Decentralized Federated Learning (DFL) enables collaborative model training without central coordination. However, DFL faces challenges in dynamic networks, where existing methods struggle to balance consensus rate and communication efficiency, while overlooking practical issues such as topology variation. This paper presents Dynamic AirComp-enabled DFL (DA-DFL), a novel framework that integrates over-the-air computation (AirComp) with the BASE-GRAPH consensus algorithm for efficient DFL over dynamic topologies. The convergence analysis for DA-DFL under dynamic settings is conducted to reveal the influence of the consensus period and communication errors. We define communication overhead metrics, and jointly optimize transceiver beamformers and dynamic topologies. A topology matching algorithm is developed to reduce communication overhead by aligning logical and physical topologies. Experiments show significant gains of DA-DFL in communication efficiency, e.g., reducing communication links and distances by up to 42% and 50%, respectively, compared to benchmarks.

Intelligent reflecting surface (IRS)-assisted integrated sensing and communications (ISAC) systems have been extensively studied to meet higher sensing requirements. For detection-oriented IRS-assisted ISAC problems, most studies have overlooked the detection interference caused by clutters and modeled simplified point-like targets. This paper investigates extended target detection in IRS-assisted ISAC systems within clutters. We present an optimal generalized likelihood ratio test detector and derive the corresponding probability of detection (PD) and probability of false alarm in closed form. Then, we jointly optimize the active and passive beamforming of the base station and IRS to maximize the PD under multi-user equipment (UE) communication rate constraints and the total transmit power constraint. We first simplify the complex objective function by proving the invariant property of a subspace projection matrix. We then present a novel alternating optimization (AO)-based algorithm to decouple the original problem into two subproblems, consequently convexified and solved using the semidefinite relaxation method. Simulations demonstrate the convergence of the proposed algorithm. The PD performance and the communication and sensing trade-off are significantly improved, compared to benchmarks.

The integration of global navigation satellite systems (GNSS) and fifth generation (5G) for positioning integrity monitoring has recently become a significant research focus. However, as multi-constellation GNSS and ultra-dense 5G networks are deployed, the probabilities of fault is considerably high, requiring user receivers to safeguard against numerous fault modes resulting from various simultaneous independent faults. This study introduces a fast advanced receiver autonomous integrity monitoring (FARAIM) approach utilizing GNSS/5G integration, featuring a solution separation (SS) algorithm that avoids calculating fault-tolerant position solutions, thereby reducing the number of tests traditionally required. In the FARAIM framework, we discover that adding a single 5G measurement, along with the augmentation of cellular base station clock bias states into a multi-constellation GNSS, does not completely degrade the system’s fault detection capabilities, as evidenced by the SS test’s reduced performance and the consistency of the chi-square test. We derive a tighterGershgorin boundfor the FARAIM algorithm across the overall GNSS/5G measurements. Additionally, we analyze the protection level (PL) performance for adding a single 5G measurement. Finally, autonomous ground vehicle (AGV) experimental results validate the effectiveness of the proposed FARAIM algorithm in enhancing navigation performance and evalute theoretical findings regarding the influence of 5G measurements on integrity parameters.

This paper proposes a new Signal-to-interference-plus-noise ratio (SINR)-based Device selection, Power control, and Reconfigurable intelligent surface (RIS) configuration (SDPR) algorithm, which allows imperfect aggregation of wireless federated learning (FL) in RIS-assisted Non-Orthogonal Multiple Access (NOMA) systems. The SDPR algorithm selects the local models with SINRs within an acceptable range for global aggregations, benefiting FL from involving more local models with tolerable errors. The convergence of FL under the imperfect aggregation is analytically validated, where the influence of the local model quantization and modulation is captured through the translation of the SINR thresholds to the symbol error rates (SERs). Employing successive convex approximation and gradient descent, we jointly optimize the RIS configuration and the transmit powers of participating devices, thereby minimizing the convergence upper bound of FL under imperfect aggregation. Experimental results demonstrate that using SDPR, FL achieves superior convergence and accuracy by effectively utilizing model updates, even if they are received with errors. Moreover, more quantization bits do not necessarily offer better FL accuracy, and need to be tailored under specific SERs.

Federated learning (FL) contributes to data privacy by not disclosing raw data, but encounters challenges of privacy leakage from local gradient uploading. This paper introduces a novel over-the-air computation (AirComp)-based FL system that balances privacy and accuracy by leveraging the waveform superposition and channel propagation characteristics of AirComp. Specifically, we derive the privacy leakage metric to explicitly account for the effects of waveform aggregation and communication noise. We analyze the convergence upper bound to capture model update errors stemming from artificial and communication noise. We formulate a new joint privacy-accuracy optimization problem by incorporating privacy leakage in the model training objective, guiding the learning process towards enhanced privacy protection. We then employ convex optimization techniques to derive the optimal power scaling and artificial noise intensity. Simulations demonstrate up to 80% reduction in privacy leakage compared to baselines under stringent privacy constraints, while maintaining competitive learning performance. Our method exhibits enhanced robustness under low signal-to-noise ratios, achieving 40% lower privacy leakage under equivalent privacy budgets.

