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Synchronization of bio-inspired pulse-coupled oscillators is receiving increased attention due to its wide applications in sensor networks and wireless communications. However, most existing results are obtained in the absence of malicious attacks. Given the distributed and unattended nature of wireless sensor networks, it is imperative to enhance the resilience of pulse based synchronization against malicious attacks. To achieve this goal, we propose a new pulse based interaction mechanism to improve the resilience of pulse based synchronization. We rigorously characterize the condition for mounting stealthy attacks under the proposed pulse based interaction mechanism and prove analytically that synchronization of legitimate oscillators can be achieved in the presence of multiple stealthy attackers even when the initial phases are unrestricted, i.e., randomly distributed in the entire oscillation period. This is in distinct difference from most existing attack-resilient synchronization algorithms (including the seminal paper from L. Lamport and P. M. Melliar-Smith, “Synchronizing Clocks in the Presence of Faults,” J. ACM, vol. 32, no. 1, pp. 52–78, 1985), which require a priori (almost) synchronization among legitimate nodes. Numerical simulations are given to confirm the theoretical results. [ABSTRACT FROM AUTHOR]

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]

As a response to the challenge of data traffic explosion in wireless networks, content caching in device-to-device (D2D) communication networks has emerged as a promising solution. However, in practical deployment, D2D content caching has its own problems. In particular, not all of the user devices are willing to share the content with others due to numerous concerns, such as security, battery life, and social relationship. In this paper, we consider the factor of social relationship in the deployment of D2D content caching. First, we apply stochastic geometry theory to derive an analytical expression of downloading performance for the D2D caching network. Specifically, a social relationship model with respect to the physical distance is adopted in our analysis to obtain the average download delay performance using random and deterministic caching strategies. Second, to achieve a better performance in more practical and specific scenarios, we develop a socially aware distributed caching strategy based on a decentralized learning automaton, to optimize the cache placement operation in D2D networks. Different from the existing caching schemes, the proposed algorithm not only considers the file request probability and the closeness of devices as measured by their physical distance but also takes into account the social relationship between D2D users. Our simulation results show that the proposed algorithm can converge quickly and outperforms the random and deterministic caching strategies. With these results, our work sheds insights on the design of D2D caching in the practical deployment of 5G networks. [ABSTRACT FROM PUBLISHER]

This paper studies a multiple-input multiple-output (MIMO) broadcast relay channel (BRC), in which a multiple-antenna base station (BS) communicates with multiple-antenna users through an infrastructure-based multiple-antenna relay station (RS). Applying dirty paper coding (DPC) at the BS and linear processing at the RS, our aim is to find the input covariance matrices and the RS beamforming matrix that maximize the system sum-rate. To solve this non-convex problem, a more tractable dual multiple access relay channel (MARC) is investigated and an alternating-minimization algorithm is proposed. Furthermore, the mapping from the resulting covariance matrices for the MARC to the covariance matrices for the BRC is derived. Unlike other existing single-antenna-user schemes, our solution is applicable to a more general network with any number of antennas at the users. Compared with two such single-antenna-user schemes, simulations show that the proposed scheme outperforms the all-pass relay design and performs similarly to the SVD-relay design. Moreover, the proposed design performs close to the sum-rate upper bound with the performance gap decreasing with increasing number of antennas per user. It is also observed that having more antennas at the RS than at the BS is desirable for better system performance. [ABSTRACT FROM PUBLISHER]

This paper investigates joint power control and routing policies for general multihop wireless networks when all the transmitting nodes are subject to a long-term average power constraint. The main contribution of this paper is to propose online power and rate control algorithms and prove that these policies stabilize the entire queuing network whenever the packet arrival rates at each node are in the corresponding region of achievable rates. The online policies are time varying and based on the queue size at each node and the instantaneous channel conditions. The theoretical results are supported by simulations for the illustrative cases of both a multiple-access channel and a relay channel. [ABSTRACT FROM AUTHOR]

