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In this paper, we study the energy efficiency (EE) optimization for simultaneous wireless information and power transfer in multiple-input-multiple-output two-way amplify-and-forward relay networks, where the energy-constrained relay harvests energy from two sources’ signals by power splitting (PS) scheme to forward the information of two sources. Our objective is to jointly design the sources and relay precoding matrices and the PS ratio to maximize the EE of the network, under the transmit power constraints at sources and relay and the minimum spectral efficiency requirement. We transform the nonconvex precoding and PS design problem into a difference of convex programming and propose an efficient iterative algorithm based on the constrained concave convex procedure to obtain a local optimum. To reduce computational complexity, we propose a generalized singular value decomposition based channel diagonalization scheme to simplify the optimization into a joint source and relay power allocation problem. The alternating optimization method is employed to solve this problem and a suboptimal solution to the EE maximization problem is obtained. Simulation results demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

The development of analytical models to analyze the behavior of vehicular ad hoc networks (VANETs) is a challenging aim. Adaptive methods are suitable for many algorithms (e.g., choice of forwarding paths, dynamic resource allocation, channel control congestion) and services (e.g., provision of multimedia services, message dissemination). These adaptive algorithms help the network to maintain a desired performance level. However, this is a difficult goal to achieve, especially in VANETs due to fast position changes of the VANET nodes. Adaptive decisions should be taken according to the current conditions of the VANET. Therefore, evaluation of transient measures is required for the characterization of VANETs. In the literature, different works address the characterization and measurement of the idle (or busy) time to be used in different proposals to attain a more efficient usage of wireless network. This paper focuses on the idle time of the link between two VANET nodes, which we denote as $T_{{idle}}$. Specifically, we have developed an analytical model based on a straightforward Markov reward chain to obtain transient measurements of $T_{{idle}}$. Numerical results from the analytical model fit well with simulation results. [ABSTRACT FROM PUBLISHER]

In this paper, we study the power allocation problem in a multiuser cooperative network, where users help each other's transmissions to an access point. We use iterative double auction (IDA) game to model the interaction among the users and the access point. In each iteration of this game, the users first submit bids for buying other users' power and asks for selling its own power, and then, the access point determines the power allocation based on users' bids and asks. We propose a distributed algorithm for the implementation of the IDA-based power allocation. We also show that the proposed algorithm achieves a weighted sum signal-to-noise ratio (sum-SNR)-optimal solution. Simulation results are conducted to verify the performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a cellular/infostation integrated network that supports on-demand data service delivery over a wireless link to users in a high-speed train. For the requests with different lifetimes and prices that they are willing to pay, we develop the optimal packet schedule that aims at earning the maximum revenue. To this end, we formulate the problem as an integer linear program (ILP). The problem cannot be solved efficiently by existing ILP solvers with guaranteed polynomial-time complexity in the worst case. By exploring the special structure of the formulated ILP, we propose a novel Checker algorithm and prove that this algorithm is guaranteed to find the offline optimal schedule in polynomial time. Based on the relevant insights, we further develop a class of online Checker algorithms that require only causal knowledge of service demands and wireless channel capacities. It is established that these online algorithms have a worst-case competitive ratio of 1/2, i.e., total revenue earned by them is at least half as much as the revenue earned by an offline optimal schedule that knows the complete a priori knowledge of future requests and channel conditions. Simulation results are provided to demonstrate the merit of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

It is known that in addition to spectrum sparsity, spatial sparsity can also be used to further enhance spectral utilization in cognitive radio systems. To achieve that, secondary users (SUs) must know the locations and signal strength distributions (SSDs) of primary users' base stations (PUBSs). Recently, a group sparse total least squares method was developed to cooperatively sense the PUBSs' signal strength and estimate their locations. It approximates PUBSs' power decay with a path loss model (PLM), assumes PUBSs' locations on some grid points, and then accomplishes the estimation tasks. However, the parameters of the PLM have to be known in advance, and the accuracy of the location estimation is bounded by the resolution of the grid points, which limit its practical applications. In this paper, we propose a sparse Bayesian learning method to solve the problems. We use a Laplacian function to model the power decay of a PUBS and then derive learning rules to estimate corresponding parameters. The distinct features of the proposed method are that most parameters are adaptively estimated, and little prior information is needed. To further enhance the performance, we incorporate source number detection methods in the proposed algorithm such that the number of the PUBSs can be precisely detected, facilitating the estimation of PUBSs' locations and SSDs. Moreover, the proposed algorithm is modified into a recursive mode to adapt to SUs' mobility and time-variant observations. Simulations show that the proposed algorithm has good performance, even when the spatial measurement rate is low. [ABSTRACT FROM PUBLISHER]

Network coding achieves multicast network capacity by allowing intermediate nodes to mix information in packets. However, due to the mixing operation, network coding is vulnerable to pollution attacks. Different from conventional cryptographic solutions, watchdog-based solutions, which have been recently proposed for network coding, rely on trusted nodes to monitor and verify behaviors of transmission nodes. However, the impact of lossy wireless communications has not been considered. Since false alarm and misdetection depend on the loss rate, these false results can happen with high probability when packet loss happens. In this paper, we propose VANDER, which is a novel cooperative watchdog scheme in heterogeneous wireless networks, where multiple watchdogs collaborate and efficiently detect pollution attacks in a lossy wireless environment. The novelty of our approach is that, when lossy overhearing happens, watchdogs work cooperatively to share the packet information, where no extra overhead is introduced to normal transmission nodes, and rather than retransmitting all lost packets among watchdogs, watchdogs use randomly generated Vandermonde hashes to detect corrupted packets. Moreover, VANDER is capable of detecting successive colluded adversaries. In addition to the low false alarm and misdetection probabilities, VANDER also achieves low computational complexity and communication overhead. Numerical experiments are provided to support the theoretical analysis of VANDER. [ABSTRACT FROM PUBLISHER]