Blockchain-based federated learning (BFL) has gained attention for its potential to establish decentralized trust. While existing research primarily focuses on personalized frameworks for various applications, essential aspects including incentive mechanisms—critical for ensuring stable system operation—remain under-explored. To bridge this gap, we propose a game-theoretic incentive mechanism designed to foster active participation in BFL tasks. Specifically, we model a BFL system comprising a model owner (MO), i.e., task publisher, multiple miners, and training terminals, framing their interactions through two-tier Stackelberg games. In the first-tier game, the MO designs reward strategies to incentivize training terminals to contribute more data, enhancing model accuracy. The second-tier game introduces a multi-leader multi-follower Stackelberg game, enabling miners to set model packaging prices based on competitors' strategies and anticipated user behavior. By deriving the Stackelberg equilibrium, we identify optimal strategies for all participants, leading to an incentive mechanism balancing individual interests with overall performance. Compared to its benchmarks, our incentive mechanism offers 5.8% and 53.4% higher utilities in the two games compared to its alternatives, accelerating convergence and improving accuracy.

Reconfigurable intelligent surface(RIS)-assisted communication systems have been extensively studied for providing high-precision location services. However, most studies have overlooked the impact ofcarrier frequency offset(CFO) andphase noise(PN) resulting from hardware impairments on localization. This paper presents a novel,alternating optimization(AO)-based algorithm to jointly estimate the CFO, PN, anduser equipment(UE) position inorthogonal frequency division multiplexing(OFDM) systems, where, provided the UE position, closed-form expressions for the CFO and PN are derived per iteration, significantly reducing the complexity and enhancing the stability of the algorithm. Another important aspect is a new RIS phase shift optimization algorithm developed to minimize the analytical lower bound of localization accuracy, hence benefiting localization. The semidefinite relaxation method and Schur complement are utilized to convexify this challenging non-convex optimization problem to a semidefinite program. Simulations demonstrate the effectiveness of the proposed algorithms, with the localization accuracy enhanced by two orders of magnitude. The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than 10−2m.

The fifth generation (5G) positioning, a breakthrough in cellular navigation, revolutionizes location services. Multistatic time-of-arrival (TOA) 5G localization is a topic of significant interest due to its exceptional performance benefits. The spatial arrangement of unmanned aerial vehicles (UAVs) and the positions of the targets play a crucial role in precisely determining the target’s location in 5G environments. This study introduces a novel approach to enhance multistatic 5G localization performance through the placement optimization of UAVs. The derivation of the Cramér-Rao lower bound (CRLB) for TOA-based multistatic 5G localization is given, using unit norm vectors instead of conventional trigonometric parameterizations. A distinctive dual iteration majorization-minimization (DIMM) algorithm is derived, grounded in the MM principle. Our method outperforms current state-of-the-art algorithms tailored for uncorrelated noise in measurements, as it effectively addresses both uncorrelated and correlated noise scenarios. Additionally, the proposed method outperforms both gradient descent and alternating directions method of multipliers (ADMM) approaches in terms of performance. A comprehensive analysis of computational complexity and convergence attests to the pragmatic viability of our methodology. Rigorous simulations affirm its effectiveness across diverse noise variances and UAV-target distances.

Relay-enabled cooperative communication has been a hot topic in the area of the Internet of Things (IoT), for its help in providing important solutions to resource limitations in IoT communication scenarios. In this article, we study the outage minimization problem in a power-limited decode-and-forward (DF) relaying network with environmental uncertainty. To reduce the outage probability and improve the quality of service, existing researches usually rely on the assumption of both exact instantaneous channel state information (CSI) and environmental uncertainty. However, it is difficult to obtain perfect instantaneous CSI immediately under practical situations where channel states change rapidly, and the uncertainty in communication environments may not be observed, which makes traditional methods not applicable. Therefore, we turn to reinforcement learning (RL) methods for solutions, which do not need any prior knowledge of underlying channels or assumptions of environmental uncertainty. The RL method is to learn from the interaction with the communication environment, optimize its action policy, and then propose relay selection and power allocation schemes. In this work, we first analyze the robustness of RL action policy by giving the lower bound of the worst case performance, when RL methods are applied to communication scenarios with environment uncertainty. Then, we propose a robust algorithm for outage probability minimization based on RL. Simulation results reveal that compared to traditional RL methods without robust design, our approach has good generalization ability and can improve the worst case performance by about 4%.

