We consider a two sensor distributed detection system transmitting a binary non-uniform source over a Gaussian multiple access channel (MAC). We model the network via binary sensors whose outputs are generated by binary symmetric channels of different noise levels. We prove an optimal one dimensional constellation design under individual sensor power constraints which minimizes the error probability of detecting the source. Three distinct cases arise for this optimization based on the parameters in the problem setup. In the most notable case (Case III), the optimal signaling design is to not necessarily use all of the power allocated to the more noisy sensor (with less correlation to the source). We compare the error performance of the optimal one dimensional constellation to orthogonal signaling. The results show that the optimal one dimensional constellation achieves lower error probability than using orthogonal channels.

Among nonstationary time series, the class of Uniformly Modulated Processes (UMPs) have Evolutionary Power Spectra (EPS) that are the outer product of a modulating time-function and the spectrum of a stationary process. In this paper, we propose a smoothing procedure on an estimator of the EPS of a general nonstationary process to obtain an estimator of the EPS of a UMP. This is done by obtaining isolated estimates of the time-modulating function and the spectrum of the stationary component of a UMP, and then combining them to form our smoothed EPS estimate. Via simulation, we compare our estimator to the High Resolution Spectrogram and an estimator minimizing the Frobenius norm between the general estimator and one with the structure of a UMP’s EPS.

We propose a semi-parametric multitaper F -test for the detection of line components which have been modulated by a polynomial of a given degree. The test is based on one proposed by Thomson in his 2009 paper, "Polymomial Phase Demodulation in Multitaper Analysis." We give the asymptotic null distribution of our test statistic as F with degrees of freedom depending on the modulating polynomial degree, p, and the number of tapers used, K. We demonstrate the performance of our F -test via simulation, and we apply our F -test to optical solar data obtained by the Global Oscillations for Low Frequencies (GOLF) instrument onboard the SOHO solar observatory satellite.

With the abundant on-board resources in smart vehicles, they have become major candidates for providing ubiquitous services, including public sensing. One of the challenges facing such ubiquitous utilization is the recruitment of the participating vehicles. In this paper, we present the reputation-aware, trajectory-based recruitment (RTR) framework that handles recruitment of vehicles for public sensing. The framework considers the spatiotemporal availability of participants along with their reputation to select vehicles that achieve desired coverage of an area of interest within a budget cap. The framework consists of a reputation assessment scheme, a pricing model, and a selection scheme collaborating for a main recruitment objective; maximizing coverage with minimum cost. We propose greedy heuristic solutions targeting the selection problem in real-time. The RTR framework generalizes the basic selection problem to handle some practical scenarios, including departing vehicles and varying redundancy requirements. We also propose a reputation assessment scheme and a pricing model as parts of the framework. Extensive performance evaluation of the proposed framework is conducted and the evaluation shows that the proposed greedy heuristics are able to achieve results close to previously obtained optimal benchmarks under different scenarios, and that the framework succeeds in achieving high levels of coverage even when vehicles do not stick to their announced trajectories.

In this paper, lower bounds on the probability of a finite union of events are considered, i.e., $P(\bigcup_{i=1}^N A_i)$, in terms of the individual event probabilities $\{P(A_i), i=1,\ldots,N\}$ and the sums of the pairwise event probabilities, i.e., $\{\sum_{j:j\neq i} P(A_i\cap A_j), i=1,\ldots,N\}$. The contribution of this paper includes the following: (i) in the class of all lower bounds that are established in terms of only the $P(A_i)$'s and $\sum_{j:j\neq i} P(A_i\cap A_j)$'s, the optimal lower bound is given numerically by solving a linear programming (LP) problem with $N^2-N+1$ variables; (ii) a new analytical lower bound is proposed based on a relaxed LP problem, which is at least as good as the bound due to Kuai, Alajaji, and Takahara [Discrete Math., 215 (2000), pp. 147--158]; (iii) numerical examples are provided to illustrate the performance of the bounds.

