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Probabilistic constellation shaping (PCS) has attracted a lot of attention for increasing the spectral efficiency (SE) in coherent fiber optic communication. Unfortunately, increasing the transmit power could increase non-linear self- and cross-phase modulations due to the Kerr effect in fiber optic. Owing to smaller peak-to-average power ratio (PAPR), higher angular distance between constellation points, and lower amplitude levels, amplitude and phase shift keying (APSK) can be more robust to phase noise than quadrature amplitude modulation (QAM). A new PCS scheme is proposed for the standard APSK 16 and 64 DVB-S2/S2X modulation formats. Simulation results in additive white gaussian noise (AWGN) channels show a close bit error rate performance for the proposed system compared to probabilistically-shaped QAM.

This paper studies the diversity-multiplexing trade-off (DMT) of half-duplex diamond relay channels. Diversity and multiplexing gains usually evaluate the performance of any relaying strategy, in wireless relay networks with fading channels. DMT captures the fundamental trade-off between these two gains, for communications at high signal to noise ratios (SNR). In this paper, we propose an efficient algorithm that computes the DMT of static-QMF communication strategy in half-duplex diamond relay channels. To find DMT of static QMF, one needs to solve a cumbersome optimization problem. The proposed algorithm, breaks the given optimization to finite1 number of sub-optimization problems, each one of which is a quasi-concave optimization problem. Then, using a binary search method, each sub-optimization problem is solved efficiently and the results of sub-optimization problems are combined to find DMT of the static QMF communication in the network.

We study the diversity-multiplexing trade-off (DMT) of diamond relay channels. A diamond network contains a transmitter, two relays and a receiver. DMT of a network represents a fundamental trade-off between the reliability and rate of transmission with the diversity and multiplexing gains for wireless networks with fading channels. In this paper, we compute the DMT of full-duplex diamond relay channels and find the DMT of QMF communication strategy for half-duplex diamond relay channels. Also, a new genetic algorithm is introduced to solve the DMT of static QMF strategy for half-duplex diamond relay channels. By simulation, it is shown than the running time of the genetic algorithm is much better than the running time of finding the DMT of SQMF using grid based optimization techniques. The novelty of the proposed genetic algorithm is in using creative crossover and mutation operators in the algorithm which improved the results.

In this paper, we address the problem of direction-of-arrival (DOA) estimation using a novel spatial sampling scheme based on difference set (DS) codes, called DS-spatial sampling. It is shown that the proposed DS-spatial sampling scheme can be modeled by a deterministic dictionary with minimum coherence. We also develop a low complexity compressed sensing (CS) model for DOA estimation. The proposed methods can reduce the number of array elements as well as the number of receivers. Compared with the conventional DOA estimation algorithm, the proposed sampling and processing method can achieve significantly higher resolution.

The diversity-multiplexing trade-off (DMT) expresses the optimal trade-off between the transmission rate and the error probability for communications at high signal to noise ratios (SNR) in wireless networks with fading channels. For half-duplex single relay networks with quasi-static fading channels, if the average signal to noise ratio (SNR) of the source-relay (S-R) link is equal to the average SNR of the relay-destination (RD) link, quantize-map-and-forward (QMF) and dynamic decode-and-forward (DDF) strategies are DMT optimal at high and low multiplexing gains, respectively. In this paper, we show that DDF and SQMF strategies are not generally DMT optimal in half-duplex single relay networks when the average SNR of the S-R link and R-D link are not equal. We show that DMT of dynamic QMF strategy is strictly greater than DMT of DDF and static QMF strategies for a specific half-duplex single relay network at certain multiplexing gains.

We consider computing the capacity of half-duplex relay networks with orthogonal channels. In such networks, if the network has a layered structure, we show that the capacity can be computed in polynomial using the ellipsoid method. Moreover, for networks with local connectivity, such that the connectivity radius is a constant independent of size of the network, a polynomial time algorithm is presented to compute the capacity.