Quantized Congestion Notification (QCN) has been developed for IEEE 802.1Qau to provide congestion control at the Ethernet Layer or Layer 2 in data center networks (DCNs) by the IEEE Data Center Bridging Task Group. One drawback of QCN is the rate unfairness of different flows when sharing one bottleneck link. In this paper, we propose an enhanced QCN congestion notification algorithm, called fair QCN (FQCN), to improve rate allocation fairness of multiple flows sharing one bottleneck link in DCNs. FQCN identifies congestion culprits through joint queue and per flow monitoring, feedbacks individual congestion information to each culprit through multi-casting, and ensures convergence to statistical fairness. We analyze the stability and fairness of FQCN via Lyapunov functions and evaluate the performance of FQCN through simulations in terms of the queue length stability, link throughput and rate allocations to traffic flows with different traffic dynamics under three network topologies. Simulation results confirm the rate allocation unfairness of QCN, and validate that FQCN maintains the queue length stability, successfully allocates the fair share rate to each traffic source sharing the link capacity, and enhances TCP throughput performance in the TCP Incast setting. [ABSTRACT FROM AUTHOR]

In this paper, a derandomized algorithm for sampling decoding is proposed to achieve near-optimal performance in lattice decoding. By setting a probability threshold to sample candidates, the whole sampling procedure becomes deterministic, which brings considerable performance improvement and complexity reduction over to the randomized sampling. Moreover, the upper bound on the sample size K, which corresponds to near-maximum likelihood (ML) performance, is derived. We also find that the proposed algorithm can be used as an efficient tool to implement soft-output decoding in multiple-input multiple-output (MIMO) systems. An upper bound of the sphere radius R in list sphere decoding (LSD) is derived. Based on it, we demonstrate that the derandomized sampling algorithm is capable of achieving near-maximum a posteriori (MAP) performance. Simulation results show that near-optimum performance can be achieved by a moderate size K in both lattice decoding and soft-output decoding. [ABSTRACT FROM AUTHOR]

In this paper, a novel low-complexity tree-search (TS) algorithm, which is referred to as a hierarchical tree search (HTS), is proposed for soft-output multiple-input–multiple-output (MIMO) detection to mitigate the performance loss caused by an early termination. The proposed HTS algorithm is realized by the following two components: the hierarchical set partitioning to find all hypotheses with reasonable quality as fast as possible and to fairly improve them and the new cost metric to determine the search order by considering the quality of the hypotheses found so far. Through simulation, it is shown that the performance–complexity tradeoff of the proposed HTS algorithm surpasses those of the existing algorithms in various MIMO configurations. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a systematic solution to the problem of scheduling delay-sensitive media data for transmission over time-varying wireless channels. We first formulate the dynamic scheduling problem as a Markov decision process that explicitly considers the users' heterogeneous multimedia data characteristics (e.g., delay deadlines, distortion impacts and dependences, and so on) and time-varying channel conditions, which are not simultaneously considered in state-of-the-art packet scheduling algorithms. This formulation allows us to perform foresighted decisions to schedule multiple data units for transmission at each time in order to optimize the long-term utilities of the multimedia applications. The heterogeneity of the media data enables us to express the transmission priorities between the different data units as a priority graph, which is a directed acyclic graph. This priority graph provides us with an elegant structure to decompose the multidata unit foresighted decision at each time into multiple single-data unit foresighted decisions which can be performed sequentially, from the high priority data units to the low priority data units, thereby significantly reducing the computation complexity. When the statistical knowledge of the multimedia data characteristics and channel conditions is unknown a priori, we develop a low-complexity online learning algorithm to update the value functions, which capture the impact of the current decision on the future utility. The simulation results show that the proposed solution significantly outperforms existing state-of-the-art scheduling solutions. [ABSTRACT FROM PUBLISHER]

