With the goal of enabling ultrareliable and low-latency wireless communications for industrial Internet of Things (IIoT), this paper studies the use of energy-based modulations in noncoherent massive single-input multiple-output (SIMO) systems. We consider a one-shot communication over a channel with correlated Rayleigh fading and colored Gaussian noise, in which the receiver has statistical channel state information (CSI). We first provide a theoretical analysis on the limitations of unipolar pulse-amplitude modulation (PAM) in systems of this kind, based on maximum likelihood detection. The existence of a fundamental error floor at high signal-to-noise ratio (SNR) regimes is proved for constellations with more than two energy levels, when no (statistical) CSI is available at the transmitter. In the main body of the paper, we present a design framework for quadratic detectors that generalizes the widely-used energy detector, to better exploit the statistical knowledge of the channel. This allows us to design receivers optimized according to information-theoretic criteria that exhibit lower error rates at moderate and high SNR. We subsequently derive an analytic approximation for the error probability of a general class of quadratic detectors in the large array regime. Finally, we numerically validate it and discuss the outage probability of the system.

This paper explores the advantages of incremental redundancy hybrid-ARQ schemes for covert communications in which a feedback channel is available. The use of efficient incremental redundancy with sub-codewords of different sizes provides significant gains in terms of covertness while preserving the error rate performance. The convexity of covertness metrics is exploited to design the size and degree profile of each block when a concatenated code based on MDNL codes is employed in the binary symmetric channel.

This paper presents an explicit channel code design for covert communications in the Binary Symmetric Channel. The proposed architecture is composed of the serial concatenation of a linear code and a graph-based non-linear code baptized as moderate-density non-linear code. This non-linear code encompasses pulse position modulation as a particular case. In order to guarantee covertness, a scrambling sequence that is not known to the warden is employed.

This paper aims at computing tight upper bounds for the maximum a posteriori threshold of low-density parity check codes in the asymptotic blocklength regime for the transmission over binary-input memoryless symmetric-output channels. While these bounds are already known, we propose a novel derivation based on a completely different approach: based solely on the concept of the chain rule and the conditional entropy, resorting to the concentration theorem for the code ensemble to compute the syndrome entropy with low complexity employing density evolution.

We propose a scheme for Compressed Sensing in the noiseless setting that reconstructs the original signal operating on a binary graph where the samples are obtained sequentially. The proposed scheme has an affordable computational complexity and a large performance enhancement with respect to similar schemes in the literature, thanks to the proposed measurement matrix structure and enhanced decoding based on a message passing algorithm.

In this paper, the authors propose a variable length source code that achieves very good performance even for short sequences and has low encoding complexity, although it does not achieve the theoretical limit. Furthermore, the performance can be predicted for infinite codeword length with close form expressions, and has been proven to be good even when a suboptimum decoder with very low complexity is employed at the decoder side.

We investigate the performance of a discrete-time all-analog-processing joint source-channel coding system for the transmission of memoryless sources over average power constrained AWGN channels. First, N:1 bandwidth compression systems are analyzed and optimized. At the encoder, N samples of an i.i.d. source are directly mapped into one channel symbol using a non-linear curve. Different from previous work in the literature, we introduce an additional degree of freedom at the encoder, MMSE decoding instead of ML decoding is considered, and we focus on both high and low channel signal-to-noise ratio (CSNR) regions. By using MMSE decoding, the proposed system presents a performance very close to the theoretical limits, even at low CSNR, as long as the system parameters are properly optimized. Then, N:K bandwidth compression systems are constructed by parallel combination of an M:1 system and a 1:1 uncoded system, and the optimal power allocation between the two constituent systems is derived in order to maximize the overall output signal-to-distortion ratio (SDR). Finally, 1:2 bandwidth expansion systems using mapping functions similar to those used in 2:1 system are investigated. Different from digital systems, the proposed scheme does not require long block lengths to achieve good performance, and shows graceful degradation when the CSNR is lower than the one used for the design.

This paper introduces a joint bit loading and power allocation algorithm for systems combining bit-interleaved coded modulation (BICM) with multicarrier transmission. The proposed algorithm maximizes the mutual information, so it can be regarded as a generalization of mercury/waterfilling policy that incorporates bit loading. The followed approach relies on irregular modulation and power to cast the problem in the framework of convex optimization. This allows to derive the optimum solution without resorting to greedy algorithms, embedding the bit loading in the definition of an equivalent constellation such that the complexity increase with respect to mercury/waterfilling is negligible. While irregular modulation plays a key role in algorithm definition, it is proved that only a few subcarriers employ it and it is shown that a practical low complexity algorithm can be obtained with minimal losses that does not use irregular modulation.

We investigate the performance of a discrete-time all-analog-processing joint source channel coding system for the transmission of i.i.d. Gaussian and Laplacian sources over AWGN channels. In the encoder, two samples of an i.i.d. source are mapped into a channel symbol using a space-filling curve. Different from previous work in the literature, MMSE decoding instead of ML decoding is considered, and we focus on both high and low channel SNR regions. The main contribution of this paper is to show that the proposed system presents a performance very close to the theoretical limits, even at low SNR, as long as the curve parameters are properly optimized.

