Search

We introduce a construction of a set of code sequences {Cn(m) : n ≥ 1, m ≥ 1} with memory order m and code length N(n). {Cn(m)} is a generalization of polar codes presented by Arikan in [1], where the encoder mapping with length N(n) is obtained recursively from the encoder mappings with lengths N(n − 1) and N(n − m), and {Cn(m)} coincides with the original polar codes when m = 1. We show that {Cn(m)} achieves the symmetric capacity I(W) of an arbitrary binary-input, discrete-output memoryless channel W for any fixed m. We also obtain an upper bound on the probability of block-decoding error Pe of {Cn(m)} and show that $${P_e} = O({2^{ - {N^\beta }}})$$ is achievable for β < 1/[1+m(ϕ − 1)], where ϕ ∈ (1, 2] is the largest real root of the polynomial F(m, ρ) = ρm − ρm − 1 − 1. The encoding and decoding complexities of {Cn(m)} decrease with increasing m, which proves the existence of new polar coding schemes that have lower complexity than Arikan’s construction.

We consider demand response, supply management and power scheduling in a smart grid in the presence of renewable energy sources. We maximize the total social welfare while constraining the probability of renewable forecast error, and solve the ensuing optimization problem to derive the best conventional power production and user consumption schedules. The resulting optimal policies demonstrate their superiority over a competing policy in terms of social welfare, particularly when the constraint is tight.

We optimize quantization intervals based on Chernoff information (CI) and deflection criterion (DC) for distributed detection with multiple sensors and a fusion center (FC). The CI and DC of the overall system are derived, and locally optimal quantization of sensor observations is proved to achieve global optimality for identical sensor observations. An optimal iterative partitioning algorithm is proposed for both nonidentically and identically distributed sensor reports. Results indicate that optimal 2-bit quantization gives detection performance that is close to what is achievable by collecting soft information from sensors.

In this paper, we consider backscatter modulation-based radio-frequency identification (RFID) communication with energy harvesting tags. In this regard, we consider the effects of energy harvesting on amplified backscattering and evaluate the average read probability of the RFID tags over fading channel scenarios. We then propose a heuristic transmit power policy optimization algorithm for the backscatter modulated signals at the active RFID tags. Both theoretical and simulation results which are in tight agreement with each other show that the use of active tags with energy harvesting capabilities significantly improve the average read probability performance of RFID communication.

Consumer demand profiles and fluctuating renewable power generation are two main sources of uncertainty in matching demand and supply. This paper proposes a model of the electricity market that captures the uncertainties on both the operator and user sides. The system operator (SO) implements a temporal linear pricing strategy that depends on real-time demand and renewable generation in the considered period combining real-time pricing with time-of-use pricing. The announced pricing strategy sets up a noncooperative game of incomplete information among the users with heterogeneous, but correlated consumption preferences. An explicit characterization of the optimal user behavior using the Bayesian Nash equilibrium solution concept is derived. This explicit characterization allows the SO to derive pricing policies that influence demand to serve practical objectives, such as minimizing peak-to-average ratio or attaining a desired rate of return. Numerical experiments show that the pricing policies yield close to optimal welfare values while improving these practical objectives.

We consider polar coding over a binary-input, discrete-output memoryless channel. We obtain bounds on the Bhattacharyya parameters of the so-called synthesized binary-input channels by using the Hamming weights of the rows of the generator matrix and the symmetric capacity of the underlying channel. Our bounds provide insight on the channel-specific construction of polar codes by showing the influence of the Hamming weight and the underlying channel. By using the new bounds one can obtain a characterization for the exponent of polar codes, where the inherent effect of the Hamming weights becomes evident.

In this paper, we study demand response (DR) management when a wind farm is connected to a smart grid. Total social welfare, defined in terms of consumer utility and cost of power production, is maximized while the probability of power deficit due to the uncertainty in renewable production is limited by an upper bound. We find the optimal deficit-limiting conventional power production and consumption schedules, and demonstrate their superiority over competing policies, particularly when the renewable share is high. It is also noted that geographically diverse turbines (or wind farms) should be connected to the grid to ensure less variable power output and low enough probability of deficit.