Relay selection is crucial in improving the performance of wireless cooperative networks. Most previous works for relay selection use the current observed channel conditions to make the relay-selection decision for the subsequent frame. However, this memoryless channel assumption is often not realistic given the time-varying nature of some mobile environments. In this paper, we consider finite-state Markov channels in the relay-selection problem. Moreover, we also incorporate adaptive modulation and coding, as well as residual relay energy in the relay-selection process. The objectives of the proposed scheme are to increase spectral efficiency, mitigate error propagation, and maximize the network lifetime. The formulation of the proposed relay-selection scheme is based on recent advances in stochastic control algorithms. The obtained relay-selection policy has an indexability property that dramatically reduces the computation and implementation complexity. In addition, there is no need for a centralized control point in the network, and relays can freely join and leave from the set of potential relays. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we present an iterative joint channelestimation and data-detection technique for multiple-input-multiple-output (MIMO) code-division multiple-access (CDMA) systems over Rayleigh fading channels. The proposed receiver performs channel estimation and data detection using the expectation-maximization (EM) algorithm. We derive a closedform expression for the optimized weight coefficients of the EM algorithm, which is shown to provide large performance improvement relative to the conventional equal-weight EM-based signal decomposition. Our results show that the receiver can achieve near-optimum performance with modest complexity using very few training symbols. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios. [ABSTRACT FROM AUTHOR]

According to Bluetooth specifications, communications between slave devices within a piconet are controlled by its master node. In other words, a master node has to be involved in all packet exchange activities in its associated piconet. Consequently, how to determine the polling sequence and how to estimate service time for individual slave nodes are two critical intrapiconet missions to a master and are usually categorized as the intrapiconet polling and scheduling problem. Obviously, a good polling sequence can reduce the average packet delay, whereas accurate service time estimation would result in better throughput performance. Furthermore, the packet delay of intrapiconet transmission is mostly caused by the inadequate behavior in which the master node polls an idle slave since other nonidle slaves belonging to the same piconet have to wait for the completion of such POLL/NULL packet transmissions. Certainly, unnecessary pollings would cause bandwidth utilization reduction as well. Therefore, the adaptability to traffic pattern is a necessary condition to a good intrapiconet polling and scheduling algorithm. In this paper, we proposed a traffic-aware algorithm to deal with the polling and scheduling issue within a piconet. The objective of our work is to maximize the throughput performance and to reduce the average intrapiconet packet delay as simultaneously as possible. As expected, the simulation results verify that our work designs an intrapiconet scheduling scheme with low delay and high throughput, which is adaptive to various traffic scenarios as well. [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]

Recently, sensor network technologies are being applied to more places such as intelligent home, offices and universities. Localization algorithms are required for indoor wireless sensor network (WSN) applications because the identification of real positions can upgrade the importance of sensing information. In this paper, a sensor network, where small-sized, low-cost and low-rate sensors are uniformly deployed in an indoor WSN environment, is taken into account. We introduce efficient algorithms, termed multi- duolateration localization (MDL) and grouping multi- duolateration localization (GMDL), which can improve the accuracy of location identification by employing jumper setting. The MDL algorithm can estimate 2-dimensional coordinates with high accuracy by the acquisition of edge information from the setting. In addition, the GMDL algorithm can be applied to the estimation of 3-dimensional coordinates by the acquisition of edge and surface information from the setting. The proposed algorithms perform localization more accurately than trilateration and faster than multidimensional scaling (MDS). The results from a MATLAB simulation show the outperformance of the proposed algorithms and demonstrate the possibility of 3-dimensional localization. [ABSTRACT FROM AUTHOR]