We consider the uplink transmission in multiuser wireless systems with multiple single-antenna transmitting users and a multiantenna receiver. We address two problems in this paper. The first problem is user admission, i.e., given a large number of users, how to admit the maximum number of users that can simultaneously satisfy quality of service (QoS) and power constraints. The second problem is how to distribute the data among the users when they are allowed to share data before transmission. The aim is to minimize the total data exchange cost. Such a problem is called user clustering. We formulate those problems into sparsity-maximization problems, which are NP-hard. Inspired by compressive sensing techniques, we propose a common framework to tackle those problems by first applying the \ell1 -norm relaxation and then solving them with convex optimization methods. Simulations show that the proposed algorithms achieve excellent performance. For user admission, the numbers of admitted users by the proposed algorithms are close to the optimum numbers of admitted users obtained by exhaustive search (ES) . For user clustering, the total data exchange cost is reduced by more than 10% after only a few iterations. When the QoS requirement is low, the user data exchange can be avoided using the proposed method, which achieves the optimum result obtained by ES. [ABSTRACT FROM PUBLISHER]

Wireless communication in vehicular networks is highly vulnerable to electromagnetic interference, which seriously undermines the communication effectiveness. Thus, interference estimation and elimination are essential in vehicular communications. To solve the multitasking problem of interference estimation and cancelation within the transformation domain of vehicular communications where they are divided into the consistency and inconsistency based on the different conditions of spectrum sensing, the present study proposes an iterative Bayesian sparse learning approach for simultaneous interference recovery and elimination which applies multiple Bayesian learning for measured signals, and the structured sparse learning method for simultaneous interference mitigation with signal recovery. Furthermore, the updating optimization based on Bayesian estimation framework is performed to facilitate the learning process and improve the performance of interference estimation and signal reconstruction. Simulation results show that the proposed algorithms achieved more rapid convergence and more accurate recovery (31.84% and 27.88% higher in spectrum sensing with consistency and inconsistency, respectively) than conventional algorithms. During all iterations, the proposed algorithms, because of updating optimization, were more robust than conventional ones, but the computational complexity remained largely the same. To explore the correlation between the parameters and the interference recovery performance, we performed experiments, which revealed linear, logarithmic, and exponential regression correlations for particular forms of interference. We also compared the demodulation performance of the proposed algorithm with existing methods, and it turned out the proposed algorithm also obtained higher gains on average in the bit error rate. [ABSTRACT FROM AUTHOR]

This paper investigates simultaneous wireless information and power transfer (SWIPT) for a multiple-input–single-output (MISO) secrecy channel. First, transmit beamforming without artificial noise (AN) is designed to achieve the secrecy rate maximization problem subject to the transmit power and the energy harvesting (EH) constraints. This problem is not convex, but this can be solved by applying a bisection search to a sequence of the associated power minimization problems, each of which can be solved by using a novel relaxation approach. Moreover, we extend our proposed algorithms to the robust case by incorporating channel uncertainties. Then, transmit beamforming with AN is investigated for the secrecy rate maximization problem, where two relaxation approaches are presented, i.e., a two-level optimization algorithm and a successive convex approximation (SCA), to solve the secrecy rate maximization problem. In addition, tightness analyses of rank relaxation are provided to show that the optimal transmit covariance matrix exactly returns rank one. Simulation results are provided to validate the performance of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

Vehicular ad hoc networks (VANETs) present a multilevel feature when vehicles move in multilevel scenarios, such as viaducts, tunnels, and ramps. Different from city and highway scenarios, complicated node distribution and transmission conditions make the routing decision more challenging in the multilevel environment. To address the issue, this paper investigates geographic routing protocols for the multilevel scenario of VANETs. We first reveal the impacts of the multilevel feature on network characteristics by an outdoor transmission experiment and a stochastic analysis. In particular, the measured data and analysis results show that the wireless transmission range dramatically degrades, which deteriorates the connectivity probability and the performance of geographic routing protocols in the network. Motivated by these results, we propose a multilevel-scenario-oriented greedy opportunity routing protocol (M-GOR). In the protocol, we present a calculation method for the connectivity probability and a greedy opportunity forwarding (GOF) algorithm to respond to the impacts of the multilevel structure. Both the analysis and simulation results demonstrate benefits of M-GOR in terms of the average hop count and delivery ratio. [ABSTRACT FROM PUBLISHER]

In this paper, we study energy-efficient power allocation in distributed antenna systems (DASs). Unlike previous works that assumed perfect channel-state information (CSI) at the transmitter, we investigate a more practical case, where only the slowly varying large-scale CSI is known, to reduce the system overhead for CSI acquisition. Under a generalized DAS model, the power allocation problem for energy-efficiency (EE) maximization is formulated as a nonconvex fractional programming problem. Using the random matrix theory, the nonlinear fractional programming theory, and the saddle-point theory, we propose an iterative algorithm that achieves the global optimal solution. We also show that deploying the antennas in a distributed manner may enlarge the region where the optimal EE and the optimal spectral efficiency (SE) can be simultaneously achieved. Accordingly, we suggest the DAS-like way to densify the future 5G and beyond cellular networks to achieve EE and SE simultaneously. [ABSTRACT FROM PUBLISHER]