A new communication scheme for Gaussian diamond relay networks based on superposition coding and partial decoding at the relays is presented. A first level codebook is constructed with symbols chosen from a binary alphabet while a second level codebook is composed of codewords chosen with Gaussian symbols. The relays partially decode the message to identify when to amplify the incoming signals. The new communication scheme is a generalization of decode-and-forward, amplify-and-forward, and bursty-amplify-and-forward. The achievable rates are studied in the asymptotic low SNR regime. It is shown that the new scheme outperforms all previously known schemes for some channels and parameter ranges.

Very often, constrained coding schemes impose nonlinear constraints on arrays and therefore it is usually nontrivial to determine how many arrays satisfy the given constraints. In this paper we show that there are non-trivial constrained coding scenarios where the number of constrained arrays can be estimated to a surprisingly high accuracy with the help of the sum-product algorithm, despite the fact that the underlying factor graphs have many short cycles, and we investigate the reasons why this is the case. These findings open up interesting possibilities for determining the number of constrained arrays also for scenarios where other counting and bounding techniques are not readily available.

When data traffic in a wireless network is bursty, small amounts of data sporadically become available for transmission, and the energy cost associated with synchronizing the network nodes prior to each communication block is not negligible. Therefore, designing energy-efficient communication schemes for such asynchronous scenarios is of particular importance. In this paper, we show that, for symmetric diamond networks, by performing the tasks of synchronization and communication separately, it is possible to achieve the minimum energy-per-bit to within a factor that ranges from 2 in the synchronous case to 1 in the highly asynchronous regime.

The capacity or approximations to capacity of various single-source single-destination relay network models has been characterized in terms of the cut-set upper bound. In principle, a direct computation of this bound requires evaluating the cut capacity over exponentially many cuts. We show that the minimum cut capacity of a relay network under some special assumptions can be cast as a minimization of a submodular function, and as a result, can be computed efficiently. We use this result to show that the capacity, or an approximation to the capacity within a constant gap for the Gaussian, wireless erasure, and Avestimehr-Diggavi-Tse deterministic relay network models can be computed in polynomial time. We present some empirical results showing that computing constant-gap approximations to the capacity of Gaussian relay networks with around 300 nodes can be done in order of minutes.

Consider the set A n of all n×n binary matrices in which the number of 1's in each row and column is at most n/2. We show that the redundancy, n2 − log 2 ∣An∣, of this set equals ρn + o(n), for a constant ρ ≈ 1.42515.

The 60 GHz band is an unlicensed band which features a wide communication bandwidth and a low probability of intercept due to high oxygen absorption. The wide bandwidth allows for a high volume of information to be transmitted wirelessly over a short interval of time. The signal confinement in both time and space makes the 60 GHz band an ideal candidate for a covert Body Area Network (BAN). Current and future soldiers can attain enhanced mobility and increased survivability by taking advantage of advancements in V-band WLAN technology. In this paper, we study the 60 GHz propagation conditions associated with a BAN, optimized for interconnecting various subsystems on a soldier. The 60 GHz wireless channel model study presented includes both human and pig measurement results and analytical channel simulations using ray tracing methods. Measurements were made using three different antennas—horn, slot and patch. The characterization of the channel is provided through path loss data and in some cases, S-parameter data from each node at different receiver points on a soldier's body and gear.

We consider the deterministic construction of a measurement matrix and a recovery method for signals that are block sparse. A signal that has dimension N = nd, which consists of n blocks of size d, is called (s, d)-block sparse if only s blocks out of n are nonzero. We construct an explicit linear mapping Phi that maps the (s, d) -block sparse signal to a measurement vector of dimension M, where s - d < N (1- (1- M/N)d/d+1) - o(1). We show that if the (s,d)- block sparse signal is chosen uniformly at random then the signal can almost surely be reconstructed from the measurement vector in O(N3) computations.

DNA microarrays comprising tens of thousands of probe spots are currently being employed to test multitude of targets in a single experiment. Typically, each microarray spot contains a large number of copies of a single probe designed to capture a single target, and hence collects only a single data point. This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. Since only a small fraction of the total number of genes represented by the two samples is differentially expressed, a vast number of probe spots will not provide any useful information. To this end we consider an alternative design, the so-called compressed microarrays, wherein each spot is a composite of several different probes and the total number of spots is potentially much smaller than the number of targets being tested. Fewer spots directly translates to significantly lower costs due to cheaper array manufacturing, simpler image acquisition and processing, and smaller amount of genomic material needed for experiments. To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. Moreover, we propose an algorithm which has far less computational complexity than the widely-used linear-programming-based methods, and can also recover signals with less sparsity.