In this paper, we propose a time-of-arrival (TOA) estimation scheme using impulse-radio ultra-wideband (IR-UWB). This scheme is featured by a low sampling rate and is robust against clock drift. Low-rate stroboscopic sampling, which can achieve an equivalent sampling rate as high as the Nyquist sampling rate, is adopted to achieve a high resolution TOA estimate by IR-UWB. Since a long preamble is required to collect sufficient data samples, the clock drift is one of the main error sources in TOA estimation with stroboscopic sampling IR-UWB systems. Taking the drift into account, we first obtain a maximum-likelihood (ML) estimate of the drift, and then estimate the TOA using the averaged data samples calibrated for the drift. Simulation results corroborate that the associated drift calibration significantly reduces the TOA estimation errors, and that stroboscopic sampling can achieve the same estimation resolution as Nyquist rate sampling.

In this paper, a channel precoding technique is proposed for M-ary continuous phase modulation (CPM) for multipath downlink channels. The proposed pre-equalizer, based on the approximation of CPM signals by complex exponential functions, applies modulo-arithmetic operations to ensure the stability as in Tomlinson-Harashima method. Spectrally efficient precoded signals are obtained with small envelope variations by employing a transmit antenna selection technique. Because the equalization functions are transferred to the base station, the complexity in the mobile terminals is reduced significantly. The simulation results depict the power spectral density of the transmitted signals and the bit-error rate. An upper bound on the error performance is computed and the relation between the number of transmit antennas and the spectral efficiency is derived.

In this paper, two novel iterative chip-level synchronization methods, one blind and the other data-aided, are proposed for chip-interleaved time-hopping (TH) ultra-wideband signals that are subject to intersymbol interference (ISI) and multiple access interference (MAI). Both algorithms are soft-input, soft-output devices which receive the a posteriori probability of each chip from the minimum mean-square error (MMSE) filter that suppresses both ISI and MAI. Using this soft information and the structure of TH codes, the blind synchronizer produces the probabilities of time delays which are fed into the channel decoder. In addition to the knowledge of TH codes and the soft information coming from the MMSE filter, the data-aided synchronizer uses a training sequence in the generation of the probabilities of time delays. Although the channel decoder for chip-interleaved systems is rather simple, here it is redesigned to incorporate the soft information. The resulting receivers perform as well as iterative equalization with perfect timing information, and catch the single-user performance at moderate signal-to-noise ratios.

The performance of impulse radio is investigated for ultra-wideband (UWB) channels and orthogonal M-ary pulse position modulation (PPM) in terms of the maximum achievable information rate, where the outputs of the M-ary correlation receiver implemented through the rake structure with conventional maximal ratio combining are subject to either soft or hard decisions. The major difference observed between the soft- and hard-output systems is that at low values of the mutual information the minimum information bit energy to noise ratio required for reliable communication increases with the constellation size for the hard-output systems. Furthermore, it is demonstrated that independent of the type of the output, achievable rates are considerably low at medium-to-large signal-to-noise ratio (SNR) values in UWB channels compared to those in additive white Gaussian noise channels, as the frequency-selectivity of the UWB channel destroys the orthogonality of the transmitted PPM signals. Interference from other users and constellation size put additional constraints on the achievable rates at high SNR.

In this letter, an orthogonal space-time (ST) block coding (STBC) technique for continuous phase modulation (CPM) is proposed and combined with a frequencydomain equalizer (FDE) to obtain high performance gains over frequency-selective channels. The new method maintains the constant envelope and phase continuity of the CPM waveforms perfectly by using appropriate tail symbols during the addition of cyclic prefix and interblock transitions and, therefore, has no negative impact on the spectral efficiency. Because the ST-coded CPM blocks remain orthogonal, FDE is applied as in the case of single antenna transmissions without any increase in the computational load. Simulations corroborate the performance improvement offered by STBC while also preserving the desired bandwidth efficiency of CPM.

