Deep Reinforcement Learning Autoencoder with Noisy Feedback

This correspondence analyzes the performance of concatenated coding systems and modulation schemes operating over the additive white Gaussian noise (AWGN) channel by examining the loss of capacity resulting from each of the processing steps. The techniques described in this correspondence allow the separate evaluation of codes and decoders and thus the identification of where loss of capacity occurs. Knowledge of this capacity loss is very useful for the overall design of a communications system, e.g., for evaluating the benefits of inner decoders that produce information beyond the maximum-likelihood (ML) estimate. The first two sections of this correspondence provide a general technique for calculating the composite capacity of an orthogonal or a bi-orthogonal code and the AWGN channel in isolation. The later sections examine the composite capacities of an orthogonal or a bi-orthogonal code, the AWGN channel, and various inner decoders including the decoder estimating the bit-by-bit probability of a one, as is used in turbo codes. The calculations in these examples show that the ML decoder introduces a large loss in capacity. Much of this capacity loss can be regained by using only slightly more complex inner decoders, e.g., a detector for M-ary frequency-shift keying (MFSK) that puts out the two most likely frequencies and the probability the ML estimate is correct produces significantly less degradation than one that puts out only the most likely frequency.

This paper presents a new study in an attempt to apply Fast Euclidean Direction Search (FEDS) algorithm on adaptive smart antennas. The novelty in this work is the use of FEDS algorithm in a smart antenna system for mobile communications because up to our knowledge this algorithm is not applied on smart antenna so far. In order to properly evaluate the performance of smart antenna for mobile communication systems, an Additive White Gaussian Noise (AWGN) channel model is required that each received signal at each element in the array includes only an additive, zero mean, Gaussian noise . The ability of the FEDS algorithm to operate in a fast changing signal environment is examined by subjecting the input signal to Rayleigh fading with a Jakes power spectral density which is a popular choice for land mobile communications systems. Through simulation results of smart antennas for AWGN channel, the RLS and FEDS start to converge from the initial iteration and 10 respectively, whereas, the LMS and NLMS start to converge after 50 and 10 iterations respectively. In the presence of the Rayleigh fading channel, the LMS, and NLMS algorithms converge after 60 and 25 iterations respectively, while the RLS convergence from the initial iteration and FEDS converge from the iteration number 25. On the other hands, the FEDS algorithm shows better tracking and estimation of the desired input signal compared with both LMS and NLMS algorithms and comparable with RLS algorithm. The comparison of linear plot of array factor using FEDS and RLS , both algorithms generates the same deeper null for undesired signals of about í30 dB towards the interferer using an AWGN channel, while it generates deeper null of about -23 dB and -30 dB respectively using the Rayleigh fading channel.

The optical ZCZ code, which is a set of pairs of binary and biphase sequences with zero correlation zone, can provide code division multiple access (CDMA) communication system without co-channel interference. The M-ary amplitude shift keying spread spectrum (M-ary/ASK-SS) system using this code can detect a desired sequence without interference of undesired sequences. We have proposed the compact construction of a matched filter bank for this code, which is called the compact-type matched filter bank. However, M-ary/ASK-SS system using this filter bank might go down the bit error rate (BER) performance because input-output characteristics of this filter bank for this code with zero correlation zone 4n - 2 are nonlinear characteristics. In this paper, we clarify that BER performance in the M-ary/ASK-SS system over the additive white Gaussian noise (AWGN) channels is not influenced by nonlinear characteristics of this filter bank by the theoretical formula of BER characteristics and the computer simulation. The BER performance in the ASK-SS system using compact-type matched filter under the AWGN environment go down compared to that using direct-type matched filter as expected. But the BER performance in the M-ary/ASK-SS system using this compact-type matched filter bank under the AWGN environment is almost equal compared to that using parallel-type matched filter bank. In fact, this compact-type matched filter bank is effective in the M-ary/ASK-SS system.

This paper presents asymptotic multiuser efficiency (AME) performance analysis of the decorrelator based successive interference cancellation (DBSIC) DS-CDMA multiuser receiver DBSIC is similar to the conventional SIC receiver except that a decorrelator is used at each detection stage prior to cancellation. AMEs of DBSIC are compared with those of single-user matched filter, optimum, decorrelating and conventional SIC receivers over both additive white Gaussian noise (AWGN) and Rayleigh fading channels. It is shown that over AWGN channels, DBSIC AME is lower than the optimum receiver AME when the interfering user is weaker than the user of interest but approaches the unity optimum receiver AME as the interferer becomes stronger than the user of interest. For all spreading codes cross-correlation considered, DBSIC has a similar AME curve shape as the conventional SIC receiver but has a higher AME which remains positive for all user energy ratios, illustrating its near-far resistance over AWGN channels. The AME of DBSIC is better than that of the decorrelating detector when the interferer is much weaker or slightly stronger than the user of interest and has worse performance when the interferer is not much weaker. It is shown that DBSIC is completely near-far resistant over Rayleigh fading channels with the same AME as the decorrelating scheme for user 1 and as the optimum scheme for user 2, both AMEs being independent of the user energy ratio. This shows that DBSIC is preferable compared to the decorrelating detector, while still being less complex than the optimum scheme over Rayleigh fading channels

