowdsourced system.

Mobile crowdsensing (MCS) is becoming more and more popular with the increasing demand for various sensory data in many wireless applications. In the traditional server-client MCS system, a central server is often required to handle massive sensory data (e.g., collecting data from users who sense and dispatching data to users who request), hence it may incur severe congestion and high operational cost. In this work, we introduce a peer-to-peer (P2P) based MCS system, where the sensory data is stored in user devices locally and shared among users in an P2P manner. Hence, it can effectively alleviate the burden on the server, by leveraging the communication, computation, and cache resources of massive user devices. We focus on the economic incentive issue arising in the sharing of data among users in such a system, that is, how to incentivize users to share their sensed data with others. To achieve this, we propose a data market, together with a hybrid pricing mechanism, for users to sell their sensed data to others. We first study how would users choose the best way of obtaining desired data (i.e., sensing by themselves or purchasing from others). Then we analyze the user behavior dynamics as well as the data market evolution, by using the evolutionary game theory. We further characterize the users' equilibrium behaviors as well as the market equilibrium, and analyze the stability of the obtained equilibrium.

Participatory sensing (PS) is a novel and promising sensing network paradigm for achieving a flexible and scalable sensing coverage with a low deploying cost, by encouraging mobile users to participate and contribute their smartphones as sensors. In this work, we consider a general PS system model with location-dependent and time- sensitive tasks, which generalizes the existing models in the literature. We focus on the task scheduling in the user-centric PS system, where each participating user will make his individual task scheduling decision (including both the task selection and the task execution order) distributively. Specifically, we formulate the interaction of users as a strategic game called Task Scheduling Game (TSG) and perform a comprehensive game-theoretic analysis. First, we prove that the proposed TSG game is a potential game, which guarantees the existence of Nash equilibrium (NE). Then, we analyze the efficiency loss and the fairness index at the NE. Our analysis shows the efficiency at NE may increase or decrease with the number of users, depending on the level of competition. This implies that it is not always better to employ more users in the user-centric PS system, which is important for the system designer to determine the optimal number of users to be employed in a practical system.

A set-membership affine projection algorithm is applied to estimate the communication channel between the wireless sensor nodes in a general form, where the channel is modeled as a complex matrix in the presence of additive white gaussian noise. An efficient hybrid model for the affine projection algorithm is briefly introduced and the problem of matrix invertibility in some cases of the affine projection algorithm is resolved by a new method which does not use any matrix inversion. Simulations show good performance of our proposed algorithm in terms of convergence speed and demonstrate reduced complexity.

In this paper, we consider a multihop wireless sensor network (WSN) with multiple relay nodes for each hop where the amplify-and-forward (AF) scheme is employed. We present a strategy to jointly design the linear receiver and the power allocation parameters via an alternating optimization approach that maximizes the sum-rate of the WSN. We derive constrained maximum sum-rate (MSR) expressions along with an algorithm to compute the linear receiver and the power allocation parameters with the optimal complex amplification coefficients for each relay node. Computer simulations show good performance of our proposed methods in terms of sum-rate compared to the method with equal power allocation.

In this paper, we consider a general cooperative wireless sensor network (WSN) and the problem of channel estimation. We develop a matrix-based set-membership normalized least mean squares (SM-NLMS) algorithm for the estimation of the complex channel parameters in order to reduce the computational complexity significantly and extend the lifetime of the WSN by reducing its power consumption. The proposed SM-NLMS channel estimation method requires the setting of a bound for appropriate performance. However, an inappropriate and fixed error bound will result in overbounding and underbounding problems which degrade the performance significantly. Therefore, we present and incorporate an error bound function into the SM-NLMS channel estimation method which can adjust the error bound automatically with the update of the channel estimates. Computer simulations show good performance of our proposed algorithms in terms of convergence speed and steady state, reduced complexity and robustness to the time-varying environment and different signal-to-noise ratio (SNR) values.