We present new results on bounding the probability of a finite union of events, equation for a fixed positive integer N, using partial information on the events joint probabilities. We first consider bounds that are established in terms of {P(Ai)} and {ΣjcjP(Ai∩ Aj)} where c1, …, cNare given weights. We derive a new class of lower bounds of at most pseudo-polynomial computational complexity. This class of lower bounds generalizes the recent bounds in [1], [2] and can be tighter in some cases than the Gallot-Kounias [3]–[5] and Prékopa-Gao [6] bounds which require more information on the events probabilities. We next consider bounds that fully exploit knowledge of {P(Ai)} and {P(Ai∩ Aj)}. We establish new numerical lower/upper bounds on the union probability by solving a linear programming problem with equation variables. These bounds coincide with the optimal lower/upper bounds when N ≤ 7 and are guaranteed to be sharper than the optimal lower/upper bounds of [1], [2] that use {P(Ai)} and {ΣjP(Ai∩ Aj)}.

The classes of all lower/upper bounds on the probability of a finite union of events which are expressed only in terms of the individual event probabilities and the sums of the pairwise event probabilities are considered. The optimal lower and upper bounds in each class are given numerically by solving a linear programming (LP) problem. Furthermore, a suboptimal analytical lower bound is established by solving a relaxed LP problem, which is at least as good as an existing bound due to Kuai, et al. [1]. Note that the new lower bounds can be further improved algorithmically by optimizing them over subsets [2], [3], and can be applied to general estimation problems involving the probability of a finite union. Finally, the new lower/upper bounds are illustrated by examining the symbol and bit error rates of an uncoded communication system used in conjunction with Mary phase-shift keying (PSK) modulation over additive white Gaussian noise (AWGN) channels under maximum a posteriori (MAP) decoding.

Smart vehicles are considered major providers of ubiquitous information services. In this paper, we propose a solution for expedited and cost-effective access to vehicular public sensing services. The proposed caching-assisted data delivery (CADD) scheme applies caching on the delivery path of the collected data. Cached information can be used for later interests without having to request similar data from vehicles. CADD relies on the deployment of a “light-weight” road caching spot (RCS) at intersections, and on vehicles for communication to/from RCSs. CADD involves a novel caching mechanism that utilizes real-time information for selecting the caching RCSs while considering popularity in cache replacement. A data chunk to be replaced may be forwarded to another less-loaded RCS. CADD considers vehicles' headings to direct communication towards the destination, which reduces access delay. Performance evaluation of CADD shows significant improvements in the access cost and delay compared to a scheme that does not deploy RCSs.

Public sensing services utilizing the abundant on-vehicle resources are gaining high interest nowadays. One of the challenges facing such ubiquitous utilization is the recruitment and selection of the participating vehicles. In this paper, we present an optimal reputation-aware, trajectory-based framework that handles recruitment of vehicles for public sensing. The framework considers the spatiotemporal availability of participants along with their reputation to select vehicles that achieve desired coverage of an area of interest within a budget limit. In addition, we present a reputation assessment scheme and a pricing model for computing a reputation score and a recruitment cost for each candidate participant. The framework is formulated as an integer linear programming optimization problem and hence provides a benchmark and upper bound on achievable potential. We present analysis for two different practical recruitment objectives and show results under various scenarios.

With the abundant on-board resources in intelligent vehicles, they have become major candidates for providing ubiquitous services, including urban sensing. This paper proposes an efficient recruitment scheme for vehicles in urban sensing applications. Our trajectory-based recruitment (TBR) scheme solves the problem of participant selection by considering spatiotemporal availability of participants. The aim of TBR is choosing the minimum number of vehicles that achieve a required level of coverage for the area of interest. TBR utilizes the easy-to-acquire trajectories of the candidate vehicles as indicators of the availability of participants, and applies a minimal-cover greedy algorithm for selection. The basic greedy algorithm is adapted to handle some practical scenarios, including departing vehicles and varying redundancy requirements. The paper also discusses two data acquisition models for retrieving the sensing data (on-demand and unsolicited). Assessment of TBR shows that it achieves high levels of coverage even when vehicles do not stick to their announced trajectories.