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme. [ABSTRACT FROM PUBLISHER]

In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

Estimation of signal-to-noise ratio (SNR) plays an important role in many fields of communications. This paper addresses the SNR estimation of multiple phase-shift keying (MPSK) signals in Nakagami- $m$ fading channels. A family of SNR estimators with closed-form expressions is developed based on the relationship among different orders of moments. Compared with the existing algorithms, the estimator, which exploits the minimum order of moments, exhibits the best performance in general. We also propose a joint estimation algorithm for SNR and the fading factor when it is unknown. Simulation results verify the feasibility and superiority of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

The increasing densification of telecommunications mobile networks raises the compelling problem of exposure to electromagnetic fields (EMF). In this paper, we address the particular concept of heterogeneous networks, where small cells (SCs) are deployed to build up a second layer to the existing macrocell network. SC deployment is shown to be particularly effective in reducing EMF exposure, in addition to capacity enhancement. Previous studies on EMF have focused on the uplink (UL) and downlink (DL) components separately, although they are strongly correlated. Relying on the definition of the exposure index (EI) from the European FP7 Lexnet project, we evaluate the impact on EMF exposure due to SC deployment. Guidelines for EMF-efficient deployment of SCs are first presented and evaluated. Then, a new enhanced intercell interference coordination (eICIC) algorithm using UL and DL metrics to optimize capacity and exposure to EMF is proposed. Static and self-optimizing dynamic implementations of the solution are considered, using a utility function related to the sojourn time of the users. Time-domain and frequency-domain self-optimized eICIC are compared in terms of EMF exposure reduction and capacity enhancement. The eICIC algorithms are combined with a self-optimized coverage range expansion function leading to further reduction of the EI. The proposed solution for optimizing both capacity and exposure to EMF is compared with a capacity-driven solution based on the DL metrics only. Simulation results illustrate the performance gains obtained for the different SC deployment scenarios. [ABSTRACT FROM PUBLISHER]

Software-defined networking (SDN) and network function virtualization (NFV) are a promising system architecture and control mechanism for future networks. Although some works have been done on wireless SDN and NFV, recent advancements in device-to-device (D2D) communications are largely ignored in this novel framework. In this paper, we study the integration of D2D communication in the framework of SDN and NFV. An inherent challenge in supporting software-defined D2D is the imperfectness of network state information, including channel state information (CSI) and queuing state information, in virtual wireless (QSI) networks. To address this challenge, we formulate the resource sharing problem in this framework as a discrete stochastic optimization problem and develop discrete stochastic approximation algorithms to solve this problem. Such algorithms can reduce the computational complexity compared with exhaustive search while achieving satisfactory performance. Both the static wireless channel and time-varying channels are considered. Extensive simulations show that users can benefit from both wireless network virtualization and software-defined D2D communications, and our proposed scheme can achieve considerable performance gains in both system throughput and user utility under practical network settings. [ABSTRACT FROM PUBLISHER]

In this paper, we study the energy-efficient resource allocation problem for device-to-device (D2D) communication underlaying cellular networks, which aims to maximize the minimum weighted energy efficiency of D2D links while guaranteeing minimum data rates for cellular links. We first characterize the optimal power allocation of the cellular links to transform the original resource allocation problem into the joint subchannel and power allocation problem for D2D links. We then propose three resource allocation algorithms with different complexity levels, namely, dual-based, branch-and-bound (BnB), and relaxation-based rounding (RBR) algorithms. While the dual-based algorithm solves the problem by using the dual decomposition method, the BnB and RBR algorithms tackle the problem by employing the relaxation approach. We establish the strong performance guarantees for the proposed algorithms through theoretical analysis. Extensive numerical studies demonstrate that the proposed algorithms achieve superior performance and significantly outperform a conventional algorithm. [ABSTRACT FROM PUBLISHER]

One of the key challenges in wireless networking is allocating the available radio resources to maximize key service delivery parameters such as the aggregate throughput and the multimedia quality of experience (QoE). We propose a novel and effective scheduling policy that allocates resource blocks, such that interference power is shifted toward capacity-saturated users, while improving the throughput of unsaturated users. The highlight of this paper is that the proposed scheme can dramatically improve the performance of cells that have a high discrepancy in its signal-to-noise ratio (SNR) distribution, which is typical in urban areas. The results show that a free-lunch (FL) solution is possible, whereby for negligible performance degradation in the saturated users, a large improvement in the nonsaturated users can be obtained. However, on average, the number of FL user pairings is low. By relaxing the degradation constraints, the non-FL (NFL) solution can yield a greater multiuser throughput gain. Motivated by a surge in mobile multimedia traffic, we further demonstrate that the proposed scheduling may have a profound impact on both energy efficiency and QoE of multimedia service delivery. [ABSTRACT FROM PUBLISHER]

In cognitive networks, primary and secondary users (PUs and SUs) share the radio resource according to fair regulations. The goal of this paper is to introduce a new method to detect an unfair user (freeloader) that hides its radio signal under the noise floor in the presence of an active (either primary or secondary) user. The performance of the proposed technique is evaluated by theoretical analysis and computer simulations. The probability of detection is expressed, for constant false-alarm rates, as a function of both the active signal power and the background noise variance. The results show the effectiveness of the new monitoring algorithm in several operating cases. [ABSTRACT FROM PUBLISHER]