In this paper, we consider the joint power allocation and base station (BS) assignment in nonorthogonal downlink transmission code-division multiple-access (CDMA) communication systems. In addition to the popular sum-power criterion, we introduce three novel criteria that better take into account the network requirements and particularly the separate power limit of each of the BSs. In our model, we allow each user to be connected to more than one BS and show that such a scheme can improve the system capacity (while currently this mechanism is only used to support soft handoff). Nevertheless, we show that in any optimal solution, the actual number of connections used by each user is small. We propose a unified approach to optimization, which allows the use of linear programming (LP)-based iterative algorithms to solve all of the presented optimization problems. The proposed scheme is verified by simulations for several scenarios. The simulation results show that by taking into account the power constraints of each of the BSs, the system capacity can be increased by up to 33%. The results can also approximate the performance of orthogonal CDMA networks with precision that increases with system bandwidth. [ABSTRACT FROM AUTHOR]

Network diversity multiple access protocols (NDMA) have been shown to considerably outperform previously proposed algorithms. However, several issues in their analysis remain unsolved, particularly under the assumption of imperfect collision-multiplicity detection in asymmetrical system configurations (i.e., configurations where the users have different channel and queuing statistics). To partially fill this gap, this paper presents a detailed study on stability, throughput and delay properties of asymmetrical nonblind NDMA protocols under such imperfect detection assumption. Additionally, the protocol formulation includes a carrier-sense mechanism which is found to improve system performance under finite-SNR environments and which can be useful in assessing the benefits of retransmission diversity on IEEE 802.11 wireless local area networks (WLANs). New expressions for the parameters and performance metrics of the NDMA protocol under different assumptions are here provided. These include the stability condition of a system without feedback to acknowledge correct packet reception and the derivation of the boundaries of the throughput region using a joint cross-layer optimization with respect to the probabilities of transmission and false alarm. Simulation results are used to further assess the performance of the protocol and to confirm the analytic results. [ABSTRACT FROM AUTHOR]

This paper deals with beamforming in wireless relay networks with perfect channel information at the relays, receiver, and transmitter if there is a direct link between the transmitter and receiver. It is assumed that every node in the network has its own power constraint. A two-step amplify-and-forward protocol is used, in which the transmitter and relays not only use match filters to form a beam at the receiver but also adaptively adjust their transmit powers according to the channel strength information. For networks with no direct link, an algorithm is proposed to analytically find the exact solution with linear (in network size) complexity. It is shown that the transmitter should always use its maximal power while the optimal power of a relay can take any value between zero and its maxima. Also, this value depends on the quality of all other channels in addition to the relay's own. Despite this coupling fact, distributive strategies are proposed in which, with the aid of a low-rate receiver broadcast, a relay needs only its own channel information to implement the optimal power control. Then, beamforming in networks with a direct link is considered. When the direct link exists during the first step only, the optimal power control is the same as that of networks with no direct link. For networks with a direct link during the second step only and both steps, recursive numerical algorithms are proposed. Simulation shows that network beamforming achieves the maximal diversity order and outperforms other existing schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new maximum-likelihood (ML) target localization approach which uses quantized sensor data as well as wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that statistics of imperfect wireless channels between sensors and the fusion center along with some physical layer design parameters are incorporated in the localization algorithm. We call this approach "channel-aware target localization." ML target location estimators are derived for different wireless channel models and receiver architectures. Furthermore, we derive the Cramér-Rao lower bounds (CRLBs) for our proposed channel-aware ML location estimators. Simulation results are presented to show that the performance of the channel-aware ML location estimators are quite close to their theoretical performance bounds even with relatively small number of sensors and their performance is superior compared to that of the channel-unaware ML estimators. [ABSTRACT FROM AUTHOR]