Modulo lattice additive noise (MLAN) channels appear in the analysis of structured lattice codes with lattice decoding for Costa's dirty-paper channel and the additive white Gaussian noise (AWGN) channel. Building on the recent result on the optimal density of an infinite constellation over an AWGN channel, in this paper, it is shown that with a proper choice of the shaping lattice and the scaling parameter, the dispersion of the MLAN channel is asymptotically the same as that of the (zero-interference) AWGN channel at the same signal-to-noise ratio.

Two three‐dimensional (3D) 16‐ary signal constellations having extended lattice structures are presented in this paper. The theoretical symbol error probabilities (SEPs) of the new constellations in additive white Gaussian noise (AWGN) and Rayleigh fading channel are derived. Computer simulation confirms that the closed‐form expressions for average SEPs of the constellations in the AWGN channel are very accurate. The theoretical SEP upper bounds in the Rayleigh fading channel are very tight. Since the presented constellations have larger minimum Euclidean distance (MED) than the conventional one, error performance can be improved up to 0.9 dB in an AWGN environment. Hence, the proposed constellations and their theoretical analysis can be used as a reference for the development of a wireless communication system with 3D signal constellations.

It is a promising technology to replace traditional transceiver design with neural network (NN)-based autoencoder to realize end-to-end communication. The joint optimization of multiple blocks helps the communication system to achieve better performance. However, this approach needs a differentiable channel modeling to train both NNs at the transceiver. For a non-differentiable channel in a real scenario, gradient backpropagation cannot be performed. In this paper, we propose a Kalman-based autoencoder framework to achieve gradient estimation and backpropagation by adding a control layer at the last layer of the encoder NN, which combines the rich feature representations learned by the NN with the Bayesian filter method based on Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF). According to simulation results, the proposed framework has a fast convergence rate and strong robustness in both additive white Gaussian noise (AWGN) and Rayleigh block fading (RBF) of perturbation scenarios.

Coding for the fading channel: a survey

We consider the additive white Gaussian noise (AWGN) channel with a (σ, ρ)-power constraint, which is motivated by energy harvesting communication systems. This constraint imposes a limit of σ + kρ on the total power of any k ≥ 1 consecutive transmitted symbols in a codeword. We analyze the capacity of this channel geometrically, by considering the set S n (σ, ρ) ⊆ ℝn which is the set of all n-length sequences satisfying the (σ, ρ)-power constraints. For a noise power of ν, we obtain an upper bound on capacity by considering the volume of the Minkowski sum of S n (σ, ρ) and the n-dimensional Euclidean ball of radius √nν. We analyze this bound using a result from convex geometry known as Steiner's formula, which gives the volume of this Minkowski sum in terms of the intrinsic volumes of S n (σ, ρ). We show that as n increases, the logarithms of the intrinsic volumes of {S n (σ, ρ)} converge to a limit function under an appropriate scaling. An upper bound on capacity is obtained in terms of the limit function, thus pinning down the asymptotic capacity of the (σ, ρ)-power constrained AWGN channel in the low-noise regime. We derive stronger results when σ = 0, corresponding to the amplitude-constrained AWGN channel.

Background. The disadvantage of spectrally efficient signals with frequency multiplexing is the occurrence of intersymbol interference, which is further aggravated when these signals propagate in frequency selective channels.
 Aim. The possibility and effectiveness of using neural network approaches for channel equalization and signal detection in communication systems using SEFDM signals has been assessed.
 Methods. A receiver structure for SEFDM systems based on a deep complex-valued convolutional neural network is proposed, which allows recovering bits from the temporal representation of the signal without using the fractional Fourier transform and inverting the cross-correlation matrix between frequency subcarriers. A two-stage network training scheme has been developed. Based on simulation modeling, a comparative analysis of the noise immunity of SEFDM systems was carried out both in a channel with white Gaussian noise and in a channel with Rayleigh fading, using classical and neural network receivers.
 Results. It is shown that there is no loss of noise immunity in channels with additive white Gaussian noise and an increase in noise immunity of the system up to 2 dB in the channel specified by the extended automotive model (3GPP-EVA).
 Conclusion. The effectiveness of using deep neural complex-valued convolutional networks as receivers for spectrally efficient communication systems, as well as their advantage over classical ones, is shown.
 Keywords – SEFDM; deep complex-valued convolutional neural network; turbocoding.