For target tracking applications, wireless sensor nodes provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration among themselves to improve the target localization and tracking accuracies. An energy-efficient collaborative target tracking paradigm is developed for wireless sensor networks (WSNs). A mutual-information-based sensor selection (MISS) algorithm is adopted for participation in the fusion process. MISS allows the sensor nodes with the highest mutual information about the target state to transmit data so that the energy consumption is reduced while the desired target position estimation accuracy is met. In addition, a novel approach to energy savings in WSNs is devised in the information-controlled transmission power (ICTP) adjustment, where nodes with more information use higher transmission powers than those that are less informative to share their target state information with the neighboring nodes. Simulations demonstrate the performance gains offered by MISS and ICTP in terms of power consumption and target localization accuracy.

In this paper, a doubly-iterative linear receiver, equipped with a soft-information aided frequency domain minimum mean-squared error (MMSE) equalizer, is proposed for the combined equalization and decoding of coded continuous phase modulation (CPM) signals over long multipath fading channels. In the proposed receiver architecture, the front-end frequency domain equalizer (FDE) is followed by the soft-input, soft-output (SISO) CPM demodulator and channel decoder modules. The receiver employs double turbo processing by performing back-end demodulation/decoding iterations per each equalization iteration to improve the a priori information for the front-end FDE. As presented by the computational complexity analysis and simulations, this process provides not only a significant reduction in the overall computational complexity, but also a performance improvement over the previously proposed iterative and noniterative MMSE receivers.

In this paper, a doubly iterative receiver is proposed for joint turbo equalization, demodulation, and decoding of coded binary continuous-phase modulation (CPM) in multipath fading channels. The proposed receiver consists of three soft-input soft-output (SISO) blocks: a front-end soft-information-aided minimum mean square error (MMSE) equalizer followed by a CPM demodulator and a back-end channel decoder. The MMSE equalizer, combined with an a priori soft-interference canceler (SIC) and an a posteriori probability mapper, forms a SISO processor suitable for iterative processing that considers discrete-time CPM symbols which belong to a finite alphabet. The SISO CPM demodulator and the SISO channel decoder are both implemented by the a posteriori probability algorithm. The proposed doubly iterative receiver has a central demodulator coupled with both the front-end equalizer and the back-end channel decoder. A few back-end demodulation/decoding iterations are performed for each equalization iteration so as to improve the a priori information for the equalizer. As presented in the extrinsic information transfer (EXIT) chart analysis and simulation results for different multipath fading channels, this provides not only faster convergence to low bit error rates, but also lower computational complexity.

Space-frequency coded (SFC) orthogonal frequency-division multiple access (OFDMA) is considered in the presence of multitone (MT) interference. Analytical expressions for the bit-error probability (BEP) are derived for OFDMA with SFC in a frequency-selective fading environment. It is shown that SFC increases the resistance of OFDMA against the MT interference and reduces BEP considerably

The performance of an energy system under a real-time pricing mechanism depends on the consumption behavior of its customers, which involves uncertainties. In this paper, we consider a system operator that charges its customers with a real-time price that depends on the total realized consumption. Customers have unknown and heterogeneous consumption preferences. We propose behavior models in which customers act selfishly, altruistically or as welfare-maximizers. In addition, we consider information models where customers keep their consumption levels private, communicate with a neighboring set of customers, or receive broadcasted demand from the operator. Our analysis focuses on the dispersion of the system performance under different consumption models. To this end, for each pair of behavior and information model we define and characterize optimal rational behavior, and provide a local algorithm that can be implemented by the consumption scheduler devices. Analytical comparisons of the two extreme information models, namely, private and complete information models, show that communication model reduces demand uncertainty while having negligible effect on aggregate consumer utility and welfare. In addition, we show the impact of real-time price policy parameters have on the expected welfare loss due to selfish behavior affording critical policy insights., A revised version is to appear on IEEE Trans. on Smart Grid