Finding the positions of nodes in an ad hoc wireless sensor network (WSN) with the use of the incomplete and noisy distance measurements between nodes as well as anchor position information is currently an important and challenging research topic. However, most WSN localization studies have considered that the anchor positions and the signal propagation speed are perfectly known which is not a valid assumption in the underwater and underground scenarios. In this paper, semi-definite programming (SDP) algorithms are devised for node localization in the presence of these uncertainties. The corresponding Cramér-Rao lower bound (CRLB) is also produced. Computer simulations are included to contrast the performance of the proposed algorithms with the conventional SDP method and CRLB. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimal strategy for the transmission of scalable video over packet-based multiple-input multiple-output (MIMO) systems. The scalable extension of H.264/AVC that provides a combined temporal, quality and spatial scalability is used. For given channel conditions, we develop a method for the estimation of the distortion of the received video and propose different error concealment schemes. We show the accuracy of our distortion estimation algorithm in comparison with simulated wireless video transmission with packet errors. In the proposed MIMO system, we employ orthogonal space-time block codes (O-STBC) that guarantee independent transmission of different symbols within the block code. In the proposed constrained bandwidth allocation framework, we use the estimated end-to-end decoder distortion to optimally select the application layer parameters, i.e., quantization parameter (QP) and group of pictures (GOP) size, and physical layer parameters, i.e., rate-compatible turbo (RCPT) code rate and symbol constellation. Results show the substantial performance gain by using different symbol constellations across the scalable layers as compared to a fixed constellation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate efficient wireless data access algorithms in mobile hotspots. We introduce a proxy cache (PC) and propose PC-based poll-each-read (P-PER) and PC-based callback (P-CB) data access algorithms to reduce the transmission cost over wireless links in mobile hotspots. An analytical model is developed, and extensive simulations are conducted to demonstrate the performance of P-PER and P-CB. It is shown that P-PER and P-CB can improve cache hit performance and significantly reduce transmission cost. A tradeoff between P-PER and P-CB suggests the need to use a hybrid proxy-based approach to attain optimal performance of wireless data access in mobile hotspots. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the sequential message-passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes. This decoding algorithm shows better bit error rate (BER) performance than the conventional message-passing decoding algorithm, especially for the small number of iterations. Analytical results indicate that as the number of partitioned subsets of check nodes increases, the BER performance is improved. We also derive the recursive equations for mean values of messages at check and variable nodes by using density evolution with a Gaussian approximation. From these equations, the mean values are obtained at each iteration of the sequential decoding algorithm and the corresponding BER values are calculated. They show that the sequential decoding algorithm converges faster than the conventional one. Finally, the analytical results are confirmed by the simulation results. [ABSTRACT FROM AUTHOR]

An efficient way to reduce the peak-to-average power ratio (PAPR) in OFDM systems is clipping. After the clipping in an MIMO-OFDM system, the additive noise may not be white. In this paper, we develop fast (single-symbol) ML decoding algorithms for orthogonal space-time block codes (OSTBC) and (linearly transformed) quasi orthogonal space- time block codes (QOSTBC) in clipped MIMO-OFDM systems by using a clipping noise model with Gaussian approximation. By using the statistics of the clipping distortions, our newly developed fast ML decoding algorithms improve the performance for clipped MIMO-OFDM systems with OSTBC and QOSTBC while the decoding complexities are not increased. Simulation results are presented to illustrate the improvement. [ABSTRACT FROM AUTHOR]

Mobile positioning has drawn significant attention in recent years. Nonline-of-sight (NLOS) propagation error is the dominant error source in mobile positioning. Most previous research in this area has focused on NLOS identification and mitigation. In this paper, we investigate new positioning algorithms to take advantage of the NLOS propagation paths rather than canceling them. Based on a prior information about the NLOS path, a geometrical approach is proposed to estimate mobile location by using two NLOS paths. On top of this, the least-squares (LS)-based position estimation algorithm is developed to take multiple NLOS paths into account, and its performance in terms of root mean-square error (RMSE) is analyzed. A general LS algorithm considering both LOS and NLOS paths is also derived, and the maximum likelihood-based algorithm is presented to jointly estimate the mobile's and scatterers' positions. The Cramer-Rao lower bound on the RMSE is derived for the benchmark of the performance comparison. The performance of the proposed algorithms is evaluated analytically and is done via computer simulations. Numerical results demonstrate that the derived analytical results closely match the simulated results. [ABSTRACT FROM AUTHOR]