Forward error correction (FEC) coding is the preferred error correction technique for interactive video streaming applications on the Internet. Because its performance is impaired by the burstiness of packet loss of Internet links, peer-to-peer (P2P) networks are often proposed to provide multiple paths between a sender and a receiver. However, peers may leave abruptly and the number of disjoint paths may be limited; it is unclear whether or when the use of P2P networks for path diversity can be justified. In this paper, we study the packet loss ratio after FEC correction when using P2P networks to provide multiple paths. We examine two situations: a sender can find enough disjoint paths, or uses a limited number of disjoint paths. We model Internet links using Markov chains, provide numerical analysis of the performance of systematic FEC codes, and verify the results by simulation. We find that although using P2P networks for path diversity often results in a lower post-FEC loss ratio, conditions apply. There exist guidelines but no simple formula to determine when to use P2P networks for path diversity and coding parameters. An application should carefully evaluate the performance gain before taking actions.

This paper is concerned with undoing aliasing effects. Such effects arise from discretely sampling a continuous-time stochastic process. We describe a technique to undo aliasing effects, given two processes, one being a time-delayed version of the other. Our motivation is the helioseismological problem of determining modes of pressure waves in the sun. Our technique is applied to solar radio data. We conclude that certain low-frequency modes known in the helioseismology literature are likely the result of aliasing.

In a heterogeneous wireless sensor network (WSN), relay nodes (RNs) are adopted to relay data packets from sensor nodes (SNs) to the base station (BS). The deployment of the RNs can have a significant impact on connectivity and lifetime of a WSN system. This paper studies the effects of random deployment strategies. We first discuss the biased energy consumption rate problem associated with uniform random deployment. This problem leads to insufficient energy utilization and shortened network lifetime. To overcome this problem, we propose two new random deployment strategies, namely, the lifetime-oriented deployment and hybrid deployment. The former solely aims at balancing the energy consumption rates of RNs across the network, thus extending the system lifetime. However, this deployment scheme may not provide sufficient connectivity to SNs when the given number of RNs is relatively small. The latter reconciles the concerns of connectivity and lifetime extension. Both single-hop and multihop communication models are considered in this paper. With a combination of theoretical analysis and simulated evaluation, this study explores the trade-off between connectivity and lifetime extension in the problem of RN deployment. It also provides a guideline for efficient deployment of RNs in a large-scale heterogeneous WSN.

The evolutionary spectrum (ES) is a time-dependent analogue of the spectrum of a stationary process. Existing estimators of the ES suffer from bias problems in the boundary region of the time-frequency domain, due to windowing effects. We propose a new estimator of the ES of a uniformly modulated process which mitigates these problems. Our estimator is based on an extrapolation of the ES in time, using an estimate of the time derivative of the ES. We apply our estimator to a simulated example of a uniformly modulated process with known ES.

One of the main challenges for metro-scale WiMAX mesh network deployments is related to capacity scaling. In a full mesh mode, a WiMAX node acts as a mesh router as well as a client access node. To improve latency and speed performance, typical dual- and multi-radio mesh solutions use different radio channels to create separate links for access and mesh relaying services. The available spectrum is therefore split between mesh and client access services. Network operators operating the WiMAX system over the licensed spectrum are not keen to provide separate radio channels for access and mesh relay services, as this reduces the total number of users serviced per spectrum allocation. MIMO-multiplexing relaying approach however provides separate links for access and mesh relaying services on the same radio channel. In this paper, we discuss the multi-hop capacity of OFDM-based MIMO-multiplexing relaying in WiMAX networks. For an NxN MIMO-multiplexing relaying with amplification factor alpha at relay nodes, R-hops relaying degrade the capacity by at most -Nlog2(alpha2R/(1 +SigmaRr-1alpha2rNr)) +RN/log2(N) bits/sec/Hz. Therefore, greater capacity loss is experienced in networks employing high-order MIMO- multiplexing relaying. We also show that the capacity loss is independent of the OFDM configurations employed; thus network operators could employ higher OFDM configurations to compensate data rate loss in access services when some of the MIMO-multiplexing links are dedicated to mesh relay. This analysis provides useful guidelines for operators planning MIMO-multiplexing option for mesh support in WiMAX network.