Power-line communications (PLC) usually suffers from the effects of non-white (correlated) noise, which results from the memory in the channel. In order to combat the effects of the channel memory, interleavers, which introduce a large amount of delay, are often employed together with error control coding. Low-density parity-check (LDPC) codes have been adopted in the recent broadband PLC (BPLC) standards, including the Homeplug AV specification. Analysis of the noise amplitude distribution of each individual frequency component across the PLC noise spectrum has recently shown that noise can be modeled using the generalized Gaussian distribution (GGD). In this paper, an LDPC-coding based receiver that utilizes the GGD noise model is developed, and it is shown that the proposed receiver structure outperforms the conventional receivers designed under the Gaussian noise assumption. Real noise measurements and Homeplug AV specification definitions are employed to demonstrate the high error performance of the proposed receiver structure.

Consumer demand profiles and fluctuating renewable power generation are two main sources of uncertainty in matching demand and supply. This paper proposes a model of the electricity market that captures the uncertainties on both, the operator and the user side. The system operator (SO) implements a temporal linear pricing strategy that depends on real-time demand and renewable generation in the considered period combining Real-Time Pricing with Time-of-Use Pricing. The announced pricing strategy sets up a Bayesian game among the users with unknown, heterogeneous and correlated consumption preferences. The explicit characterization of the optimal selfish user behavior using the Bayesian Nash equilibrium solution concept allows the SO to derive pricing policies that influence demand to serve practical objectives such as minimizing peak-to-average ratio or attaining a desired rate of return while at the same time hedging renewable generation uncertainty.

A game-theoretic framework based on smart pricing in power grids that incorporates heterogeneous user preferences and renewable power uncertainty is considered. The system operator adopts an adaptive pricing policy that depends on total consumption and renewable generation. The pricing policy sets up a non-cooperative game of incomplete information among users with heterogeneous preferences. Selfish, altruistic and welfare maximizing user behavior models are proposed. Information exchange models in which users only have private information, communicate or receive broadcasted information are considered. For each pair of behavior and information exchange models, rational consumption strategy is characterized. Numerical analyses reveal that communication is beneficial for the expected aggregate payoff while it does not affect the expected net revenue of the system operator. Moreover, the additional information to the users helps reduce the variance of total consumption among runs increasing the accuracy of demand predictions.

This paper is a corrected version of that printed in IEEE Trans. Broadcast., vol.52, no3, p.325-35, 2006 September. Space-frequency coded (SFC) and channel coded orthogonal frequency-division multiplexing (COFDM) are considered under narrow-band interference (NBI). Analytical expressions for the bit error probability (BEP) are derived for OFDM with SFC in a frequency-selective fading environment. It is shown that SFC increases the resistance of COFDM against the NBI and reduces the BEP considerably. Specific attention is paid to Digital Terrestrial Television Broadcasting (DTTB), and the associated coding gains are discussed

A robust receiver is proposed for time-hopping pulse-position-modulation impulse radio systems transmitting over frequency-selective ultra-wideband channels, which aims to remove large-amplitude outliers that occur due to impulsive noise. The proposed system contains a rake structure for collecting the signal energy, which is dispersed over a large number of paths as a result of the wide channel bandwidth. Before the paths are merged via maximal ratio combining, they are passed through nonlinearities to trim the outlier noise components. The robust receiver so designed is shown to outperform the conventional linear rake receiver that consists of only matched filters at its fingers.