In this paper, a new channel-estimation algorithm based on maximum-likelihood (ML) algorithm for estimation and tracking of the multiple-input-multiple-output (MIMO) channels is presented. The ML algorithm presents the optimum estimation when the exact channel model is known. The derived channel-estimation algorithm is very efficient, with a computational complexity comparable to the least mean square and much lower than the recursive least squares and the Kalman algorithms. The proposed algorithm is analyzed, and the effect of the channel-tracking error is applied as a modifying component for the derived algorithm. The proposed algorithm is simulated for half- and full-rank flat-fading time-varying MIMO channels for the different values of fDT, Eb/No, and training lengths via Monte Carlo simulation technique. The minimum mean-square-error (mmse) joint detector is considered as the detection algorithm. The output of the mmse receiver is considered as the virtual training data in the blind mode of operation: the same as in the decision-directed algorithm. By various simulations, the bit error rate and the mse of tracking the proposed algorithm for different values of fDT, presenting the speed of channel variations, are evaluated and compared with the Kalman filtering approach. By simulating the proposed algorithm for different values of the training length, the minimum training length required for different channel conditions is extracted. [ABSTRACT FROM AUTHOR]

This paper proposes task-scheduling algorithms designed to save energy in ad hoc networks by using a novel concept of "affinity" as the scheduling metric. This notion of affinity is used to represent the task-linking topology. For one-hop ad hoc networks, we define three types of affinity measures: node affinity, task affinity, and weighted affinity. Node affinity is defined as the weighted sum of incoming and outgoing task flows at each node, and task affinity as the sum of node affinities of source and destination nodes for each task. Based on these affinity measures, we consider three heuristic scheduling algorithms that are executable in real time: 1) affinity-first shortest task scheduling Qat; 2) task-size-first affinity-based scheduling Qta; and 3) task-size weighted-affinity scheduling Qwa. These algorithms have the advantage of making the total duration of the doze state of all the nodes in the network longer than in other conventional schemes, thereby achieving significant power saving (PS). Our simulation experiments suggest that the PS performance of Qwa is comparable to optimal scheduling (which is not implementable in practice for realistic link topologies). We also present a framework for a PS medium-access-control (MAC) protocol in which our scheduling algorithm can be deployed, and we describe how to enhance the existing 802.11 PS MAC to implement our framework. Simulation results demonstrate that our enhanced PS MAC protocol combined with affinity-based scheduling improves power efficiency and throughput significantly over other existing schemes. [ABSTRACT FROM AUTHOR]

It has been well recognized that significant throughput gains can be leveraged in multiuser wireless communication systems by exploiting multiuser diversity with a smart scheduler. This scheduler collects channel state information (CSI) from all users and allocates the resources to the user(s) experiencing favorable channel conditions. However, for a frequency-division-duplex system with a large number of users, how to efficiently collect the required CSI will be a challenging task, especially when the feedback links are of limited capacity. In this paper, we propose a scheduling algorithm to exploit multiuser diversity with possibly imperfect one-bit channel state feedback. The basic idea is to define a threshold λ and let each user report one-bit information to the scheduler about the comparison between its measured channel fading level and λ. Correspondingly, the scheduler uses these feedback bits to classify all users into two sets and assigns the channel to one user belonging to the set experiencing favorable channel conditions. Several implementation schemes are developed by attacking the optimization of λ under different system configurations, covering both the case when the one-bit feedback is perfect and those when the one-bit feedback is imperfect. Computer simulations show that when the user number is large, say, more than ten users, the proposed scheduling supports significantly larger data rate over the round-robin scheduling, while in comparison with the optimum scheduling with complete CSI, the performance loss is limited if the one-bit feedback is of high reliability. In addition, our studies show that we can effectively enhance the robustness against feedback imperfectness by incorporating the feedback reliability into optimization of λ. [ABSTRACT FROM AUTHOR]