High-speed railways (HSRs) have been widely deployed all over the world in recent years. Different from traditional cellular communications, the high mobility of HSR communication makes it essential to implement power allocation (PA) along time. In the HSR case, the transmission rate greatly depends on the distance between the base station (BS) and the train. As a result, the train receives a time-varying data rate service when passing by a BS. It is clear that the most efficient PA will spend all the power when the train is nearest the BS, which will cause great unfairness along time. On the other hand, channel inversion allocation achieves the best fairness in terms of constant rate transmission. However, its power efficiency is much lower. Therefore, power efficiency and fairness along time are two incompatible objects. For the HSR cellular system considered in this paper, a tradeoff between the two is achieved by proposing a temporal proportional-fair PA scheme. In addition, a near-optimal closed-form solution and one algorithm finding \epsilon-optimal allocation are presented. [ABSTRACT FROM PUBLISHER]

The max-log list demapper has been widely employed in the implementations of a coded multiple-input–multiple-output (MIMO) receiver, where only a candidate list of signal vectors is examined in the likelihood-ratio calculation to reduce complexity. Traditionally, the candidate list is generated in the original-lattice domain, which, unfortunately, results in severe degradation in the performance of the demapper if the channel is in ill-condition. In this paper, two new lattice-reduction-aided max-log list demappers are proposed, i.e., one for an iterative receiver and the other for a noniterative receiver. With similar complexity, the proposed demappers provide significant gains over existing demappers, particularly for the cases with a small list size and/or under a spatially correlated channel, due to the new algorithms for the generation of the candidate list. In addition, for the iterative receiver, the prior information coming out of the decoder is exploited to lower the complexity of the demapper. [ABSTRACT FROM PUBLISHER]

We study power-allocation problems in wireless systems through a network utility maximization framework. In this framework, the type of utility function represents the characteristics of each service. Hence, to deal with multiclass services in the system, it is important to accommodate various types of utility functions. In this paper, we consider four types of utility functions that can represent most of the services in wireless networks. We first develop a simple algorithm that solves a general optimization problem with those four types of utility function. We show that, even though the proposed algorithm might not provide the optimal solution, it provides an asymptotically optimal solution, which could be a good approximation to the optimal solution. The proposed algorithm can be applied to various resource-allocation problems in wireless systems. Among them, in this paper, we show that the proposed algorithm can be applied to both uplink and downlink power-allocation problems. [ABSTRACT FROM AUTHOR]

Maximum-likelihood (ML) detection for higher order multiple-input-multiple-output (MIMO) systems faces a major challenge in computational complexity. This limits the practicality of these systems from an implementation point of view, particularly for mobile battery-operated devices. In this paper, we propose a modified approach for MIMO detection, which takes advantage of the quadratic-amplitude modulation (QAM) constellation structure to accelerate the detection procedure. This approach achieves low-power operation by extending the minimum number of paths and reducing the number of required computations for each path extension, which results in an order-of-magnitude reduction in computations in comparison with existing algorithms. This paper also describes the very-large-scale integration (VLSI) design of the low-power path metric computation unit. The approach is applied to a 4 x 4, 64-QAM MIMO detector system. Results show negligible performance degradation compared with conventional algorithms while reducing the complexity by more than 50%. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach for computing the number, location, and transmission powers of wireless base stations in a 2-D urban setup. The new approach utilizes 2-D convolution to extract the supply-demand correlation. Available efficient methods for computing the convolution are then used to substantially reduce the complexity of the solution. The proposed approach enables network designers to choose arbitrary antenna propagation and radio demand patterns using a simple color-coding mechanism. Simulations of the proposed algorithm show its efficiency and flexibility in solving the placement problem. In this paper, we consider only the coverage planning. Consequently, the technique in this paper is useful in networks that operate with time-division multiple-access technology. However, the work can be extended to consider simultaneous coverage and frequency planning. [ABSTRACT FROM AUTHOR]

This paper proposes a zone-based approach referred to as zone-based GrEEn (ZBGrEEn) algorithm, for energy-efficient spectrum sharing (SS) networks. The SS network is considered to encompass device-to-device (D2D) transmissions between a single primary transmitter (PT) and multiple primary receivers (PRs), placed in distinctive zones around the PT. Power allocation is performed optimally so that the target rate of each application demanded by the PRs are adequately met. When the PT fails to meet the target rates over ${\rm{PT}} \to {\rm{PR}}$ D2D links, it transmits cooperatively through an appropriate secondary transmitter (ST) in the SS network, which acts as an amplify-and-forward (AF) relay. The ST's basis for selection is the ${\rm{PT}} \to {\rm{ST}}$ channel state and power level at the ST. Use of game theory for solving the energy efficiency (EE) maximization problem has also been quantified. ZBGrEEn introduces zone factor for determining the transmission time for each zone in the SS network. This time determines the energy consumption at the user terminals, thereby impacting its battery lifetime. The efficiency of the proposed scheme has been validated through simulation results, with the enhancement in battery lifetime of up to 46.28% achievable for the PT. Game theory is also observed to be rewarding in battery lifetime augmentation, especially for ultradense networks (UDNs). Performance of other user terminals in the network can be similarly evaluated. [ABSTRACT FROM AUTHOR]

In wireless location, nonline-of-sight (NLOS) errors are a major error source. To improve positional performance, these errors should be identified and mitigated before measurements are used in position computations. A novel NLOS mitigation algorithm is proposed in this paper. Employing system redundancy, the algorithm derives from the distribution function of hyperbolic intersections, which is an intermediate mobile location. Cost functions based on the intermediate position are then created for each time-of-arrival measurement. NLOS errors are detected and mitigated via hypothesis tests performed on the cost functions. Simulation tests are presented in this paper, and the results verify the effectiveness of this NLOS-error-mitigation method. This paper also demonstrates that algorithm performance depends closely on receiver noise. Reducing receiver noise or providing the intermediate mobile location via external aiding can improve performance. [ABSTRACT FROM AUTHOR]