This paper derives the multi-hop capacity of OFDM-based MIMO-multiplexing relaying systems. MIMO-multiplexing relaying presents a spectrally efficient means of realizing mesh supports in wireless networks operating over licensed bands by providing separate links for access and mesh relaying services on the same broadband radio channel. We show that for an NtimesN MIMO-multiplexing relaying system with amplification factor alpha at relay nodes, R-hops relaying degrade the capacity by at most -Nlog2(alpha2R/ (1 + Sigmar=1Ralpha2rNr)) + RNlog2(N) bits/sec/Hz. Therefore, greater capacity loss is experienced in MIMO-multiplexing relaying involving high order MIMO systems. We also illustrate that the capacity loss is independent of the OFDM configurations employed; thus network operators could employ higher OFDM configurations to compensate for data rate loss in access services when some of the MIMO-multiplexing links are dedicated to mesh relay. This pioneering analysis provides useful guidelines for network operators planning to employ MIMO-multiplexing option for mesh relay supports.

Abstract The IEEE 802.16 standard is gaining broad consideration to serve the expanding demand for broadband access networks. In this standard, the best effort traffic uses the reservation multiple access control (MAC) mechanism, which is widely adopted in recent broadband network technologies. The goal of this paper is to study the performance of the MAC protocol of the best effort traffic in the IEEE 802.16 standard with emphasis on the size of the reservation period. We use a two‐stage Markov chain model to capture all possible events on the reservation and service periods. This allows the computation of the inflow and outflow of bandwidth requests (BWRs) and their associated data packets which leads to the delay and throughput formulas. By means of illustrative examples and numerical results, validated through simulation, we investigate the key importance of the size of reservation period. We highlight potential performance improvement, through opportunistic dynamic control of the size of the reservation period to enhance the performance of reservation MAC protocol. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper we study efficient triangular grid-based sensor deployment planning for coverage when sensor placements are perturbed by random errors around their corresponding grid vertices, where the random errors are modeled by uniform displacements inside error disks of a given finite radius. The average coverage percentage of the sensing field is derived as a function of the length of the grid tiles d, and the radius of the random error disks, R. Our expressions for the average coverage percentage are computed numerically and verified by Monte-Carlo simulations. The analytical methods can be used with other types of grid-based deployment with little modification, such as square grid-based deployment. One appealing feature of grid-based deployment that we observe is that the sensing coverage is rather resilient to random errors. Based on this observation and the quantitative results from our analysis, we discuss several approaches to efficient grid-based deployment planning for coverage and illustrate these through numerical examples.

In this paper we study efficient grid-based sensor deployment planning for coverage when sensor placements are perturbed by random errors around their corresponding grid vertices. We propose a generic approach to evaluate the average coverage percentage of the sensing field. Our generic approach can be used with all types of grid-shapes, all kinds of random error distributions, and different sensing models. We apply the generic approach to two practical deployment scenarios, namely, the triangular grid-based deployment with bounded uniform random errors and unbounded normal random errors. For both cases, the average coverage percentage is computed numerically and verified by Monte-Carlo simulations. Based on the numerical results, we discuss several approaches to efficient grid-based deployment planning for coverage and illustrate these through numerical examples.

A model for a binary additive noise communication channel with memory is introduced. The channel noise process, which is generated according to a ball sampling mechanism involving a queue of finite length$M$, is a stationary ergodic$M$th-order Markov source. The channel properties are analyzed and several of its statistical and information-theoretical quantities (e.g., block transition distribution, autocorrelation function (ACF), capacity, and error exponent) are derived in either closed or easily computable form in terms of its four parameters. The capacity of the queue-based channel (QBC) is also analytically and numerically compared for a variety of channel conditions with the capacity of other binary models, such as the well-known Gilbert–Elliott channel (GEC), the Fritchman channel, and the finite-memory contagion channel. We also investigate the modeling of the traditional GEC using this QBC model. The QBC parameters are estimated by minimizing the Kullback–Leibler divergence rate between the probability of noise sequences generated by the GEC and the QBC, while maintaining identical bit-error rates (BER) and correlation coefficients. The accuracy of fitting the GEC via the QBC is evaluated in terms of ACF, channel capacity, and error exponent. Numerical results indicate that the QBC provides a good approximation of the GEC for various channel conditions; it thus offers an interesting alternative to the GEC while remaining mathematically tractable.