Wireless multiple-access protocols using retransmission diversity resolve packet collisions through source separation techniques. Retransmissions are applied to achieve the necessary diversity level to determine the collision multiplicity blindly, whose proper detection is essential for good resolution performance. The so-called network-assisted diversity multiple access (NDMA) employs the rank test (RT) method for blind multiplicity detection, but static channel parameters throughout the retransmissions and the tight control of the transmitter phase are required. In this paper, a superior blind collision multiplicity-detection technique is introduced by combining the minimum description length criterion and a variation of the RT method. Collision resolution is accomplished through independent component analysis (ICA). Unlike blind NDMA,the new protocol, named ICA-NDMA, works in quasi-static fading channels, and does not need any phase control. Extensive performance analyses corroborate the performance advantages offered by ICA-NDMA.

Space-frequency coded (SFC) orthogonal frequency-division multiple access (OFDMA) system is considered under partial band noise jamming (PBNJ). Analytical expressions for the bit error probability (BEP) are derived for OFDMA with and without SFC in a frequency-selective fading environment. It is shown that SFC increases the resistance of OFDMA against PBNJ and reduces the BEP considerably.

A frequency-hopping (FH) space-frequency (SF)-coded clustered orthogonal frequency division multiple access (OFDMA) system is considered. It is shown that SF coding reduces the number of collisions in OFDMA with FH provided that the subcarrier clusters are formed randomly at each hopping instant. Analytical expressions for the collision rate, which is defined as the expected number of collisions per symbol, are derived. Bit error rate (BER) computations via simulations in a frequency-selective fading channel corroborate the theoretical findings for SF-coded FH-OFDMA.

When wireless sensors are used to keep an area under surveillance, a critical issue is the quality of the deployment from the sensing coverage viewpoint. In this paper, we propose several quality measures, which indicate if the deployment provides sufficient coverage, or whether redeployment is required or not. The terrain is modeled as a grid and the placement of the sensors is uniformly distributed. Neyman–Pearson detection is utilized to determine the effects of false-alarm and signal characteristics on the measures.

This paper attempts to provide an evaluation of high performance local area network type 2 (HIPERLAN/2), equipped with space-frequency coded double-antenna transmit diversity. The HIPERLAN/2 physical layer entities are simulated for frequency-selective fading channels for the standard's digital modulation schemes of BPSK, QPSK, 16-QAM and 64-QAM. Protocol data unit (PDU) error rate comparisons of HIPERLAN/2 with and without space-frequency coding reveal the potential gains and caveats. The analytical bit error probability calculations confirm that the simulation results are consistent with the theory.

Frequency-shift keying (FSK) is a popular modulation scheme in power-limited communication links. This paper introduces space-time FSK (ST-FSK), which does not require any channel state information at the transmitter and the receiver, as in conventional noncoherent FSK. ST-FSK can be viewed as a special unitary ST modulation design. However, ST-FSK has a number of advantages over existing unitary ST modulation designs. ST-FSK is easier to design, and enjoys lower decoding complexity. Furthermore, ST-FSK guarantees full diversity. Finally, ST-FSK can be adopted in the digital as well as in the analog domain, and merges very naturally with frequency-hopping multiple access. As expected, all these advantages come at the cost of a decrease in spectral efficiency.

In the frequency estimation of sinusoidal signals observed in impulsive noise environments, techniques based on Gaussian noise assumption are unsuccessful. One possible way to find better estimates is to model the noise as an alpha-stable process and to use the fractional lower order statistics (FLOS) of the data to estimate the signal parameters. In this work, we propose a FLOS-based statistical average, the generalized covariation coefficient (GCC). The GCCs of multiple sinusoids for unity moment order in SαS noise attain the same form as the covariance expressions of multiple sinusoids in white Gaussian noise. The subspace-based frequency estimators FLOS-multiple signal classification (MUSIC) and FLOS-Bartlett are applied to the GCC matrix of the data. On the other hand, we show that the multiple sinusoids in SαS noise can also be modeled as a stable autoregressive moving average process approximated by a higher order stable autoregressive (AR) process. Using the GCCs of the data, we obtain FLOS versions of Tufts-Kumaresan (TK) and minimum norm (MN) estimators, which are based on the AR model. The simulation results show that techniques employing lower order statistics are superior to their second-order statistics (SOS)-based counterparts, especially when the noise exhibits a strong impulsive attitude. Among the estimators, FLOS-MUSIC shows a robust performance. It behaves comparably to MUSIC in non-impulsive noise environments, and both in impulsive and non-impulsive high-resolution scenarios. Furthermore, it offers a significant advantage at relatively high levels of impulsive noise contamination for distantly located sinusoidal frequencies.

