Haralabos C. Papadopoulos

Sequential signal encoding from noisy measurements using quantizers with dynamic bias control

Signal estimation from a sequential encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an information-bearing signal from low-bandwidth digitized information received from remote sensors, and may or may not broadcast feedback information to the sensors. We demonstrate that the use of an appropriately designed and often easily implemented additive control input before signal quantization at the sensor can significantly enhance overall system performance. In particular, we develop efficient estimators in conjunction with optimized random, deterministic, and feedback-based control inputs, resulting in a hierarchy of systems that trade performance for complexity.

Distributed computation of averages over ad hoc networks

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with arbitrary but fixed connectivity. The algorithms we develop are linear dynamical systems that generate sequences of improving approximations to the desired computation at each node, via iterative processing and broadcasting. The algorithms are locally constructed at each node by exploiting only locally available and macroscopic information about the network topology. We present methods for optimizing the convergence rates of these algorithms to the desired computation, and evaluate their performance characteristics in the context of a problem of signal estimation from multinode noisy observations. By conducting simulations based on simple power-loss propagation models, we perform a preliminary comparison of the algorithms we develop against other types of distributed algorithms for computing averages, and identify transmit-power optimized algorithmic implementations as a function of the size and density of the sensor network.

Locally constructed algorithms for distributed computations in ad-hoc networks

In this paper we develop algorithms for distributed computation of a broad range of estimation and detection tasks over networks with arbitrary but fixed connectivity. The distributed algorithms we develop are linear dynamical systems that generate sequences of approximations to the desired computation. The algorithms are locally constructed at each node by exploiting only locally available and macroscopic information about the network topology. We present methods for designing these distributed algorithms so as to optimize the convergence rates to the desired computation and demonstrate their performance characteristics in the context of a problem of signal estimation from multi-node signal observations in Gaussian noise.

Techniques for Multi-User MIMO with Two-Way Training

We consider forward-link multiuser MIMO transmission, whereby K users are served by a base station with a large number of (potentially distributed) transmit antennas. We address the problem of channel state information (CSI) mismatch between the transmitter and the receivers with a two-way training scheme. In a quasi-static setting, the proposed scheme requires only K pilot symbols for uplink training and as little as one symbol for downlink training. In particular, we show that the performance of the system where the receiver has perfect channel knowledge can be approached with a small value of downlink training symbols. We consider two variants of linear MMSE precoders that take into account the quality of the channel state information at the transmitter and demonstrate that robust high sum-rate systems can be designed that rely on single-antenna receivers, provided that a large enough number of antennas is used for transmission. Finally, we sketch an example of implementations for the equal rate case.

Beamforming Algorithms for Information Relaying in Wireless Sensor Networks

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.

Physical-layer secrecy in AWGN via a class of chaotic DS/SS systems: analysis and design

We study a class of pseudo-chaotic spread spectrum systems for secure communication over additive white Gaussian noise (AWGN) channels, whereby a symbol stream is linearly modulated on a spreading sequence generated by iterating an initial condition through a suitably chosen chaotic map. We compare the uncoded probability of error (Pr(/spl epsiv/)) attainable by intended receivers that know the initial condition to the associated Pr(/spl epsiv/) of unintended receivers that know the modulation scheme but not the initial condition. The sensitive dependence of chaotic sequences on initial conditions, together with the presence of channel noise, can be exploited to provide substantially lower Pr(/spl epsiv/) to intended than to unintended receivers. We develop computationally efficient methods for obtaining tight bounds on the best P r(/spl epsiv/) performance of intended and unintended receivers. In the process, we identify chaotic map attributes that affect the relative Pr(/spl epsiv/) advantages provided to intended receivers and develop methods for designing maps that achieve a target gap between the intended and unintended receiver Pr(/spl epsiv/).

Sequential signal encoding and estimation for distributed sensor networks

We develop algorithms for sequential signal encoding from sensor measurements, and for signal estimation via fusion of channel-corrupted versions of these encodings. For signals described by state space models, we present optimized sequential binary-valued encodings constructed via threshold-controlled scalar quantization of a running Kalman filter signal estimate from the sensor measurements. We also develop methods for robust fusion from observations of these encodings corrupted by binary symmetric channels.

Distributed source localization algorithms for acoustic ad-hoc sensor networks

In this paper, we develop and evaluate distributed implementations of source localization estimators from energy-based measurements obtained via an ad-hoc network of acoustic sensors. The distributed locally constructed algorithms that we present produce at each node a sequence of estimates approximating a desired source localization algorithm. As our investigation reveals, the localization performance of these distributed algorithms depends on the type of desired localization algorithm, the network topology and the number of communication and fusion steps employed in these approximations.

Reduced-Complexity Adaptive Receiver Algorithms for 4G SU-MIMO Systems

In this paper we present reduced-complexity high- performance receivers for single-user MIMO systems that employ coded OFDM transmission with bit-interleaved coded modulation (BICM). High data-rate versions of these systems have been proposed for 4G. The near-optimum inner-outer decoder structures for these MIMO/BICM/OFDM/QAM systems have complexity that grows exponentially fast with the number of multiplexed streams (number of transmit antennas) and the number of bits represented by the QAM constellations. We focus on reduced-complexity inner-outer decoder structures that exploit adaptive soft-output M algorithms for soft inner decoding. Our simulations show that these receivers can be systematically optimized to yield substantial complexity reductions without significant loss in performance.

Physical-layer secrecy with DS/SS from piecewise linear chaotic Markov maps: analysis and design

We study a class of pseudo-chaotic spread spectrum systems for secure communication over additive white Gaussian noise channels, whereby a symbol stream is linearly modulated on a sequence generated by iterating an initial condition through a suitably chosen chaotic map. We compare the optimal uncoded probability of error (Pr(/spl epsi/)) attainable by intended receivers that know the initial condition, to the associated Pr(/spl epsi/) of unintended receivers that know the modulation scheme but not the initial condition. The sensitive dependence of chaotic sequences on initial conditions together with the presence of channel noise can be exploited to provide Pr(/spl epsi/) benefits to intended receivers. We develop bounds on the best Pr(/spl epsi/)performance of intended and unintended receivers. In the process, we identify properties of the chaotic maps that affect the secrecy benefits and develop methods for designing maps that meet a required level of secrecy.