In emergency communications, guaranteeing ultrareliable and low-latency communication is challenging yet crucial to save human lives and to coordinate the operations of first responders. To address this problem, we introduce a general approach for channel selection in mission-critical communications, i.e., choose channels with the best quality timely and accurately via channel probing. Since the channel conditions are dynamic and initially unknown to wireless users, choosing channels with the best conditions is nontrivial. Thus, we adopt online learning methods to let users probe channels and predict the channel conditions by a restricted time interval of observation. We formulate this problem as an emerging branch of the classic multiarmed bandit (MAB) problem, namely thepure-exploration banditproblem, to achieve a tradeoff between sampling time/resource budget and the channel selection accuracy (i.e., the probability of selecting optimal channels). The goal of the learning process is to choose the “optimal subset” of channels after a limited time period of channel probing. We propose and evaluate one learning policy for the single-user case and three learning policies for the distributed multiuser cases. We take communication costs and interference costs into account, and analyze the tradeoff between these costs and the accuracy of channel selection. Extensive simulations are conducted and the results show that the proposed algorithms can achieve considerably higher channel selection accuracy than previous exploration bandit approaches and classic MAB methods. [ABSTRACT FROM AUTHOR]

Wireless systems have evolved into supporting non- real-time data services. However, as the data rates for the services increase, the number of base stations per service area needs to expand, which leads to a high degree of base-station density. On the other hand, the so-called best effort resource management has been extensively studied for nonreal-time data services. One example of such efforts can be found in packet scheduling. The purpose of this paper is to investigate the economic impact of the down-link scheduling in terms of the base-station density. To this end, equations that can calculate the base-station density for the given quality of service (QoS) requirements such as outage probabilities, average data rates, and target Eb /Io were derived. Under specific QoS requirements on Rayleigh-fading channel environments, it is found that the one-by-one downlink scheduling (round-robin) requires about 50%-60% of the base stations required by the conventional simultaneous transmission. It was also found that this impact is almost the same as simply doubling the spectrum bandwidth for the simultaneous transmission. The base-station density is directly related to the infrastructure cost and thus may be translated into service prices. Therefore, the analysis in this paper provides a new insight into an economic aspect; how much the service prices for downloading the nonreal-time data would decrease with the introduction of packet scheduling in the code division multiple access downlink. [ABSTRACT FROM AUTHOR]

Tactical communications networks are multihop wireless networks in which switches and endpoints are mobile nodes. In a tactical environment, system performance degrades when switching nodes and/or communication links fail to operate. Fast algorithms for performance analysis are desirable for optimizing the network in a timely fashion. Further, tactical networks commonly use preemptive priorities to achieve low blocking probabilities for high-priority calls when the loss of equipment in the battlefield is not trivial. This paper discusses three measures and analytical algorithms for the performability evaluation of a two-tier tactical communications network where preemptive priority service discipline is employed and traffic is divided into multiple classes to provide multiple grades of service. Each class of traffic has its distinct characteristics, such as average call-arrival rate, average call-holding time, and service priority. The three performability measures are devised to evaluate the impact of nodes/links failures on system performance. Experiments show that the preemption does provide robust service for higher priority traffic. The techniques for performability evaluation presented in this paper may also be useful in other rapidly deployable networks, where mobility, communication efficiency, and computational complexity for adapting the network to unpredictable environments are of significant concern. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cross-layer resource optimization for wireless relay networks with dynamic node-selfishness information. The degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES) of the relay node (RN) are defined as the effects of its own residual energy resource and the incentive in an incentive mechanism, respectively. A model of two-timescale node-selfishness dynamics is constructed to describe the time-varying characteristics of the RN's DeIS and DeES. Based on the two-timescale node-selfishness dynamics of the RNs, a two-timescale resource-optimization dynamical algorithm (TRODA) is employed to control the transmit powers of both RNs and end users (EU), as well as the flow rates of the data packets injected by all EUs. Meanwhile, the stability and the tracking error of the TRODA under the two-timescale node-selfishness dynamics are analyzed by Lyapunov theory. Our simulation results demonstrate that the two-timescale optimization algorithm is stable and has trivial tracking error. [ABSTRACT FROM AUTHOR]

The ability of mobile devices to be connected to more than one radio node at the same time enables mobile devices to transmit and receive traffic to and from multiple paths. This ability helps to increase the average mobile device data rate and to improve the network reliability. Load balancing among multiple paths become a key factor to avoid network congestion, nevertheless it requires efficient techniques to split traffic without adding more delay or generating too much packet reordering for delay-sensitive traffic. In this paper, we address two key issues in the context of uplink wireless mobile networks: 1) how to accurately split traffic among multiple paths and 2) how to minimize the end-to-end delay without increasing packet reordering. We propose delay-aware load balancing algorithm (DALBA), a novel strategy that splits traffic at the granularity of the packet. DALBA aims to minimize the splitting error (SE) and the end-to-end delay difference by effectively using all of the available paths. We analyze DALBA's performance through extensive simulations using H.264 video traffic. Numerical results demonstrate that DALBA outperforms previous algorithms in terms of SE, end-to-end delay and peak signal-to-noise ratio while keeping packet reordering to a suitable low value. [ABSTRACT FROM AUTHOR]