Due to the scarcity of the spectral resources and mobility of the portables, the call attempts may be blocked during call initiation or terminated during the hand-off process. When the blocking ratio exceeds some grade of service level, the capacity of the congested cell must be replanned using the call attempt data. However, most of the time, the measurements are inflated by the redials and the retrials. During the replanning process, the first step should be to calculate the effective load from the measured cumulative call statistics. In this paper, we provide simple-to-implement analytical models that compute the average number of retrials and redials per original call attempt using only the operations and maintenance center measurements. The effective load is then determined through appropriate scaling. That way, unnecessary waste of channels to sustain retrials and redials is avoided during the cellular capacity re-planning phase.

Robust single-user detection is employed in a direct sequence code-division multiple-access (DS-CDMA) system in which the noise process contains impulsive components. The breakdown point is computed for a mixture noise model. The bit error probability expressions are derived under a Gaussian mixture. The performance is also evaluated in the presence of power imbalance and asynchronous reception. Noise, rather than interference, is shown to be the primary obstacle in achieving good performance for certain practical signal power and user load levels. It is concluded that DS-CDMA employing a robust correlator receiver performs better than the conventional matched filter in an impulsive noise environment.

An integrated medium access control framework for a direct sequence code-division multiple access (CDMA) radio access to an asynchronous transfer mode (ATM) network is considered. The system accommodates multimedia services such as voice and data. The inherently high error rate associated with the multipath fading channel is partly overcome by the introduction of a data link control layer employing one- and two-dimensional CRC codes for error detection/correction in voice and data packets, respectively. Analysis and simulations show promising average delay and error probability performance, as well as low coding redundancy. Two-dimensional CRC, particularly in conjunction with automatic repeat request (ARQ), outperforms convolutional coding with much less overhead and processing requirement at the ATM-CDMA interface.

Distributed management of microgrids is a vital problem to provide scalability to the next-generation electricity grids. Energy management of interconnected microgrids yields an optimization problem with large number of variables. In this paper, Dantzig-Wolfe decomposition is employed to delegate the computational load to local controllers. Island and grid-connected modes of operation are compared, and it is shown that elastic loads in different microgrids display similar behavior in grid-connected mode.

We study demand response (DR), supply management and power scheduling in a smart grid in the presence of photovoltaic sources such as solar panels. We aim to increase efficiency and reliability of the grid by limiting the probability of power shortfall, which is introduced to the system by the uncertainty in the solar power production. Total social welfare, defined in terms of consumer utility and total cost of power imposed on the provider, is maximized subject to a constraint on the probability of power shortfall, which reflects the degree of deficit risk taken by the provider. Two distinct DR mechanisms are developed for optimal production and consumption scheduling during day and night. The behavior of both supply and demand sides are investigated under changing cost parameters. It is shown that the deficit-limiting approach to DR management presented in this paper outperforms a competitor that equates power generation to consumption on the average.

We consider a demand side management model in which the power provider adopts an adaptive pricing strategy that depends on fluctuations in renewable sources and consumption behavior of customers with heterogeneous marginal utilities in the smart grid. Given the adaptive pricing strategy, we formulate the power consumption behavior of customers as a repeated noncooperative game with incomplete information. We provide an explicit characterization of unique Bayesian Nash equilibrium strategy in terms of individual marginal utilities. The rational behavior is also characterized in a communication scheme where smart meters exchange consumption levels with neighboring meters. A local algorithm that computes equilibrium consumption and propagates beliefs is presented when the network is known. Simulation results show that communication is beneficial for welfare and that power provider can lower the peak-to-average ratio of total consumption by adjusting its target profit ratio.