Currently, DNA micro arrays comprising tens of thousands of probe spots are employed to test entire genomes in a single experiment. Typically, each microarray spot contains a large number of copies of a single probe, and hence collects only a single data point. This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. Since only a small fraction of the total number of genes represented by the two samples is differentially expressed, a large fraction of a microarray does not provide any useful information. To this end, in this paper we consider an alternative microarray design wherein each spot is a composite of several different probes, and the total number of spots is potentially much smaller than the number of genes being tested. Fewer spots directly translates to significantly lower costs due to cheaper array manufacturing, simpler image acquisition and processing, and smaller amount of genomic material needed for experiments. To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. Experimental verification of the proposed methodology is presented.

We introduce a new family of error-correcting codes that have a polynomial-time encoder and a polynomial-time list-decoder, correcting a fraction of adversarial errors up to /spl tau//sub M/ = 1 - /sup M+1//spl radic/(M/sup M/R/sup M/) where R is the rate of the code and M /spl ges/ 1 is an arbitrary integer parameter. This makes it possible to decode beyond the Guruswami-Sudan radius of 1 /spl radic/R for all rates less than 1/16. Stated another way, for any /spl epsiv/ > 0, we can list-decode in polynomial time a fraction of errors up to 1 - /spl epsiv/ with a code of length n and rate /spl Omega/(/spl epsiv//log(1//spl epsiv/)), defined over an alphabet of size n/sup M/ = n/sup O(log(1//spl epsiv/))/. Notably, this error-correction is achieved in the worst-case against adversarial errors: a probabilistic model for the error distribution is neither needed nor assumed. The best results so far for polynomial-time list-decoding of adversarial errors required a rate of O(/spl epsiv//sup 2/) to achieve the correction radius of 1 - /spl epsiv/. Our codes and list-decoders are based on two key ideas. The first is the transition from bivariate polynomial interpolation, pioneered by Sudan and Guruswami-Sudan [1999], to multivariate interpolation decoding. The second idea is to part ways with Reed-Solomon codes, for which numerous prior attempts at breaking the O(/spl epsiv//sup 2/) rate barrier in the worst-case were unsuccessful. Rather than devising a better list-decoder for Reed-Solomon codes, we devise better codes. Standard Reed-Solomon encoders view a message as a polynomial f(X) over a field F/sub q/, and produce the corresponding codeword by evaluating f(X) at n distinct elements of F/sub q/. Herein, given f(X), we first compute one or more related polynomials g/sub 1/(X), g/sub 2/(X), ..., g/sub M-1/(X) and produce the corresponding codeword by evaluating all these polynomials. Correlation between f(X) and g/sub i/(X), carefully designed into our encoder, then provides the additional information we need to recover the encoded message from the output of the multivariate interpolation process.

A soft-decision decoding algorithm for Reed- Solomon codes was recently proposed in (2). This algorithm converts probabilities observed at the channel output into algebraic interpolation conditions, specified in terms of a multiplicity matrix M. Koetter-Vardy (2) show that the probability of decoding failure is given by h{SM 5 A(M)}, where SM is a random variable and A(M) is a known func- tion of M. They then compute the multiplicity matrix M that maximizes the eqected value of SM. Here, we attempt to directly minimize the overall probability of decoding fail- ure PT{SM < A(M)}. First, we recast this optimization problem into a geometrical framework. Using this frame- work, we derive a simple modification to the KV algorithm which results in a provably better multiplicity assignment. Alternatively, we approximate the distribution of SM by a Gaussian distribution, and develop an iterative algorithm to minimize Pr{sM 5 A(M)} under this approximation. This leads to coding gains of about 0.20dB for RS codes of length 255 and up to 0.75dB for RS codes of length 15, as compared to the Koetter-Vardy algorithm.