A novel probabilistic radio resource allocation algorithm is proposed for code-division multiple-access cognitive radio networks. Our method incorporates a sensing-based hybrid access- and interference-limited spectrum sharing strategy to maximize the achievable spectral efficiency of cognitive users (CUs) while guaranteeing the quality-of-service (QoS) requirements of licensed services. Specifically, the enhanced performance for CUs is achieved through optimal joint power and rate adaptation to the time-varying multiuser interference and frequency-selective fading channel conditions. We show that this scheme can improve the average spectral efficiency of CUs, while satisfying the peak and average received-interference constraints at the primary receiver. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter–receiver pairs (Tx–RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX–RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX–RX-pair problem. For a generic N>\3 TX–RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX–RX pairs and then leveraging the power-allocation results obtained for the two- and three-TX–RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX–RX-pair problem and a distributed algorithm for N>\2 TX–RX pairs. [ABSTRACT FROM PUBLISHER]

Broadcasting is a key function in mobile ad hoc networks. Topology-dependent scheduling algorithms depend on the detailed network connectivity information and cannot adapt to dynamic topological changes in mobile networks. To overcome this limitation, topology-transparent broadcast scheduling algorithms have been proposed. Due to the large overhead of implementing the acknowledgement mechanism in broadcast communications and to guarantee that there is at least one collision-free transmission in each frame, most existing topology-transparent broadcast scheduling algorithms require a node to transmit the same packet repeatedly at multiple slots during one frame. This is very inefficient. In this paper, we propose an efficient topology-transparent broadcast scheduling algorithm. First, instead of transmitting the same packet repeatedly during one frame, each node collects transmission codes of its two-hop neighbors and transmits several different packets during one frame. Second, noting that many unassigned slots are not utilized, we propose several methods to utilize the unassigned slots efficiently in a collision-free and traffic-adaptive manner. Thus, our algorithm provides a guaranteed throughput and achieves a much better average throughput. The analytical and simulation results show that our proposed algorithm outperforms other existing topology-transparent broadcast algorithms dramatically. [ABSTRACT FROM AUTHOR]

Crowdedness spot is a crowded area with an abnormal number of objects. Detecting the crowdedness spots of moving vehicles in an urban area is essential to many applications. An intuitive method is to cluster the objects in areas to get the density information. Unfortunately, the data capturing vehicle mobility possesses some new features, such as highly mobile environments, supremely limited size of sample objects, and nonuniform biased samples, and all these features have raised new challenges that make traditional density-based clustering algorithms fail to retrieve the real clustering property of objects, making the results less meaningful. In this paper, we propose a novel nondensity-based approach called mobility-based clustering. The key idea is that sample objects are employed as “sensors” to perceive the vehicle crowdedness in nearby areas using their instant mobility rather than the “object representatives.” As such, the mobility of samples is naturally incorporated. Several key factors beyond the vehicle crowdedness have been identified, and techniques to compensate these effects are accordingly proposed. Furthermore, taking the detected crowdedness spots as a label of the taxi, we can identify one particular taxi to be a crowdedness taxi that crosses a number of different crowdedness spots. We evaluate the performance of our methods and baseline approaches based on real traffic situations (to retrieve the real traffic crowdedness) and real-life data sets. Finally, the interesting findings are provided for further discussions. [ABSTRACT FROM PUBLISHER]

Due to the tradeoff between capacity and fairness in a wide variety of networks, increasing one objective may deteriorate the other. This paper investigates the possibility of improving capacity and fairness at the same time, specifically in two-hop cellular networks. First, we prove that an achievable capacity region can be enlarged by using relay, i.e., the capacity and fairness tradeoff relationship is alleviated. To achieve the Pareto-efficient boundary of this enlarged capacity region, an appropriate scheduling algorithm should be developed. Thus, second, we propose a generalized opportunistic scheduling algorithm for relaying mode that can improve both capacity and fairness. Furthermore, we propose a heuristic algorithm with low complexity and signaling overhead for relaying mode. Extensive simulations under various configurations demonstrate that the proposed algorithm not only increases the throughput of each user but also effectively alleviates unfairness among users. [ABSTRACT FROM PUBLISHER]

Location estimation and tracking for mobile stations have attracted a significant amount of attention in recent years. Different types of signal sources are considered available to provide measurement inputs for location estimation and tracking in heterogeneous wireless networks. Various techniques have been studied and combined for location tracking, e.g., the least square methods for location estimation associated with the Kalman filters for location tracking. In this paper, the hybrid unified Kalman tracking (HUKT) technique is proposed to provide an integrated algorithm for precise location tracking based on both time of arrival (TOA) and time difference of arrival (TDOA) measurements. A new variable is incorporated as an additional state within the Kalman filtering formulation to consider the nonlinear behavior in the measurement update process. The relationship between this new variable and the desired location estimate is applied in the state update process of the Kalman filter. Three different designs of hybrid factor are proposed to adaptively adjust the weighting value between the TOA and TDOA measurements. Moreover, similar concepts are also utilized in the design of unified Kalman tracking schemes for pure TOA and TDOA measurement inputs in this paper. Compared with existing schemes, numerical results illustrate that the proposed HUKT algorithm can achieve enhanced accuracy for mobile location tracking, particularly under environments with an insufficient number of measurements in one of the signal paths. [ABSTRACT FROM PUBLISHER]