Modern inertial measurement units (IMUs) with low cost, small size, and low power consumption are the key to improving indoor positioning accuracy. However, the performance of IMU in current mobile phones is spotty. The IMU positioning error of mainstream cell phones is basically greater than 1%, which makes it difficult for indoor fusion positioning technology to achieve accuracy within 3 m on a mobile phone, seriously restricting the development of the industry. Therefore, the existing indoor fusion algorithms need to introduce additional hardware, such as high-density UWB/Wi-Fi/Bluetooth deployment, to compensate for the lack of IMU performance, which significantly increases the deployment cost, difficulty, and reduces environmental applicability. It is found that deep learning can greatly improve the performance of IMU, but traditional deep learning methods have problems, such as privacy protection, difficulty in data collection, and low training efficiency. Therefore, we propose a novel data-driven inertial navigation method based on federated learning, named FedINS, to improve IMU performance and solve the above-mentioned problems. In order to further improve performance and reduce hardware cost, we introduce the concept of segment fusion. FedINS2 (2 means the second S, which is the segment) is formed by combining FedINS with low-cost edge-end ranging, which has terminal computing capabilities and edge-end ranging capabilities. FedINS2 not only greatly improves the performance of the smartphone IMU from 3.6% to 0.8%, but also has the characteristics of privacy protection and efficient data collection. Experimental results demonstrate that the proposed data-driven inertial navigation algorithm is effective.

This article is dedicated to addressing the localization challenge in 5G environments, specifically utilizing time-of-arrival (TOA) measurements. The focus is on scenarios where the user equipment (UE) and base transceiver stations (BTSs) or first-generation NodeBs (gNBs) face challenges related to synchronization or inaccurate BTS or gNB positions. First, we present the range-weighted majorization minimization (RW-MM) algorithm, harnessing majorization minimization (MM) techniques for UE localization. We rigorously establish the algorithm’s monotonicity and demonstrate its convergence to a stationary point, providing a theoretical foundation for the RW-MM method. In addition, we present a novel UE localization method called robust range-weighted semidefinite relaxation (RRW-SDR). This method is specifically designed for situations involving bounded gNB position errors. The RRW-SDR algorithm optimizes the worst-case weighted least square function while uniquely addressing the individual error constraints associated with each gNB. Furthermore, we derive the Cramér-Rao lower bound (CRLB) for TOA-based UE localization. We additionally establish a more stringent lower bound on the determinant of the target estimation error covariance. This is particularly significant when dealing with scenarios that include independent measurement noise with varying variances. Moreover, we identify an optimal user-gNB geometrical configuration capable of achieving this lower bound. To validate the effectiveness and practical applicability of our contributions, we conduct a series of meticulous numerical simulations. These experiments affirm the robustness and utility of the developed methods.

The cellular network of magnetic Induction (MI) communication holds promise in long-distance underground environments. In the traditional MI communication, there is no fast-fading channel since the MI channel is treated as a quasi-static channel. However, for the vehicle (mobile) MI (VMI) communication, the unpredictable antenna vibration brings the remarkable fast-fading. As such fast-fading cannot be modeled by the central limit theorem, it differs radically from other wireless fast-fading channels. Unfortunately, few studies focus on this phenomenon. In this paper, using a novel space modeling based on the electromagnetic field theorem, we propose a 3-dimension model of the VMI antenna vibration. By proposing “conjugate pseudo-piecewise functions” and boundary$p(x)$distribution, we derive the cumulative distribution function (CDF), probability density function (PDF) and the expectation of the VMI fast-fading channel. We also theoretically analyze the effects of the VMI fast-fading on the network throughput, including the VMI outage probability which can be ignored in the traditional MI channel study. We draw several intriguing conclusions different from those in wireless fast-fading studies. For instance, the fast-fading brings more uniformly distributed channel coefficients. Finally, we propose the power control algorithm using the non-cooperative game and multiagent Q-learning methods to optimize the throughput of the cellular VMI network. Simulations validate the derivation and the proposed algorithm.

The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold.

Not only has information technology evolved rapidly, but the spatial cognition theory for blind and visually impaired (BVI) people has also made great strides, which has opened up a new opportunity for indoor travel assistance systems (ITASs). However, there are still some issues that have not been effectively addressed due to the lack of guidance of the spatial cognition theory. Thus, this article presents a comparative survey among ITASs proposed in the last four years in an effort to inform researchers and developers about system problems and challenges and inform BVI people about the various types and functions of the ITAS. This article will also make researchers and developers aware of the importance of the spatial cognition theory. Furthermore, we give predictions for future trends based on a detailed analysis of 17 ITASs.

The advantages of millimeter-wave and large antenna arrays technologies for accurate wireless localization have received extensive attentions recently. However, how to further improve the accuracy of wireless localization, even in the case with obstructed line-of-sight, is largely undiscovered. In this paper, the reconfigurable intelligent surface (RIS) is introduced into the system to make the positioning more accurate. First, we establish the three-dimensional RIS-assisted wireless localization channel model. After that, we derive the Fisher information matrix and the Cramér-Rao lower bound for evaluating the estimation of absolute mobile station position. Finally, we propose an alternative optimization method and a gradient decent method to optimize the reflect beamforming, which aims to minimize the Cramér-Rao lower bound to obtain a more accurate estimation. Our results show that the proposed methods significantly improve the accuracy of positioning, and decimeter-level or even centimeter-level positioning can be achieved by utilizing the RIS with a large number of reflecting elements.