It is known that conventional nulling-and-canceling (NC) detection for multiple-input/multiple-output (MIMO) systems cannot exploit all of the available diversity, and, thus, its performance is significantly inferior to that of maximum likelihood (ML) detection. Conventional NC employs the layerwise postequalization signal-to-noise ratios (SNRs) as reliability measures for layer sorting. These SNRs are average quantities that do not depend on the received vector. In this paper, we propose the novel dynamic nailing-and-canceling (DNC) technique that uses approximate a posteriori probabilities as measures of layer reliability. The DNC technique is a minimum mean-square error (MMSE) nulling scheme combined with an improved "dynamic" layer sorting rule that exploits the information contained in the current received vector. We calculate the error probability of DNC for a simple special case and show that it is upper bounded by the error probability of conventional NC. Simulation results are presented for spatial multiplexing systems and for systems using linear dispersion codes. It is demonstrated that the DNC technique can yield near-ML performance for a wide range of system sizes and channel SNRS at a fraction of the computational complexity of the sphere-decoding algorithm for ML detection. [ABSTRACT FROM AUTHOR]

Energy constraints have a significant impact on the design and operation of wireless sensor networks. This paper investigates the base station (BS) selection (or anycast) problem in wireless sensor networks. A wireless sensor network having multiple BSs (data sink nodes) is considered. Each source node must send all its locally generated data to only one of the BSs. To maximize network lifetime, it is essential to optimally match each source node to a particular BS and find an optimal routing solution. A polynomial time heuristic is proposed for optimal BS selection and anycast via a sequential fixing procedure. Through extensive simulation results, it is shown that this algorithm has excellent performance behavior and provides a near-optimal solution. [ABSTRACT FROM AUTHOR]

Orthogonal space-time block codes (OSTBCs) have attracted much attention owing to their simple code construction, maximal diversity gain, and low maximum-likelihood (ML) detection complexity when channel state information (CSI) is available at the receiver. This paper addresses the problem of ML OSTBC detection with unknown CSI. Focusing on the binary and quaternary PSK constellations, we show that blind ML OSTBC detection can be simplified to a Boolean quadratic program (BQP). From an optimization viewpoint the BQP is still a computationally hard problem, and we propose two alternatives for dealing with this inherent complexity. First, we consider the semidefinite relaxation (SDR) approach, which leads to a suboptimal, but accurate, blind ML detection algorithm with an affordable worst-case computational cost. We also consider the sphere decoding approach, which leads to an exact blind ML detection algorithm that remains computationally expensive in the worst case, but generally incurs a reasonable average computational cost. For the two algorithms, we study implementation methods that can significantly reduce the computational complexity. Simulation results indicate that the two blind ML detection algorithms are competitive, in that the bit error performance of the two algorithms is almost the same and is noticeably better than that of some other existing blind detectors. Moreover, numerical studies show that the SDR algorithm provides better complexity performance than the sphere decoder in the worst-case sense, and vice versa in the average sense. [ABSTRACT FROM AUTHOR]

A novel extension of the Multiple Signal Classification (MUSIC) algorithm for the frequency estimation problem is proposed in this work. It is shown that the MUSIC algorithm is a data dependent optimum notch filter design technique. When the input data passed through this optimum notch filter, the signal-to-noise ratio (SNR) of the output is minimized. The zeros of the optimum notch filter are then used to estimate the frequencies. In the classical MUSIC algorithm, this optimization is carried out over the set of all finite impulse response (FIR) filters having a fixed order. The framework of this paper allows the use of rational notch filters. The fact that the rational filters have sharper spectral characteristics is used to obtain improved estimates, particularly at low SNR. Some interesting properties of the proposed algorithm are derived, and further perspectives on the implementation aspects are provided. The analytical predictions are substantiated using numerical simulation results. [ABSTRACT FROM AUTHOR]