A distributed system which comprises independent sensors and a fusion center is considered. Optimum quantization of sensor observations by Chernoff Information (CI) and deflection criterion (DC) for detection purpose is studied. Chernoff information of overall system is derived, and locally optimum quantization of sensor observations is proved to achieve global optimality. A relationship between DC and CI of quantized observations is established. A novel iterative algorithm which works similar to Lloyd-Max algorithm is proposed to find optimum quantization intervals. Results indicate that optimum 2-bit quantization gives detection performance that is close to what is achievable by collecting soft reports from sensors.

A polar coding scheme for bit-interleaved coded modulation (BICM) is proposed. Code construction is performed by extending Arikan's heuristic to parallel channels. A lower-complexity and numerically robust implementation of list decoding with cyclic redundancy check is adopted. The performance of the design methods used for the construction of proposed bit-interleaved polar coded modulation (BIPCM) system is evaluated over 16-ary quadrature-amplitude modulation with different mapping schemes. It is shown that the proposed BIPCM architecture provides significant performance advantages over the BICM schemes implemented with other well-known codes for moderate block lengths.

We propose an adaptive random access algorithm for mobile environments with capture. The algorithm has a dynamic window size which is updated according to the traffic intensity. The packet arrival rate is tracked by employing sequential detection procedures. Simulations indicate that reducing the window size adaptively under heavy load conditions improves the average delay significantly. Copyright © 2001 John Wiley & Sons, Ltd.

We consider a mobile radio random-access algorithm equipped with a choice of three distinct antenna diversity plans (two-fold, maximum selection, or ideal selection diversity) to improve the performance. The algorithm is stable with high throughput and low delays. The mobile environment is regulated by the presence of multipath fading, shadowing, and path loss. The capture model is a threshold model based on the signal-to-interference ratio. The maximum throughput and optimal window size of the algorithm are analytically computed. The delay performance is evaluated through simulations, and significant improvement is demonstrated over slotted ALOHA under various mobile scenarios.

This paper considers discrete-time distributed detection of a constant, weak signal in dependent noise. A number of sensors transmit binary decisions to a central processor, which assimilates them to produce a "nal verdict. Asymptotic relative e$ciency (ARE) performance measure is used to compare the decentralized and centralized detection schemes. The precise de"nitions of ARE-robustness and asymptotic ARE-robustness are introduced, and utilized to understand better the trade-o!s involved in multiple-sensor detection in the presence of dependent noise. ( 1999 Elsevier Science

The main functionality of a surveillance wireless sensor network is to detect unauthorized traversals in a field. In this paper, we develop a formulation to determine the probability of detecting a randomly positioned target by a set of binary sensors to serve as the deployment quality measure. This formulation leads to a recipe to determine the number of sensors required to deliver a certain deployment quality level. The sensing- and communication-neighbor degrees which can be used as design criteria in a sensor network are defined and calculated. The model. is verified by simulations whose outcomes closely match the analytical results.

We consider a distributed system where sensors make location parameter estimates using their observations. A central processor collects the local estimates and declares a final estimate based on them. We present a simple study of the convergence properties of three structures where empirical mean and M-estimates are used in various combinations. It is shown that when occasional outliers exist, decentralized estimators that provide robustness at stages where data corruption occurs perform superiorly.

The multiple access problem as characterized by infinite user population and a slotted-time channel is examined, and an algorithm that utilizes a control mini-slot is proposed. The stability region of the proposed algorithm is determined and compared to the random access algorithm with the highest known throughput. A break-even point is also given.

We introduce the design of a class of code sequences {en(m), n = 1, 2,... } with memory level m = 1,2,... based on the channel polarization idea, where {en(1)} coincides with the polar codes presented by Arikan in [1]. The new codes achieve the symmetric capacity of arbitrary binary-input discrete memoryless channels. We derive bounds on the polarization performance as scaled with m. We show that {en(m)} offers monotonically decreasing encoding and decoding complexity with growing m.