Now indoor Location-Based Services (LBSs) have numerous applications built on indoor navigation. However, current indoor navigation can only meet the needs of finding a certain point of interest (POI), it can not help to find a certain person. Therefore, we propose Location Paging and Answering Service (LPAS) for finding out someone in an indoor environment. However, it may face serious problems of privacy disclosure of the clients' indoor location data. To ensure security, privacy and convenience, we leverage blockchain to model the indoor environment, and build Blockchain-Based Indoor LPAS (BILPAS) by embedding our three-way-handshake Diffie-Hellman Key Agreement (DHKA) procedures. Our BILPAS can automatically establish a secure and privacy communication tunnel between any two users, greatly liberating participants from unnecessary self-determining interactions. We present the proof-of-concept prototype and implement it on Hyperledger Fabric. Then, we conduct the security and privacy analysis and evaluate the time overhead. The experiment results validate the high privacy, security and convenience of our BILPAS.

Reconfigurable intelligent surface (RIS) technology can significantly improve the energy and spectrum efficiency of wireless communication systems. Most existing studies were conducted with an assumption of ideal hardware, while the impact of hardware impairments receives little attention. However, the non-negligible hardware impairments should be taken into consideration when we evaluate the system performance. In this paper, we consider an RIS assisted communication system with hardware impairments, and focus on the channel estimation study and the power scaling law analysis. First, with linear minimum mean square error estimation, we theoretically characterize the relationship between channel estimation performance and impairment level, number of reflecting elements, and pilot power. After that, we analyze the power scaling law and reveal that if the base station (BS) has perfect channel state information, the transmit power of user can be made inversely proportional to the BS antenna number and the square of the reflecting element number with no reduction in performance; If the BS has imperfectly estimated channel state information, to achieve the same performance, the transmit power of user can be made inversely proportional to the square-root of the BS antenna number and the square of the reflecting element number.

Intelligent reflecting surface (IRS) is envisioned as a promising hardware solution to hardware cost and energy consumption in the fifth-generation (5G) mobile communication network. It exhibits great advantages in enhancing data transmission, but may suffer from performance degradation caused by inherent hardware impairment (HWI). For analysing the achievable rate (ACR) and optimizing the phase shifts in the IRS-aided wireless communication system with HWI, we consider that the HWI appears at both the IRS and the signal transceivers. On this foundation, first, we derive the closed-form expression of the average ACR and the IRS utility. Then, we formulate optimization problems to optimize the IRS phase shifts by maximizing the signal-to-noise ratio (SNR) at the receiver side, and obtain the solution by transforming non-convex problems into semidefinite programming (SDP) problems. Subsequently, we compare the IRS with the conventional decode-and-forward (DF) relay in terms of the ACR and the utility. Finally, we carry out simulations to verify the theoretical analysis, and evaluate the impact of the channel estimation errors and residual phase noises on the optimization performance. Our results reveal that the HWI reduces the ACR and the IRS utility, and begets more serious performance degradation with more reflecting elements. Although the HWI has an impact on the IRS, it still leaves opportunities for the IRS to surpass the conventional DF relay, when the number of reflecting elements is large enough or the transmitting power is sufficiently high.

Intelligent reflecting surface (IRS) technology has emerged as a promising way to improve the energy efficiency of wireless communication systems with less complexity and hardware cost. Most existing studies are conducted with ideal hardware, however, both physical transceiver and IRS suffer from non-negligible hardware impairments which may greatly degrade the system performance. In this paper, by considering hardware impairments, we focus on the energy efficiency analysis of IRS system. Our first contribution is to derive the optimal receive combining and transmit beamforming vectors. After that, we characterize the asymptotic channel capacity. With the derived asymptotic channel capacity and the power consumption model, the analytical upper and lower bounds on the maximal energy efficiency are provided. Our results show that an IRS system can achieve both high spectral efficiency and high energy efficiency with moderate number of antennas. This observation is encouraging for that there is no need to cost a lot on expensive high-quality antennas, which corresponds to the requirements of new communication paradigms.

With the development of precise positioning technology, a growing number of location-based services (LBSs) facilitate people's life. Most LBSs require proof of location (PoL) to prove that the user satisfies the service requirement, which exposes the user's privacy. In this paper, we propose a zero-knowledge proof of location (zk-PoL) protocol to better protect the user's privacy. With the zk-PoL protocol, the user can choose necessary information to expose to the server, so that hierarchical privacy protection can be achieved. The evaluation shows that the zk-PoL has excellent security to resist main attacks, moreover the computational efficiency is independent of input parameters and the zk-PoL is appropriate to delay-tolerant LBSs.

Ratings online are often listed alongside product recommendations, but to date, limited attention has been paid as to how credible these ratings present to end-users. For lack of an effective rating verification scheme, a cheater could fabricate some high ratings and the recommended product usually does not match the corresponding ratings. To make ratings verifiable, we encapsulate each rating into a specific structure whose core is unverified rating output (URTO) that we propose. The URTO is designed based on unspent transaction output (UTXO) in Bitcoin. On this foundation, we propose the online verifiable rating system. Our system can make ratings verifiable and the evaluated validation and mining time of a rating by the whole network peers demonstrate the high reliability of our system.