Device placement is a fundamental factor in determining the coverage, connectivity, cost and lifetime of a wireless sensor network (WSN). In this paper, we explore the problem of relay node placement in heterogeneous WSN. We formulate a generalized node placement optimization problem aimed at minimizing the network cost with constraints on lifetime and connectivity. Depending on the constraints, two representative scenarios of this problem are described. We characterize the first problem, where relay nodes are not energy constrained, as a minimum set covering problem. We further consider a more challenging scenario, where all nodes are energy limited. As an optimal solution to this problem is difficult to obtain, a two-phase approach is proposed, in which locally optimal design decisions are taken. The placement of the first phase relay nodes (FPRN), which are directly connected to sensor nodes (SN), is modeled as a minimum set covering problem. To ensure the relaying of the traffic from the FPRN to the base station, three heuristic schemes are proposed to place the second phase relay nodes (SPRN). Furthermore, a lower bound on the minimum number of SPRN required for connectivity is provided. The efficiency of our proposals is investigated by numerical examples.

The IEEE 802.16 technology is increasingly being considered for fixed and mobile voice and high speed data access. The success of IEEE 802.16 in a mobile or mesh network environment is contingent on its ability to embrace and sustain dynamic traffic conditions. To this end, we study the reservation multiple access protocol of the IEEE 802.16 standard to understand the protocol performance and potentials. Basically for its primary role in controlling the protocol performance, we emphasize the design of the contention-based reservation period. We present an analytical model for computing the contention delay, data transmission delay, and throughput resulting from different contention period allocations under dynamic traffic conditions. We illustrate the performance compromises and remedies with respect to the contention period allocation. Based on the analytical model and performance evaluation results, we institute a research base to enhance the performance of the reservation multiple access protocol in IEEE 802.16 standard.

Due to cost constraints and limited sensing capability of sensor nodes, it may be impossible or unrealistic for a wireless sensor network to provide full area coverage in some circumstances. In this paper, we introduce the novel concept of information-oriented sensing coverage, which measures the actual information utility obtained by a wireless sensor network. An information-oriented sensor deployment algorithm is proposed. The algorithm makes use of the fact that in many applications the importance or the information utility at different locations is non-uniform across the sensing field. The effectiveness of the proposed algorithm is studied using simulation. Being a pioneering effort in this area, our work paves a new direction of sensor deployment.

Due to the cost constraint, it is impossible or unnecessary for a wireless sensor network to provide full area coverage in many applications. To mitigate the negative impacts of incomplete coverage, it is critical to realize that the importance of different locations of a sensing field is often non-uniform in practice. By strategically allocating more sensor nodes to the area of more importance, the differential random deployment can effectively decrease the loss for not detecting interesting events, as compared to the uniform random deployment. In this paper, we study the differential random deployment of sensor nodes in depth. We discuss a number of factors that affect the design of the differential random deployment density function. Because of the inherent complexity, we propose a heuristic structure of the differential deployment density function. The effectiveness of our proposal is verified in the performance study via simulation.

Grid-based sensor deployment is an effective and efficient practice for provisioning wireless sensor networks. Previous work has addressed grid-based deployment of sensors in order to guarantee sensing coverage under the assumption that each device can be placed exactly at the grid vertices. However, in reality, the accuracy of device placement may be subject to various errors, which are shown to impair the sensing coverage. To overcome the negative impacts of these errors, the grid resolution and the number of devices to be deployed should be re-evaluated. In this paper, two deployment errors are identified, namely, misalignment and random errors. We derive the minimum number of sensors required by a robust grid-based sensor deployment assuming that the errors are bounded. This research shows that when designing a realistic large-scale grid-based sensor deployment, errors in device placement must be taken into account.

In this paper, the problem of relay node placement in heterogeneous wireless sensor networks (WSNs) is addressed. As all nodes are energy limited, placement strategies aimed at minimizing network cost with constraints on lifetime and connectivity are presented. Based on a two-phase placement framework, a heuristic algorithm to place the second phase relay nodes (SPRNs) is proposed. Furthermore, a lower bound of the optimal solution is derived. The efficacy of our proposal is investigated in both grid networks and random networks. The work of this paper provides guidance for implementing feasible WSNs for civilian applications.

