Search

In this paper, we extend the bilinear generalized approximate message passing (BiG-AMP) approach, originally proposed for high-dimensional generalized bilinear regression, to the multi-layer case for the handling of cascaded problem such as matrix-factorization problem arising in relay communication among others. Assuming statistically independent matrix entries with known priors, the new algorithm called ML-BiGAMP could approximate the general sum-product loopy belief propagation (LBP) in the high-dimensional limit enjoying a substantial reduction in computational complexity. We demonstrate that, in large system limit, the asymptotic MSE performance of ML-BiGAMP could be fully characterized via a set of simple one-dimensional equations termed state evolution (SE). We establish that the asymptotic MSE predicted by ML-BiGAMP’ SE matches perfectly the exact MMSE predicted by the replica method, which is well-known to be Bayes-optimal but infeasible in practice. This consistency indicates that the ML-BiGAMP may still retain the same Bayes-optimal performance as the MMSE estimator in high-dimensional applications, although ML-BiGAMP's computational burden is far lower. As an illustrative example of the general ML-BiGAMP, we provide a detector design that could estimate the channel fading and the data symbols jointly with high precision for the two-hop amplify-and-forward relay communication systems. [ABSTRACT FROM AUTHOR]

In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless i communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on particle filters. We call this approach "channel-aware particle filtering." Channel-aware particle filtering schemes are derived for different wireless channel models and receiver architectures. Furthermore, we derive the posterior Cramér-Rao lower bounds (PCRLBs) for our proposed channel-aware particle filters. Simulation results are presented to demonstrate that the tracking performance of the channel-aware particle filters can reach their theoretical performance bounds even with relatively small number of sensors and they have superior performance compared to channel-unaware particle filters. [ABSTRACT FROM AUTHOR]

Impulse radio (IR) and especially time-hopping IR (TH-IR) systems have attracted considerable attention since their introduction in the context of ultra wideband (UWB) systems in the early 1990s. Recently, with the U.S. Federal Communications Commission actions allowing for the wide-spread use of UWB radio systems, the interest in these systems has grown further. These systems promise to deliver high data rates in multiple access communication channels with very simple transmitter and receiver designs. The importance of multiuser detection for achieving high data rates with these systems has already been established in several studies. This paper studies several low-complexity multiuser detectors specifically designed for TH-IR radio systems. It is demonstrated that many multiuser detectors developed primarily for direct-sequence code division multiple access (DSCDMA) systems can be used essentially without any change in TH-IR receivers. Further, several novel, low-complexity multiuser detectors that exploit the special signal structure used for transmission by TH-LR systems are developed. These novel detectors are analyzed both theoretically and via simulations. It is shown that a very simple iterative multiuser detector yields performance that is similar to that of a single-user system. [ABSTRACT FROM AUTHOR]

We study the problem of communicating sensor readings over a Gaussian multiaccess channel. We focus on the scenario that each sensor observes a single random variable and transmits it using certain signaling in a shared channel. The objective is the design of channel waveforms (i.e., the signal constellation) to facilitate the estimation of field parameters from the channel output. We propose a communication scheme in which sensors transmit according to the type of their observations—type-based multiple access (TBMA)—and show that the TBMA is asymptotically optimal in the limit of large number of sensors if the sensor channel-gains are identical. In particular, we show that TBMA together with a variant of the maximum-likelihood estimator achieves the Cramer-Rao bound asymptotically. We then extend the asymptotic analysis of TBMA to fading channels and compare the performance of TBMA with other orthogonal allocation methods such as time-division multiple access. [ABSTRACT FROM AUTHOR]