The Internet of Things (IoT) is expected to connect a massive number of smart devices to the Internet. The existing centralized architecture for handling the huge volume of data created in the IoT is facing many research challenges, including security and privacy, trustworthiness, operational challenges, business models and the practical aspects, and legal and compliance issues. These challenges ask for new approaches to online identity, trustworthy transactions, and resilient networks.

This paper addresses the channel uncertainty issue in the multi-input multi-output (MIMO) cloud radio access network (C-RAN) by studying the robust beamforming. Our objective is to minimize the overall network power and backhaul cost while guaranteeing the users' SINR constraints for a target proportion of users. The channel state information (CSI) is assumed to be imperfect and the additive channel state information error is modeled as Gaussian distributed variables. We model the total power by ℓ0/ℓ2-norm functions and use the semidefinite programing (SDP) and ℓ0-norm approximation to transform the original problem into tractable ones. Then, probability approach are proposed to deal with the CSI uncertainty and an alternating direction method of multipliers (ADMM) based algorithm is utilized to solve the transformed optimization problem. Simulation results verify that the proposed robust designs can significantly enhance the performance compared the non-robust case and efficiently resolve the channel uncertainty issue.

In this paper, we study linear transceiver designs for indoor visible light communications (VLCs) with multiple light emitting diodes (LEDs). Specifically, we investigate VLCs including white emitting diodes and VLCs including red/green/blue (RGB) LEDs. The transmitter precoding and the offset are jointly designed by considering certain key practical lighting constraints, such as optical power, non-negativeness, and color illumination. Various non-convex transceiver design problems are formulated aiming to minimize total mean-square-error to improve transmission reliability. We show that for multi-input single-output white VLCs, the optimal precoding reduces to a simple LED selection strategy. For multi-input multi-output (MIMO) white VLCs, we prove that the optimization problem with multiple constraints can be equivalently simplified to a problem with single constraint, which enables us to propose efficient algorithms to search local optimal solutions. For MIMO RGB VLCs, by using certain useful transformations, we show that the precoding design is equivalent to covariance matrix design of transmit signals, which can be further transformed to a convex optimization problem. To develop an algorithm to find the optimal solution, we derive the optimal structure of the covariance matrix and show that the optimal solution can be obtained via a water-filling approach. Extensive simulation results are provided to verify the performance of the proposed designs.

Magnetic induction (MI) has been proven to be an efficient wireless communication technique for overcoming the transmission challenges in some very harsh propagation environments, such as underground, underwater, etc. For a random distributed MI ad hoc network in a 3-D space composed of uniform medium, we propose a method for determining the required node density and transmitting power that creates an almost surely fully connected network. For which we involve an MI path-loss model and consider the effect of eddy currents, the effective coverage space and the expected node degree of an MI node are then calculated by a Lambert W-function-based integration. Finally, we propose optimized frequency selection methods for improving the connectivity of MI networks. In addition to an ideal frequency-switching optimization method, we provide for engineering applications a practical frequency-fixed optimization method, which is based on the gradient descent algorithm, where an improved initialization is used to reduce iterations.

Among most of the message scheduling strategies for low-density parity-check (LDPC) codes, the dynamic scheduling strategy behaves best in error correction performance. Dynamic selection is an integral part of dynamic scheduling decoding, which plays a decisive role throughout the decoding process. Usually, the dynamic selection strategy based on the message residuals only is employed in dynamic decoding algorithms, while other potentials of the dynamic selection strategy are rarely cared about. In this paper, we propose the triple judgment dynamic selection strategy combined with a Stability Criterion. Interestingly, the new strategy can be well applied to two different dynamic algorithms, namely, the V-VCRBP and the V-CVRBP algorithms. The proposed strategy has a great advantage: locating the message to be preferentially updated is extremely quick and accurate. Simulation results demonstrate that the V-VCRBP algorithm outperforms existing decoding algorithms in terms of BER performance and convergence speed, while the V-CVRBP algorithm has good error correction performance with a lower computational complexity.

Magnetic induction (MI) is a promising technique for communications in the very harsh propagation conditions like underground or underwater, etc. In this paper we investigate the connectivity issue of MI ad hoc networks deployed in a three-dimensional space. The transmitting distance of MI covers a quasi- ellipsoid space other than a standard sphere space in a traditional wireless propagation case. We derive a closed-form expression for the effective coverage space of an MI node to evaluate the expectation of node degree. Moreover, the probability of node isolation is derived, which provides an upper bound of network connectivity probability. With these results, pairs of the critical transmitting magnetic momentum and node density can be obtained to keep the network connected with a probability close to one. Our analytic method provide a guidelines for future studies on MI networking.

This paper studies how to efficiently reduce the mutual interference between primary and secondary networks. Different to conventional cognitive system, in which primary and secondary networks have no information exchange, the media access control mechanism we propose allow an operating PU to share the length of its remaining occupation time to SUs. With the help of such information exchange, the primary network can be better protected and the secondary network has the potential to reduce the energy consumed by spectrum sensing. To realize such a dynamic information sharing, we propose a novel spectrum-sculpting-aided scheme in this paper. Specifically, the PU deliberately inserts one zero-subcarrier into its data carriers, and uses the zero-subcarrier's position to claim its status. Through two-step sensing, each SU can attain the PU's remaining operation time, and then adjust its sensing strategy to avoid interference to the PU. Analysis of interference probability, together with simulation results, clearly exhibit the advantages of the proposed scheme in terms of the mutual interference and the amount of SU's sensing times.