This paper considers the transmission of scheduling information in orthogonal frequency-division multiple-access (OFDMA)-based cellular communication systems such as the Third-Generation Partnership Project (3GPP) long-term evolution (LTE). These systems provide efficient usage of radio resources by allowing users to be dynamically scheduled in both frequency and time. This requires considerable amounts of scheduling information to be sent to the users. This paper compares two basic transmission strategies: transmitting a separate scheduling message to each user versus broadcasting a joint scheduling message to all users. Different scheduling granularities are considered, as are different scheduling algorithms. The schemes are evaluated in the context of the LTE downlink using multiuser system simulations, assuming a full-buffer situation. The results show that separate transmission of the scheduling information requires a slightly lower overhead than joint broadcasting when proportional fair scheduling is employed and the users are spread out over the cell area. The results also indicate that the scheduling granularity standardized for LTE provides a good tradeoff between scheduling granularity and overhead. [ABSTRACT FROM PUBLISHER]

The IEEE 802.16j standard has been developed to provide performance enhancement to the existing IEEE 802.16e network by incorporating the multihop relay (MR) technology. However, frequent handoffs and low spectrum-utilization issues that were not encountered in IEEE 802.16e may be incurred in IEEE 802.16j. The relay station (RS) grouping is one optional mechanism in the IEEE 802.16j MR standard to overcome these problems. The concept of RS grouping is to group neighboring RSs together to form an RS group, which can be regarded as a logical RS with larger coverage. In this paper, we investigate RS grouping performance enhancement in terms of throughput and handoff frequency. This paper designs an RS grouping algorithm to minimize handoffs by utilizing a greedy grouping policy: RS pairs with higher handoff rates will have higher priority for selection. The simulation results show that the handoff frequency of the considered MR network can significantly be reduced, and suitable RS grouping patterns can be derived using our grouping algorithm. In addition, we propose two centralized scheduling policies, i.e., the throughput-first (TF) policy to maximize the system throughput and the delay-first (DF) policy to minimize the average packet delay. By integrating our RS grouping algorithm and centralized scheduling algorithms, the simulation results indicate that, for the case of fixed users, groupings with smaller group sizes can result in better throughput performance. However, when user mobility is considered, the throughput value increases as the group size increases. Furthermore, we also show that the DF policy can both minimize the average packet delay and provide the fairness property among users with different traffic loads. [ABSTRACT FROM AUTHOR]

Most existing scheduling algorithms for multimedia traffic incode-division multiple-access (CDMA) networks have been designed under the assumption that the physical layer operates with a bounded bit error rate (BER) such that the packet error rate (PER) in the link layer is also bounded. However, there is a tradeoff between the PER and the packet drop rate (PDR) caused by delay violations, i.e., by slightly degrading the PER, more packets can be sent, thus lowering the PDR. In this paper, we present a new cross-layer enhancement to optimize the cross-layer qualityof-service (QoS) parameter - the packet loss rate (PLR) - that accounts for both the PER and the PDR. Furthermore, we study the fairness issue in terms of the PLR experienced by different users. Then, we propose a novel algorithm to schedule uplink multimedia traffic in time-division multicode CDMA networks, which optimizes the PLR experienced by each user in each frame, subject to fair distribution of packet losses between users according to the proposed cross-layer enhancement and fairness constraint. Simulation results show that the proposed algorithm provides fairness in packet losses among users of different traffic classes and achieves superior performance over two previous algorithms in terms of increasing the system capacity and reducing the PLR and average delay because of the cross-layer enhancement. [ABSTRACT FROM AUTHOR]

Contour line estimation in a monitored physical phenomenon using wireless sensor networks (WSNs) with cross-layer considerations is investigated in this paper. Herein, we present a statistical approach to locate contour points for contour line construction in the presence of irregular sensor deployment with the assistant of Delaunay triangular meshes. The proposed contour-estimation algorithm has certain error-correction capability, and it is robust to the data degradation coming from sensing and/or communication noise. To ensure the success of the proposed algorithm, we further analyze three sources of signal distortion: sensing noise, data quantization error, and data communication noise. We find that choosing a proper data quantization level can balance the communication cost and the desired signal quality. Meanwhile, the impact of channel impairment can be mitigated by the appropriate data-fusion mechanism. In this paper, an adaptive data-fusion mechanism is proposed to avoid excessive packet retransmissions. The correctness of the proposed approach is verified through simulation, and the effects of different system parameters on the overall system performance are also given to show the robustness of the proposed algorithm. This paper also offers some guidelines on the design of the WSN system. [ABSTRACT FROM AUTHOR]

This paper presents an analysis framework for estimating spatio-temporal distortion associated with data-gathering algorithms in sensor networks. We assume that the underlying data field being sensed is spatially and temporally correlated and that the sink node performs real-time reconstruction of the field. Unlike existing work, the proposed analysis framework assumes realistic transmission schemes and network/routing parameters, such as congestion and limited transmission range. Our simulations show that, even for a simple collision-free transmission scheme and regular deployment patterns, the distortion analyses yield starkly different results, compared with the analyses performed in the absence of these assumptions. The analytical method presented in this paper could easily be extended to consider the real-time data-reconstruction problem under different transmission schemes, node distributions, and reconstruction methods. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing (OFDM) can be used to support high-data-rate transmissions over time-dispersive fading channels. Many OFDM-based communication systems use packet-based transmission, where channel estimation is needed for the detection of information-carrying symbols. The finite impulse response (FIR) channel model is simple and effective for some simulations of channel estimation for the OFDM-based communication systems over the time-dispersive channels; yet, it is only an approximate channel model that cannot be used in the case of accurate channel estimation. Unfortunately, many researchers have overlooked this issue and have been devising channel-estimation algorithms squarely based on the FIR channel model. While the channel estimation results from these algorithms can be impressive for the FIR channels, the algorithms can hardly be applied in real-world applications. This paper explains in detail the reason the FIR channel model is only an approximate channel model for the OFDM-based communication systems, trying to discourage the inappropriate usage of this model, which can lead to fruitless efforts. This paper also presents a realistic channel model for OFDM-based communication systems, which can be used to realistically access the channel parameter-estimation algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel analytical model for analyzing the performance of handoff algorithms based on absolute and relative signal strength measurements. The performance is analyzed in multicell and multisector environments where cochannel interference (CCI) is present. The model presented in this paper accounts for the distinct features of intersector handoff compared with intercell handoff. Numerical results show that the performance metrics are affected by the CCI level. The occurrence condition of unnecessary handoffs in multisector systems is also presented. [ABSTRACT FROM AUTHOR]