This letter focuses on the design of an adaptive bit-interleaved coded modulation (BICM) scheme for frequency selective slow fading channels where the transmitter has certain knowledge of the channel response. In particular, we consider the design of the interleaver stage for a specific convolutional code operating with OFDM modulation. The adaptive interleaver uses the puncturing tables of the rate-compatible punctured convolutional codes (RCPC codes) to rearrange the bits as a function of the fading values and the specific constellation. The performance of different interleavers are compared, revealing that the adaptive RCPC-based interleaver produces larger Euclidean distances between the received codewords and reduces the packet error rate (PER), specially when the number of deep-faded subcarriers increases. Numerical results also evidence the importance of the interleaver choice when comparing the performance of different power allocation strategies.

Waterfllling and mercury/waterfilling power allocation policies maximize the mutual information of independent parallel Gaussian-noise channels under an average power constraint for Gaussian signals and for practical discrete constellations respectively. In this paper, the same criterion is considered for joint power allocation and bit loading in more general scenarios where suboptimum receivers introduce a mutual information loss. The proposed approach provides the optimum solution without resorting to greedy algorithms and has nearly the same complexity as the original mercury/waterfilling.

We introduce a layered scheme based on multilevel codes and random labeling of the MIMO constellation that attains constellation-constrained outage capacity in quasi-static fading channels. The proposed approach has the desirable features of using binary codes, not requiring demapper iterations and not applying constellation-expanding linear transformations in the complex field

The performance of downlink coded cooperative transmission in mobile communication systems is evaluated. Cooperative transmission is seen as a "virtual" MIMO system where multiple transmit antennas are provided by a relay station and the base station, and different space-time block coding schemes are considered. Regenerative and non-regenerative systems are considered for the cooperative schemes. The use of rate compatible puncture turbo codes as forward error correction codes allow the system to be tested under the hybrid-type II automatic repeat request protocol. Results in terms of throughput show the benefits of coded cooperation over direct transmission.

In this paper the flexibility of space-time turbo codes to work with different antenna configurations and constellations is examined. Original contributions are presented for the enhancement of current decoding algorithms and their extension to four transmit antennas. Besides, a metric is proposed for the evaluation of quality of service (QoS) without explicit PER measurement.

A beamforming for OFDM modulation is proposed that allocates power over subchannels and subcarriers. The design follows a minimum BER criterion taking into account the different reliability of CSI estimates at both ends of the transmitter link. The transmitter design is based on a generalized exponential BER bound that is tighter than the Chernoff bound, whereas the receiver that minimizes the BER is implemented by the MAP detector. The resulting design reconfigures itself, from the known solution, when perfect CSI is available to the open-loop solution depending on the channel uncertainty.

This paper deals with closed-loop schemes in OFDM modulation systems. A Bayesian approach is presented to design minimum BER prefiltering matrices in the presence of channel uncertainties. This formulation leads to a robust algorithm that exhibits lower sensitivity to channel estimation errors than classical schemes. The proposed design encompasses the single antenna transmission, the beamforming schemes and the frequency flat fading channels as particular cases.

The design of accurate equalization schemes, optimizing the transmission strategy, can be achieved if channel status information is available not only at the receiver but also at the transmitter side. Accordingly, we propose a feasible scheme to track the channel response by the transmitter based on channel prediction, becoming a suitable solution in time-varying channels. Moreover, to follow faithfully the channel variability, the receiver can estimate the channel prediction error and next update the transmitter predictor through a return link. This paper provides an accurate study of the predictor design, and analyzes the way to minimize the amount of information exchanged through the feedback channel concerning the differential entropy of channel evolution. Furthermore, the prediction error is quantified focusing on the rate-distortion function, and it is shown that a low throughput is enough for tracking the channel coefficients even in the presence of fast time-varying channels.

Linear block codes in the complex field can be applied in spatial and/or temporal diversity receivers in order to develop high performance schemes for (almost-) blind equalization in mobile communications. The proposed technique uses the structure of the encoded transmitted information (with redundancy) to achieve equalization schemes based on a deterministic criterion. Simulations show that the proposed technique is more efficient than other schemes that follow similar equalizer structures. The result is an algorithm that provides the design of channel equalizers in low EbNo scenarios.

Linear block codes can be applied in spatial and/or temporal diversity receivers in order to develop high performance schemes for blind equalization in mobile communications. The proposed technique uses the structure of the encoded transmitted information (with redundancy) to achieve equalization schemes based on a deterministic criterion. Simulations show that the proposed technique is more efficient than other schemes that follow similar equalizer structures. The result is an algorithm that provides the design of blind channel equalizers in low EbNo scenarios.

This paper addresses the use of multichannel receivers for blind equalization in TDMA under frequency selective channels and OFDM systems in frequency flat fading channels. A new criteria is proposed for blind equalization of finite length mobile channels.

A new modulation scheme called transform modulations is proposed which improves the system performance in frequency-flat fading mobile channels. They ameliorate the effect of the fadings over the transmitted signal by spreading the information in time, using a linear transform operator. The design of this scheme is discussed and its advantages are shown by means of simulations. The relationship of this scheme with OFDM modulation is also analyzed.

A blind algorithm for channel distortion compensation is presented which can be employed in spatial or temporal diversity receivers. The proposed technique can be used in frequency selective and frequency flat fading mobile channels, using burst transmission schemes in the first case and OFDM modulation in the second one. The algorithm is base on a deterministic criteria and is suited for estimation when short sets of data are available.