This paper applies device-to-device technique to vehicular-to-vehicular communications (D2D-V) in an underlay fashion. Different from conventional D2D systems focusing on smart phones, whose positions are usually assumed to be statical, the D2D-V system suffers more mobility and uncertainties. These dynamic features indeed complicate the design of D2D-V system, but also, give more potential to accomplish some geographic based algorithms. Armed with the geographic knowledge of highway and the dynamic GPS information in each vehicle, we propose a geographic based reuse cellular user selection scheme and two different distributed power control schemes to serve various applications in vehicular networks. We model the problem firstly, then formulate two metrics: the sum rate and the minimum-achievable rate to evaluate the performance of the proposed schemes. Simulations are proposed to validate our schemes and analysis. Influences of different system settings are also discussed here.

Based on the complex network theory, this paper proposed an energy-aware Barabasi-Albert (EABA) model for wireless sensor networks (WSNs). Unlike existing research, tunable coefficients were introduced into the model for balancing connectivity and energy consumption in the network. Mean-field approach was applied to study the degree distribution of EABA. Both theoretical analysis and simulation indicate that EABA is asymptotically power-law, with an exponent no smaller than 3.

In this paper, we use complex network theory to analyze the evolving topology of wireless sensor networks (WSNs). Based on the BA model, we propose a novel complex network model, i.e., Neighborhood Log-on and Log-off model with energy awareness (NLL-E). NLL-E assumes locally preferential attachment, which exists in many complex networks. We further consider node addition and invalidation in topology evolvement, and add compensation mechanism for network robustness. The statistical properties and dynamics of NLL-E are analytically studied. Numerical simulations indicate that, comparing with BA, NLL-E has decreased average path length and increased network connectivity.

We consider the problem of relay-assisted transmission for cellular networks. In the considered system, a source node together with n relay nodes are selected in a proportionally fair (PF) manner to transmit to the base station (BS), which uses the maximal ratio combining (MRC) to combine the signals received from the source node in the first half slot and the n relay nodes in the second half slot for successful reception. The proposed algorithm incorporates the PF criterion and cooperative diversity, and is called proportionally fair cooperation (PFC). Compared with the proportional fair scheduling (PFS) algorithm, PFC provides improved efficiency and fairness. The ordinary differential equation (ODE) analysis used to study PFS cannot be used for PFC; otherwise, one has to solve a large number of nonlinear and interrelated ODE equations which is time prohibited. In this paper, we present a mathematical framework for the performance of PFC. The cornerstone of our framework is a realistic yet simple model that captures node cooperation, fading, and fair resource allocation-induced dependencies. We obtain analytical expressions for the throughput gain of PFC over traditional PFS without node cooperation. Compared with the highly time-consuming ordinary differential equation analysis, our formulae are intuitive yet easy to evaluate numerically. To our knowledge, it is the first time that a closed-form expression is obtained for the throughput of relay-assisted transmission in a cellular network with the PF constraint.

This study considers utility-based resource allocation in backbone wireless mesh networks (WMNs). Unlike single-hop cellular networks, a WMN has multi-hop transmissions with multiple contending links, and thus requires more careful design for resource allocation. To this end, we provide a clique-based method with efficient spatial reuse, which is then incorporated into proportionally fair scheduling (PFS) for fair resource management in WMNs. We call it a clique-based proportionally fair scheduling (CBPFS) algorithm. The linear and/or logarithmic rate models used to analyze PFS in single-hop cellular networks cannot be used to analyze CBPFS in backbone WMNs. Using stochastic approximation and recent results on rate modeling for Rayleigh fading channels, we conduct mathematical analysis and obtain a closed-form model to quantify CBPFS performance, without the need of the highly time-consuming ordinary differential equation (ODE) analysis. We use the derived analytical framework to estimate the link throughput of CBPFS and compare it with simulations. It is the first time a closed-form analytical model is developed to quantify the throughput of links in a multi-hop network where links are proportionally fair scheduled.

This paper is concerned with the analysis of proportionally fair scheduling (PFS), and we provide an analytical approximation for the PFS throughput over Rayleigh fading channels. Though quite accurate, the ordinary differential equation (ODE) analysis, typically used to analyze the PFS throughput, is highly time-consuming when there are lots of users. On the other hand, due to the intricate interplay among these ODE equations, the ODE analysis generally fails to provide a closed-form approximation for estimating the PFS throughput unless with simplified models such as the linear rate model to characterize channel capacity. Our aim is to provide a novel framework to evaluate PFS in Rayleigh fading without the above-mentioned limitations. To put our work on a firm base, we use results of stochastic approximation in the analysis and take the Gaussian approximation for capacity modeling for fading channels. Simulations validate this approach and show that our analytic result provides highly accurate estimate of the PFS throughput. Compared to existing studies, our work advances the state of the art in three ways. First, it goes beyond the linear rate model and applies to the commonly used Shannon rate model. Second, it provides accurate estimate of the PFS throughput without the need for the time-consuming ODE analysis. Third, it provides a unified closed-form expression for estimating the PFS throughput for both the linear rate model and the Shannon rate model. It is interesting to note that our analysis provides the same result as existing studies when assuming the linear rate model. More importantly, our formula is intuitive yet easy to evaluate numerically.