We apply maximal asymptotic conditional efficiency joint successive interference cancellation (MACE-JSIC) to frequency-selective channel equalization and propose improvements that extend MACE-JSIC detection to the broader paradigm of time-varying and imprecisely known communication systems. The need for expensive redesign to include any change in the interfering signals is a fundamental limitation of MACE-JSIC detection despite its high performance and low detection complexity. In this work, we derive sufficiency criteria that reduce maximum asymptotic efficiency (MAE) detection to low-complexity decorrelation and exploit this relationship to extend the MACE-JSIC approach to time-varying communication systems. Simulations on several multi-user systems and frequency-selective channels demonstrate that the performance of the proposed detectors is consistent with theoretical expectations. [ABSTRACT FROM PUBLISHER]

We propose a scheme for rate-constrained distributed nonparametric regression using a wireless sensor network. The scheme is universal across a wide range of sensor noise models, including unbounded and nonadditive noise; it has low complexity, requiring simple operations such as uniform scalar quantization with dither and message passing between neighboring nodes in the network, and attains minimax optimality for regression functions in common smoothness classes. We present theoretical results on the tradeoff between the compression rate, communication complexity of encoding, and the MSE and demonstrate empirical performance of the scheme using simulations. [ABSTRACT FROM AUTHOR]

We develop beamforming algorithms for information relaying over shared slowly nonselective fading channels in wireless sensor networks. We assume that, prior to beamforming their received data to a destination, the relays preprocess them by either data amplifying or decoding. The beamforming weights are broadcasted by the destination to the relays and are formed based on the individual relay-destination channel coefficients and an m-bit description of the quality of each source-relay channel. For both relay data-preprocessing models, we present methods for optimizing the m-bit quantizer employed at each relay for encoding its source-relay channel quality level, and for choosing the beamforming weights at the destination, so as optimize the destination uncoded bit error rates. As our simulations and analysis reveal, a coarse single-bit description of each source-relay channel coefficient at the destination may suffice, as it results in only a small increase in uncoded bit error rates with respect to the case where full knowledge of the source-relay channel coefficients are exploited at the destination. [ABSTRACT FROM AUTHOR]

We introduce a new family of detectors for multiple-input multiple-output (MIMO) channels. These detectors are called Chase detectors because they can be interpreted as a translation of the Chase error-control decoding algorithm from time to space. The Chase detector is parameterized by only four parameters; nevertheless, it reduces to a wide range of previously reported MIMO detectors as special cases, including the maximum-likelihood and decision-feedback detectors. The Chase detector defines a simple framework for not only comparing existing MIMO detection algorithms but also proposing new ones. For example, based on the Chase framework, we propose a new detector called B-Chase that performs well on fading channels. Specifically, on a four-input four-output Rayleigh-fading channel with uncoded 16-QAM inputs, one instance of the B-Chase detector falls only 0.4 dB short of the performance of the maximum-likelihood sphere detector while reducing complexity by 68%. Another instance of the B-Chase detector outperforms the BLAST-ordered decision-feedback detector by 4.4 dB while increasing complexity by only 17%. [ABSTRACT FROM AUTHOR]

We describe distributed tracking of a nonlinear dynamical system via networked sensors. The sensors communicate with each other by means of a multihop protocol over a communication network. We derive in-network processing algorithms to deal with arbitrary network topology and then extend these results to account for communication delays and packet losses. We show that these algorithms are optimal in the linear setting and achieve centralized performance. The proposed techniques differ from existing techniques in two important aspects: a) there is no designated leader/fusion node and each sensor attempts to optimally track the system trajectory based on its local observations and time-dependent information available from other sensors in the network; b) the message computation at each sensor is structurally identical. Consequently, the sensor network can be queried at any time and at any node to obtain optimal estimates for the state of the dynamical system. We then present two multihop protocols—one based on gossip and another token-based—for distributed implementation of the in-network processing techniques. We show several advantages of token-based schemes over gossip protocols: a) message complexity is significantly smaller for achieving the same performance; b) they are well-suited for situations where target and network data aggregation time-scales are comparable; and c) they are well-suited for random geometric graphs with nodes having small communication-connectivity radius—a scenario typical of ad-hoc wireless networks. This is because they can fuse data only from the set of nodes that can be visited in any time period. [ABSTRACT FROM AUTHOR]