In this paper, we study demand response management and power scheduling in a smart grid with renewable energy sources represented by a wind farm. Total social welfare, defined in terms of consumer utility and cost of energy production (both scheduled and renewable), is maximized while the probability of energy shortfall due to the uncertainty in renewable production is limited by an upper bound. We find the outage-limiting optimal conventional energy production and consumption schedules, and investigate the impact of the statistical relationship between the turbines in a wind farm.

Two fundamental distributed decision network structures are considered: the first system consists of finite number of sensors, each collecting asymptotically many data, while the second one employs asymptotically many sensors, each collecting a single datum. For binary hypothesis testing, the Neyman-Pearson criterion is utilized and justified via information theoretic arguments. An asymptotic relative efficiency performance measure is used to establish tradeoffs between the two structures, by comparing the performance characteristics of the decentralized detection systems to their centralized counterparts. >

Noise Modeling and OFDM Based Receiver Design in Power-Line Communication Electromagnetic interference, man-made noise and multi-path effects are the main causes of bit errors in power line communication (PLC). In this paper, frequency domain characteristics of the power-line noise are investigated. The noise amplitude distribution of each individual frequency in the spectrum is analyzed and a suitable noise amplitude distribution model is proposed. A soft-demodulation based receiver for the proposed noise model is derived and its performance is analyzed. In simulations, Zimmermann and Dostert's wellknown PLC channel model is used. Correctness of the channel simulations is verified by measurements. Orthogonal Frequency Division Multiplexing (OFDM) with convolutional coding and soft-Viterbi decoding is used to evaluate the performance of the receiver under the proposed noise model in terms of the bit-error-rate (BER) for different signal-to-noise ratio values. It is shown that instead of using a Gaussian optimal receiver in an OFDM system for the whole spectrum, utilizing Generalized Gaussian Distribution based receivers with appropriate shape information for each individual subchannel significantly increases BER performance.

This paper presents several formulations for aggregating opinions about the outcome of a future event-e.g., opinions in the form of probabilities are aggregated into a single probability. The approach used to derive these aggregation formulas is maximum entropy inference, with the assumption that the opinions and the event being predicted are joint random variables. As part of the presentation, the novel, axiomatically developed aggregation formula due to Bordley is used as a foil for contrasting and comparing the results developed here. It is shown that Bordley's result is overly restrictive when looked at in terms of maximum entropy derivations that are clearly using more information. Initially, we suppose that the opinions given on the future event are in the form of odds; then we consider the case where these opinions are in the form of probabilities; and finally, we reach a general formulation in which they could be odds, probabilities or even binary, yes/no, predictions of a future event with interactions between experts. In regard to this last point, the use of Darroch and Ratcliff's method in maximum entropy aggregation is sketched. >

We consider a decentralized hypothesis testing structure with asymptotically many sensors, each collecting a single datum. The sensors deploy robust test functions that are designed for outlier classes of hypotheses. The sensor outputs are transmitted to the fusion center for the global decision. In this paper, we concentrate on sensor-level decision making, and study the asymptotic performance of the decentralized detection scheme described above. In particular, we utilize the asymptotic relative efficiency performance measure, defined as the ratio of the number of sensors needed by the decentralized structure over the number of data needed by the centralized one to attain the same performance level. Our results indicate the superiority and the necessity of using robust statistical techniques.

Two mutually independent traffic streams whose arrivals are modeled as renewal processes are considered. The arrivals are stored in an infinite capacity buffer. One of the processes requires a strict upper bound on the total delay per arrival, and it is assumed that the processing time per arrival for both processes is a constant. Given the above model, the class of nonpreemptive scheduling policies which satisfy the delay constraint while maintaining the system stability is found. The delays induced by the latter policy are analyzed via a methodology based on the regenerative theorem. A numerical example is given in which the policy that minimizes the expected delays for both traffic streams is also considered. >