In the previous two chapters, we studied specific techniques for communication over wireless channels. In particular, Chapter 3 is centered on the point-to-point communication scenario and there the focus is on diversity as a way to mitigate the adverse effect of fading. Chapter 4 looks at cellular wireless networks as a whole and introduces several multiple access and interference management techniques. The present chapter takes a more fundamental look at the problem of communication over wireless fading channels. We ask: what is the optimal performance achievable on a given channel and what are the techniques to achieve such optimal performance? We focus on the point-to-point scenario in this chapter and defer the multiuser case until Chapter 6. The material covered in this chapter lays down the theoretical basis of the modern development in wireless communication to be covered in the rest of the book. The framework for studying performance limits in communication is information theory . The basic measure of performance is the capacity of a channel: the maximum rate of communication for which arbitrarily small error probability can be achieved. Section 5.1 starts with the important example of the AWGN (additive white Gaussian noise) channel and introduces the notion of capacity through a heuristic argument. The AWGN channel is then used as a building block to study the capacity of wireless fading channels. Unlike the AWGN channel, there is no single definition of capacity for fading channels that is applicable in all scenarios.

Self-mixing interferometry (SMI) is superior to other laser interferometry methods due to its simplicity and compactness. However, SMI signals are often complex and difficult to process due to interference in the form of variations in the effective reflectivity of the target, noisy signals, complex signal shapes, and other dependencies. Deep neural networks have been a very popular area of research in computer artificial intelligence in recent years, allowing more implicit features to be uncovered than traditional shallow machine learning. It has been shown that convolutional and back-propagation neural networks can be used for SMI signal processing. There are also studies that have used machine learning genetic algorithms for absolute distance measurement. Based on the above, this study used convolutional neural networks to form a deep neural network for absolute distance measurement based on SMI technology. We first trained the deep convolutional neural network at different feedback strengths. The results of the Convolutional Neural Network (CNN) model showed a coefficient of determination of 0.9987. which is consistent with the required model. The trained network was then used to estimate absolute distances with and without the addition of noise. The comparison proves that the proposed method is noise-proof and has high adaptability for measurements under different conditions.

Future communication system requires large bandwidths to achieve high data rates, thus rendering analog-to-digital conversion (ADC) a bottleneck due to its high power consumption. In this paper, we consider monobit receivers for QPSK. The optimal monobit receiver under Nyquist sampling is obtained and its performance is analyzed. Then, a suboptimal but low-complexity receiver is proposed. The effect of imbalances between In-phase (I) and Quadrature (Q) branches is carefully examined. To combat the performance loss due to IQ imbalances, monobit receivers based on double training sequences and eight-sector phase quantization are proposed. Numerical simulations show that the low-complexity suboptimal receiver suffers 3dB signal-to-noise-ratio (SNR) loss in additive white Gaussian noise (AWGN) channels and only 1dB SNR loss in multipath channels compared with matched-filter monobit receiver with perfect channel state information (CSI). It is further demonstrated that the amplitude imbalance has essentially no effect on monobit receivers. In AWGN channels, receivers based on double training sequences can efficiently compensate for the SNR loss without complexity increase, while receivers with eight-sector phase quantization can almost completely eliminate the SNR loss caused by IQ imbalances. In dense multipath channels, the effect of imbalances on monobit receivers is slight.

Evaluation of error rate of a digital communication system is usually done using the Monte Carlo simulation method. The main drawback of the Monte Carlo method is the need of a huge sample size for estimating low error rates. This paper introduces a novel quasi-analytical technique for estimating the error rate of a communication link over an Additive White Gaussian Noise (AWGN) channel, which greatly outperforms the Monte Carlo method. This technique is demonstrated for a simple spherical code.

Soft demodulation is a basic module of traditional communication receivers. It converts received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the nonideal additive white Gaussian noise (AWGN) channel, it is difficult to accurately calculate the LLR. In this letter, we propose a demodulator, DemodNet, based on a fully convolutional neural network with variable input and output length. We use hard bit information to train the DemodNet, and we propose log probability ratio (LPR) based on the output layer of the trained DemodNet to realize soft demodulation. The simulation results show that under the AWGN channel, the performance of both hard demodulation and soft demodulation of DemodNet is very close to the traditional methods. In three non-ideal channel scenarios, i.e., the presence of frequency deviation, additive generalized Gaussian noise (AGGN) channel, and Rayleigh fading channel, the performance of channel decoding using the soft information LPR obtained by DemodNet is better than the performance of decoding using the exact LLR calculated under the ideal AWGN assumption.