In a wireless network, connectivity is arguably the most critical issue that requires significant study since a network can hardly function well if it is disconnected. On the other hand, there are extraordinary interests in exploiting cooperative techniques in wireless networks in recent years. This paper studies the connectivity problem of large cooperative ad hoc networks. Unlike traditional cooperative networks where all nodes are willing to transmit in a collaborative manner, the cooperative network we considered does not assume that all nodes would like to transmit cooperatively when relaying other nodes' traffic. In other words, each node exhibits some sense of selfishness. Specifically, we model nodes in such a way that each node cooperatively transmits with p-selfishness or location-based p-selfishness when relaying other nodes' traffic. For the considered network, we assume that nodes are generated according to a Poisson Point Process (PPP) and techniques based on stochastic geometry and percolation theory are used to analyze the connectivity in such system. To quantify the performance of the cooperative ad hoc network with selfish behavior, we provides an upper bound of node density for such network to maintain connectivity.

We consider the problem of selective relaying in multi-hop networks. At each slot, a relay and a node along the optimal non-cooperative path are opportunistically selected to transmit to the next-hop node in a cooperative manner. Being a promising scheme for fair resource allocation, the proportional fair scheduling (PFS) algorithm provides excellent balance between throughput and fairness via multi-user diversity and game-theoretic equilibrium. To maximize the overall utility along a cooperative multi-hop path, we apply the proportional fair (PF) criterion in selecting nodes and relays for cooperative transmission. Furthermore, we analyze and provide an analytical expression for end-to-end throughput of an opportunistic relaying, cooperative multi-hop path with proportional fairness constraints over a Rayleigh flat-fading channel. To our knowledge, it is the first time that a closed-form expression is obtained for the throughput of a proportional fair relaying, cooperative multi-hop path. This research is an extension of previous theoretical work on PF for cellular networks.

We consider the problem of fair resource allocation for multi-carrier systems. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multi-user diversity. Previous work in this area has focused on the single-user scheduling in single-carrier systems over a narrowband flat-fading channel, where only one node is scheduled at a time. In wideband multi-carrier systems, multiple nodes can be scheduled concurrently over multiple narrowband channels. In this paper, we analyze proportional fair scheduling (PFS) in multi-carrier systems over a wideband frequency-selective channel. In particular, we first derive analytical expressions for the throughput of opportunistic scheduling under proportional fairness constraints in a frequency-selective channel, for both single-user and multi-user systems. Furthermore, we provide closed-form expression to quantify the throughput benefit of the multi-user PFS over the single-user PFS in frequency-selective systems. This research is an extension of our previous theoretical work on opportunistic scheduling over flat-fading channel in narrowband single-carrier systems.

Proportional fair scheduling (PFS) provides good balance between throughput and fairness via multi-user diversity and game-theoretic equilibrium. Very little analytical work exists on understanding the performance of PFS. Moreover, most researches on PFS are for cellular networks and typically use linear rate model or logarithm rate model to simplify the theoretical analysis of PFS. Since the linear rate model only applies to very small SINR, most researchers prefer the logarithm rate model in their study on PFS. While previous work which is based on the logarithm rate model provides good estimate of the PFS throughput in Rayleigh fading single-antenna cellular networks, they are not valid for multi-antenna wireless mesh networks. In this paper, PFS in multi-antenna wireless mesh networks under Rayleigh fading is discussed. Specifically, we assume that orthogonal frequency-division multiplexing (OFDM) and single-input multi-output (SIMO) techniques are used in the network. In addition, a new mathematical analysis of PFS that applies to both Rayleigh and Rician fading is presented. Simulations are conducted to verify our analytic results on PFS in the proposed mesh network. To the best of our knowledge, this work is the first one investigating the PFS problem in multi-antenna mesh networks.

The proportional fair scheduling (PFS) problem is studied in the paper. PFS is considered an attractive bandwidth allocation criterion in wireless networks for supporting high resource utilization while maintaining good fairness among network flows. The most challenge of a PFS problem is the lack of an analytic expression. By rigorously mathematical derivation, we obtain a closed-form expression for the throughput of PFS in Rayleigh fading environment. The theoretical results are compared with those from simulations. The derived model is shown to provide a high accuracy in evaluating the throughput of the PFS algorithm in Rayleigh fading networks. In particular, the expression presented here will provide great help for the system design of a PFS capable network. Moreover, compared with existing analytical results on PFS, our expression is more general in that we do not require the i.i.d relationship among nodes in our derivation.