Device deployment is a major challenge to successful implementation of large scale wireless sensor network (WSN) applications. The number and positions of devices determine many intrinsic properties of a WSN system. However, the deployment issue has mostly been confined to studies concerning coverage and connectivity. The impact of device deployment on the usability or system lifetime has been largely neglected. In this research, we investigate how a deployment strategy can affect both connectivity and network lifetime of a WSN. Based on quantitative analysis of such effects, we propose three deployment strategies, namely, connectivity-oriented, lifetime-oriented, and hybrid deployment. The performance of the three strategies is evaluated through simulation. The results of this research provide tangible guidance of efficient provisioning for large scale WSNs.

Node placement strategy is an intrinsic issue when provisioning of a wireless sensor network (WSN). In this paper, we address the placement problem for a class of heterogeneous WSNs, wherein nodes have different energy supplies and functionalities. We formulate a generalized node placement optimization problem aiming at minimizing the network cost with constraints on lifetime and connectivity. We propose a placement scheme with two phases. We model the placement of the first phase relaying nodes (FPRNs) as a minimum set cover problem and a dynamic programming algorithm is developed to solve it. For the placement of the second phase relaying nodes (SPRNs), we derive two fundamental design principles-the far-near strategy and max-min strategy. The implementation of the placement schemes is illustrated by examples. Our proposed mechanism is a first attempt towards facilitating realistic relay node placement in WSNs.

In this paper, we propose a novel single hop-clustering scheme called step wise adaptive clustering hierarchy (SWATCH). SWATCH relieves the CH number variability problem by employing stepwise CH selection in two stages. It is a dynamic and straightforward scheme as LEACH. However, instead of selecting all CHs in one step, SWATCH splits the selection phase into an initial selection stage and an add-on selection stage. The initial selection is similar to LEACH. However, if the number of CHs in the initial selection is below a pre-determined target, the add-on selection will be invoked and will continue until an acceptable number of CHs have been selected. As a result, the number of CHs selected in each round tends to congregate in a narrow range around the optimal value. In order to evaluate the performance of SWATCH, we develop a hierarchical Markov chain model to track the behavior of the system. Numerical results verify our design objective in that the number of selected CHs highly conforms to the optimal value. Based on the results, an optimal number of CHs are greatly reduced the communication energy.

In this paper, we explore the relay node (RN) placement problem in a heterogeneous wireless sensor network (WSN). The objective of the RN placement is to use a minimum number of additional RNs to enable the relaying of given traffic on existing nodes to the base station (BS) under the energy constraints. We assume RNs can adjust their transmission power according to the distance to the intended destination. To make best use of power adaptivity of RN, we propose two heuristic solutions, namely, independent placement with direct allocation (IPDA) and collaborative placement with locally optimal allocation decision (CPLOAD). Furthermore, a lower bound on the minimum number of additional RNs is provided. The effectiveness of our proposals is investigated through simulation using numerical examples.

While a lot of existing research attempts to extend the lifetime of a wireless sensor network (WSN) by designing energy efficient networking protocols, the impact of random device deployment on system lifetime is not stressed enough. Some research efforts have tried to optimize device deployment with respect to lifetime by assuming devices can be placed deliberately. However, the methodologies and solutions therein are not applicable to a randomly deployed large scale WSN. In this research, we propose three random deployment strategies for relay nodes in a heterogeneous WSN, namely, connectivity- oriented, lifetime-oriented and hybrid deployment. We investigate how a strategy can affect both connectivity and network lifetime of a multi-hop heterogeneous WSN, in which relay nodes transmit data to the base station via multi-hop relay. The performance of the three strategies is evaluated through simulations. The results of this research provide a viable solution to the problem of optimizing provisioning of a large scale heterogeneous WSN.

Device deployment is a fundamental issue of WSN provisioning. The types, number, and locations of devices determine many intrinsic properties of a WSN, such as coverage, connectivity, cost and lifetime. In this paper, we explore the problem of the optimal WSN deployment, with an objective of minimizing the network cost with lifetime constraint. We discuss and identify the characteristics of a type of WSN applications. The ultimate objectives of the device deployment for such applications are presented and discussed. We refine a deployment problem in a practical and fundamental scenario. We model this problem with the minimum set covering problem. Based on a recursive algorithm, a deterministic deployment strategy is proposed.