This paper considers a scheduling problem for multi-user transmissions over the downlink of a time-slotted cellular data network. For such a network, opportunistic scheduling exploits the time-varying radio channel and improves network performance. This paper presents a new scheduling solution that maximizes the aggregate user performance subject to certain minimum and maximum performance constraints. By constraining the performance experienced by individual users, who share a common radio downlink, to some upper bounds, it is possible to provide the system operator with a better control of radio resource allocations and service differentiation among different classes of users. The proposed solution, which is referred to as Throughput Constrained Opportunistic Scheduling (TCOS), offers better service differentiation among different classes of users than the existing opportunistic scheduling algorithms with only minimum performance constraints. For ideal memoryless fading channels, TCOS provides performance comparable with an existing scheme with minimum and maximum performance constraints. For realistic fading channels with memory, TCOS significantly improves system performance due to its ability to trade off feasibility with throughput. Extensive simulation results demonstrate the improved system performance and effective service differentiation of the proposed solution under realistic channel conditions. [ABSTRACT FROM AUTHOR]

This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

This paper proposes a blind equalizer that pursues the maximization of a novel objective function. It consists of the ratio between the square of the mean signal power and the variance of the signal power. In this paper, important prepositions are first mathematically stated. The devised equalizer is asymptotically equivalent to the well-known constant-modulus algorithm (CMA) and the Kurtosis maximization algorithm (KMA) because they pursue the same "Ricianity" criterion already employed in the "Rayleigh-ness" test for spread-spectrum code acquisition. The performance of the addressed equalizers is then investigated by focusing on direct-sequence code-division multiple~access (DS/CDMA) communication environments in a downlink scenario. In particular, we have evidenced the effectiveness of the presented equalizer in the numerical examples in terms of robustness against the effect of interference, as well as in~terms of convergence rate, in spite of a small amount of extra processing. [ABSTRACT FROM AUTHOR]

Multicarrier code-division multiple access (CDMA) with time and frequency spreading has been recently considered as a candidate for fourth-generation (4G) wireless systems. This signaling scheme simultaneously utilizes code spreading in the time and frequency domains to simultaneously improve frequency diversity and minimize multiuser access interference. As a result, it is capable of outperforming multicarrier CDMA systems that employ 1-D spreading. In this paper, a novel adaptive subcarrier allocation algorithm is developed for multicarrier CDMA with time and frequency spreading to improve the overall bit error rate (BER) performance for all spreading configurations. This algorithm assigns users to subcarrier groups that provide favorable fading characteristics while simultaneously reducing the amount of interference caused to other users. The proposed algorithm is shown to provide a performance improvement, ranging from 1.5 dB with 2 × 16 (time × frequency) and spreading to 7 dB with 16 × 2 (time × frequency) spreading. The algorithm is also shown to maintain or improve the BER floor for each spreading configuration. It is concluded that at higher and lower levels of Eb/No, a higher frequency- and time-domain spreading should be, respectively, employed to improve BER performance. Furthermore, the Eb/No threshold level to switch between time and frequency spreading for the analyzed system is found to be 2.5 dB. [ABSTRACT FROM AUTHOR]

This paper introduces a space-time decision feedback equalizer whose coefficients are recursively renewed on the basis of the modified affine projection algorithm (APA). Despite its effectiveness against a rank-deficiency problem, the complexity of the conventional APA utilizing the sample matrix inversion is considerable. Furthermore, the shift invariance property could not be exploited in the case of a space-time decision-directed equalizer (STDE) application. To overcome such challenges, this paper introduces an APA employing a hyperplane projection scheme to reduce computational complexity without any degradation in its convergence characteristic and tracking capability. The performance of the proposed equalization methodology is verified by showing its superior bit error rate behaviors, fast convergence property, and reduced computational complexity by performing adaptive STDE under the single-input-multiple-output wireless multipath fading channel. [ABSTRACT FROM AUTHOR]

In this paper, we consider a time-division code- division multiple-access (CDMA) system operating in downlink mode in a multiuser multipath channel scenario. In order to implement RAKE reception, the delays, phases, and magnitudes of the different paths need to be estimated and the estimates updated. In the context of burst transmissions, delays do not vary during a burst, and only phases and magnitudes need to be tracked. In this paper, we investigate the issue of phase tracking over rapidly time-varying multipath channels. We propose a new version of the conventional phase loop that mitigates the interpath interference effect. The optimization is based on the concept of prefiltering. The analysis shows improved tracking performance in comparison with the standard phase recovery algorithm. [ABSTRACT FROM AUTHOR]