Proportional fair scheduling (PFS) provides good balance between throughput and fairness via multi-user diversity and game-theoretic equilibrium. Very little analytical work exists on understanding the performance ofPFS. Most existing prior results are for networks with Rayleigh fading. In this paper, we provide theoretical results forPFSin general fading environments. The results reveal that the average throughput of a user solely depends on its own channel statistics when its instantaneous data rate isGaussian. Based on the theoretical results, we analyze thePFSperformance under various scenarios withRayleighand/orRicianfading, and the numerical results match very well with the simulation ones. To the best of our knowledge, this work is the first one theoretically investigating thePFSproblem in general fading environments.

We investigate the proportional fair scheduling (PFS) algorithm, with the objective of obtaining an analytic expression for it. In this paper, we derive a closed-form expression for the throughput of PFS in wireless networks. The theoretical results from analysis are compared with those from simulations. The analytic model is shown to provide a high accuracy in evaluating the throughput of the PFS algorithm in Rayleigh fading environment.

This paper provides analytical expressions to evaluate the performance of a random access wireless network in terms of user throughput and network throughput, subject to the constraint of proportional fairness amongst users. The proportional fair scheduling (PFS) algorithm is considered an attractive bandwidth allocation criterion in wireless networks for supporting high resource utilization while maintaining good fairness among network flows. The most challenge of a PFS problem is the lack of analytic expression. Though the PFS algorithm has been a research focus for some time, the results are mainly obtained from computer simulations. It is known that a PFS problem is NP-hard and, until recently, there are very few papers which give analytic insights into the PFS algorithm. Typically, existing works use simplified form of the PF preference metric and assume simple linear model, or the given analytic expression is valid only for very limited cases. In this research, we give analytical results of the PFS algorithm by providing closed-form expressions for the throughput in Rayleigh fading networks. We use Gaussian approximation method to model the feasible data rate in Rayleigh fading environments. Results obtained from the simulation and numerical analysis verifies the high accuracy of the closed-form expressions given in the paper. In particular, the analytic expressions given here will provide great help for the system design of a PFS-enable network, not only in that it is obtained from more realistic rate model, but also it applies to various kinds of network scenarios.

Being an evolution of IEEE 802.16e, IEEE 802.16j mobile multi-hop relay (MMR) is proposed to gain coverage extension and throughput enhancement. This research conducts theoretical analysis and performance evaluation on the on-demand bandwidth allocation in IEEE 802.16j MMR networks. Theoretical analysis shows that such systems using conventional bandwidth allocation will seriously suffer from low bandwidth utilization. We then develop a new, spectrum efficiency based adaptive resource allocation algorithm. Simulation results verified that, compared with conventional on-demand bandwidth allocation method, the proposed method is more bandwidth efficient and exhibits better fairness in such networks. In terms of throughput, packet loss rate and delay, the performance of SEBARA algorithm is further evaluated in an OPNET-based 802.16j MMR simulation platform. Both theoretical analysis and simulation indicate the novel method provides an attractive solution for bandwidth allocation in 802.16j MMR networks.

A new coded cooperation scheme is proposed to efficiently enhance the error correction capability for multi-hop path diversity. In this scheme, an intelligent coding scheme is used to generate correlated codewords delivered over multiple paths. The codeword over each path is self decodable to efficiently prevent error accumulation along the path. In the case of decoding failure in the intermediate nodes, different from conventional schemes, the intermediate nodes continue forward irrecoverable codeword to the destination instead of dropping it. This correlated codeword, when combined with other codewords from different paths at the destination, enables the receiver to recover large errors. The simulations are used to verify the effectiveness of the proposal scheme.

Though IEEE 802.16 MAC protocols have been proposed to support quality of service (QoS) guarantees for various kinds of applications, they do not suggest how to allocate service bandwidth to fulfill QoS requirements. Most of the traditional methods of bandwidth allocation in wireless networks treat uplink and downlink bandwidth allocation independently. This research conducts theoretical analysis on the on-demand bandwidth allocation in an IEEE 802.16 single-hop self-backhaul network using 3-sector base station. Simulations have shown that such wireless backhaul systems using conventional on-demand bandwidth allocation will seriously suffer from fairness issues among different sectors of the base station. We then develop a new, joint dynamic bandwidth allocation algorithm for IEEE 802.16 single-hop self-backhaul network using 3-sector base station. Simulation results verified that, compared with conventional on-demand bandwidth allocation method, by considering both uplink and downlink bandwidth requests jointly, the proposed method is more bandwidth efficient and exhibits better fairness.

This paper presents a novel and efficient error correction scheme with high recovery capability, especially for burst error wireless channels. The proposed scheme introduces a correlated FEC approach that not only increases the FEC recovery capability within its own block, but also in preceding block(s). It uses special algorithm that generates specially correlated coded FEC. This correlated code, when combined with other FEC codes in different block(s), enables the receiver to recover large errors, especially suitable for the recovery of burst losses. This approach is more efficient as it has higher recovery probability than a traditional one with similar amount of redundancy. The ability to substantially improve the recovery probability also enhances system reliability, when integrated with adaptive mechanism.