Recently, ultra wide-band (UWB) wireless communication technology, which provides high data transmission rates and is capable of linearly trading off between throughput and signal-to-noise ratio (SNR), has drawn much attention for short-range wireless networks. To fully exploit its desirable features and to minimize its interference with coexisting other systems requires knowledge of the SNRs at the receivers. In this paper, we propose an algorithm for estimating the pulse energy-to-noise ratio of an UWB signal through utilization of output samples from a correlator in the receiver and evaluate the performance of the proposed algorithm through computer simulation. A notable feature of the proposed algorithm is that it does not affect the effective throughput because the estimation process does not require any additional specific training signals. [ABSTRACT FROM AUTHOR]

Self-pruning is an effective scheme for efficient broadcasting in ad hoc wireless networks. In a self-pruning broadcast protocol, a node may not forward a broadcast packet if a certain self-pruning condition is satisfied based on the neighborhood information. In a static network with an ideal MAC layer, only a subset of nodes forward the broadcast packet and still guarantee the complete network delivery. Various protocols have been proposed with different self-pruning conditions. Recently, a generic self-pruning protocol was proposed by Wu and Dai [21], which combines the strength of previous conditions and is more effective. In this paper, we first propose an enhanced version of the generic protocol, which is more elegant in interpreting existing protocols and has a simpler correctness proof. Then, we evaluate the performance of the family of self-pruning protocols under various network situations with ns2. The objective is to observe the efficiency and reliability of these protocols as a function of network density, congestion, and mobility, and provide a guideline of implementation in the "real world." Our performance analysis reveals that the protocol reliability is barely affected by packet collision. However, most self-pruning protocols suffer from low delivery ratio in highly mobile networks. We further explore various techniques that improve the delivery ratio and show that both high efficiency and reliability can be achieved in highly mobile networks. [ABSTRACT FROM AUTHOR]

In this paper, we consider the exact top-k query problem in wireless sensor networks, i.e., where one seeks to find the k highest reported values as well as the complete set of nodes that reported them. Our primary contribution in this context is EXTOK, a provably correct and topology-independent new filtering-based algorithm for processing exact top-k queries. As a secondary contribution we confirm a previous result of ours by showing that the efficiency of top-k query processing algorithms, including EXTOK, can be further improved by simply choosing a proper underlying logical tree topology. We examine EXTOK's performance with respect to a number of parameters and different logical tree topologies while using both synthetic and real data sets. Our simulation reveal that EXTOK consistently outperforms the current state-of-the-art algorithm by a very significant margin and regardless of the underlying logical tree topology. [ABSTRACT FROM AUTHOR]

The introduction of relay station (RS) nodes is a key feature in next generation wireless networks such as 3GPP's long term evolution advanced (LTE-Advanced), or the forthcoming IEEE 802.16j WiMAX standard. This paper presents, using game theory, a novel approach for the formation of the tree architecture that connects the RSs and their serving base station in the uplink of the next generation wireless multi-hop systems. Unlike existing literature which mainly focused on performance analysis, we propose a distributed algorithm for studying the structure and dynamics of the network. We formulate a network formation game among the RSs whereby each RS aims to maximize a cross-layer utility function that takes into account the benefit from cooperative transmission, in terms of reduced bit error rate, and the costs in terms of the delay due to multi-hop transmission. For forming the tree structure, a distributed myopic algorithm is devised. Using the proposed algorithm, each RS can individually select the path that connects it to the BS through other RSs while optimizing its utility. We show the convergence of the algorithm into a Nash tree network, and we study how the RSs can adapt the network's topology to environmental changes such as mobility or the deployment of new mobile stations. Simulation results show that the proposed algorithm presents significant gains in terms of average utility per mobile station which is at least 17.1% better relatively to the case with no RSs and reaches up to 40.3% improvement compared to a nearest neighbor algorithm (for a network with 10 RSs). The results also show that the average number of hops does not exceed 3 even for a network with up to 25 RSs. [ABSTRACT FROM AUTHOR]