We investigate the error performance of a communication system where a nonuniform memoryless binary source is transmitted via Gray-mapped-ary phase-shift keying or quadrature amplitude modulation over memoryless Rayleigh fading channels, and demodulated via optimal maximum a posteriori detection. Using recently derived upper and lower bounds on the probability of a general union of events, which are tight and can be efficiently computed, the system symbol-error () and bit-error ( ) rates are evaluated for a wide range of channel conditions. Since for nonuniform signaling, Gray mapping is not necessarily optimal for minimizing or (as was recently shown by Takahara et al.), we also evaluate the system performance under the map obtained by Takahara et al. and compare it with a Gray-mapped system.

We investigate the modeling of the well-known burst-noise Gilbert-Elliott channel (GEC) using a recently introduced queue-based channel (QBC) model. The QBC parameters are estimated by minimizing the Kullback-Leibler divergence rate between the probability of error sequences generated by the QBC and the GEC, while maintaining identical bit error rates and correlation coefficients. The accuracy of fitting the GEC via the QBC is evaluated in terms of channel capacity and autocorrelation function. Numerical results show that the QBC provides a very good approximation of the GEC for various channel conditions. It thus offers an interesting alternative to the GEC while remaining mathematically tractable.

We propose a new framework for ant-based routing algorithms which addresses the trade-off between effective and timely discovery of optimal routes, and the desire to keep data traffic flowing smoothly. The approach utilizes a database of stored delay estimates on routes to build separate routing tables for ants and data packets. This decoupling results in better control of responsiveness to load or topology fluctuations since the tables can evolve at different rates, according to different rules. The presented architecture also features guaranteed loop-free routes for data packets.

The design of two-dimensional constellation mappings for the transmission of binary nonuniform memoryless sources over additive white Gaussian noise channels using standard M-ary PSK and QAM modulation schemes is investigated. The main application of this problem is the incorporation of an adaptive mapping assignment in modem devices that employ fixed PSK/QAM modulation schemes for the transmission of heterogenous data (such as multimedia information) containing various levels of nonuniformity. In general, the optimal mapping depends on both the probability distribution of the input signals and the signal-to-noise ratio (SNR) in the channel, in addition to the geometry of the signal constellation. We show that constellation mappings which follow the objective of minimizing the average symbol energy and, given this, maximizing the decoding probability of the most likely signals, can yield symbol-error-rate and bit-error-rate performance that is substantially better than Gray encoding maps. Gains as high as 3.5 dB in SNR E/sub b//N/sub 0/ are obtained for highly nonuniform sources. Finally, we note that the mappings techniques result in nonzero mean constellations and briefly consider their performances when they are converted to zero mean constellations by shifting. In this case, we observe that the shifted zero-mean Gray map outperforms our shifted maps for small- to medium-sized constellations (M/spl les/32), but not for larger sizes.

We consider a Bonferroni-type lower bound due to Kounias (1968) on the probability of a finite union. The bound is expressed in terms of only the individual and pairwise event probabilities; however, it suffers from requiring an exponentially complex search for its direct implementation. We address this problem by presenting a practical algorithm for its evaluation. This bound is applied together with two other bounds, a recent lower bound (the KAT bound) and a greedy algorithm implementation of an upper bound due to Hunter (1976), to examine the symbol error (P/sub a/) and bit error (P/sub b/) probabilities of an uncoded communication system used in conjunction with M-ary phase-shift keying (PSK)/quadrature amplitude (QAM) (PSK/QAM) modulations and maximum a posteriori (MAP) decoding over additive white Gaussian noise (AWGN) channels. It is shown that the bounds-which can be efficiently computed-provide an excellent estimate of the error probabilities over the entire range of the signal-to-noise ratio (SNR) E/sub b//N/sub 0/. The new algorithmic bound and the greedy bound are particularly impressive as they agree with the simulation results even during very severe channel conditions.