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In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output can obtain more performance gain by exploiting the downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI feedback with limited communication resources in frequency-division duplexing systems is of great importance. Recently, deep learning (DL)-based CSI feedback has shown considerable potential. However, the existing DL-based explicit feedback schemes are difficult to deploy because current fifth-generation mobile communication protocols and systems are designed based on an implicit feedback mechanism. In this paper, we propose a DL-based implicit feedback architecture to inherit the low-overhead characteristic, which uses neural networks (NNs) to replace the precoding matrix indicator (PMI) encoding and decoding modules. By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks. The correlation between subbands is also used to further improve the feedback performance. Simulation results show that, for a single resource block (RB), the proposed architecture can save 25.0% – 40.0% of overhead compared with the Type I codebook under different antenna configurations. For a wideband system with 52 RBs, overhead can be saved by 30.7% and 48.0% compared with the Type II codebook when ignoring and considering extracting subband correlation, respectively. [ABSTRACT FROM AUTHOR]

The key role of spectrum sensing in cognitive radios attracted substantial research attention to improve the performance of detectors. We consider a general model for the receiver noise with the potential of generalization towards modeling noise in various environments. This general noise model describes accurately noise characteristics ranging from the Gaussian noise to the severe impulsive noise. However, many previous studies are based on the ideal Gaussian noise, and they cannot capture the non-Gaussian models. To provide a robust detector against different behaviors of the noise in various environments, we employ a convolutional neural network (CNN) compatible with different noise models. The proposed CNN detector is data-driven, and due to its single-dimensional input layer, it is consistent with the received signal and requires no pre-processing. The likelihood ratio test (LRT), the Wald, and the Rao tests for this problem are derived to enrich the paper with comparative evaluations of the proposed CNN and conventional model-based approaches and other neural networks. Although various simulated scenarios substantiate the general superiority and robustness of the CNN-based against impulsive noise and mismatch of parameters, it requires higher computational complexity than other discussed detectors. [ABSTRACT FROM AUTHOR]

A class of network codes have been proposed in the literature where the symbols transmitted on network edges are binary vectors and the coding operation performed in network nodes consists of the application of (possibly several) permutations on each incoming vector and XOR-ing the results to obtain the outgoing vector. These network codes, which we will refer to as permute-and-add network codes, involve simpler operations and are known to provide lower complexity solutions than scalar linear network codes. The complexity of these codes is determined by their degree which is the number of permutations applied on each incoming vector to compute an outgoing vector. Constructions of permute-and-add network codes for multicast networks are known. In this paper, we provide a new framework based on group algebras to design permute-and-add network codes for arbitrary (not necessarily multicast) networks. Our framework allows the use of any finite group of permutations (including circular shifts, proposed in prior works) and admits a trade-off between coding rate and the degree of the code. Further, our technique permits elegant recovery and generalizations of the key results on permute-and-add network codes known in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we consider a real-time deep learning-based symbol detection approach for MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the time-frequency structure of OFDM signals, a recurrent neural network (RNN) with deep feedforward output layers is introduced, where the recurrent layers and feedforward output layers are designed to process time-domain and frequency-domain information respectively. Reservoir computing (RC), a special type of RNN, and extreme learning machine (ELM), a special type of feedforward neural network, are chosen as the corresponding building blocks to facilitate over-the-air training. An online training loss objective is introduced to recursively update the neural weights in real-time. We believe this is the first work in the literature to realize real-time machine learning for MIMO-OFDM symbol detection, i.e., conducting NN-based symbol detection on an OFDM symbol basis. We demonstrate that (1) the IEEE standardized WiFi training sequence can be directly applied as the real-time training sequence (2) the symbol detection performance can be further improved by using our theoretically derived pilot pattern. Evaluation results show that our RC-ELM-based symbol detection method outperforms traditional model-based techniques as well as state-of-the-art learning-based approaches in highly dynamic channel environments for real-time symbol detection. [ABSTRACT FROM AUTHOR]

This paper deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that the data-driven approaches should not replace, but rather complement, traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks, such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case studies wherein the use of deep learning proves extremely useful for network design. For each case study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement the data-driven approaches. Finally, concluding remarks describe those that, in our opinion, are the major directions for future research in this field. [ABSTRACT FROM AUTHOR]

A deep learning assisted sum-product detection algorithm (DL-SPDA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to approach the maximum a posterior probabilities (MAP) error performance. In specific, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not considered by the conventional detector with a limited complexity. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a turbo equalization receiver. Furthermore, we propose a compatible training technique to improve the detection performance of the proposed DL-SPDA with turbo equalization. In particular, the neural network is optimized in terms of the mutual information between the transmitted sequence and the extrinsic information. We also investigate the maximum-likelihood bit error rate (BER) performance of a finite length coded FTN system. Simulation results show that the error performance of the proposed algorithm approaches the MAP performance, which is consistent with the analytical BER. [ABSTRACT FROM AUTHOR]

In this paper, we consider the performance of a selective decode-and-forward (DF) relaying based multiple-input multiple-output (MIMO) space-time block coded (STBC) cooperative communication system with single and multiple relays. We begin with a single relay based MIMO STBC system and derive the closed form expression for the end-to-end PEP of coded block detection at the destination node. It is also demonstrated that the MIMO STBC cooperative communication system achieves the full diversity order of the system. We also derive the optimal source relay power allocation, which minimizes the end-to-end decoding error of the cooperative system for a given power budget. Subsequently, for the multiple relay scenario, we consider two different relaying protocols based on two-phase and multi-phase communication. For each of these multi-relay protocols, we derive the closed form expressions for the end-to-end error rate, diversity order, and optimal power allocation. Simulation results are presented to validate the performance of the proposed single and multiple relay based cooperative communication schemes and the derived analytical results. Further, these schemes can also be seen to lead to a performance improvement compared to several other relaying schemes in existing literature. [ABSTRACT FROM AUTHOR]

Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future. [ABSTRACT FROM AUTHOR]

This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method of control is to apply the mean field game (MFG) framework which substantially reduces communications among the UAVs. However, to realize this framework, powerful processors are required to obtain the control laws at different UAVs. This requirement limits the usage of the MFG framework for real-time applications such as massive UAV control. Thus, a function approximator based on neural networks (NN) is utilized to approximate the solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Nevertheless, using an approximate solution can violate the conditions for convergence of the MFG framework. Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions. The stability analysis of the NN based MFG approach is presented and the performance of the proposed FL-MFG is elaborated by the simulations. [ABSTRACT FROM AUTHOR]

Predicting the millimeter wave (mmWave) beams and blockages using sub-6 GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. Prior work has focused on extracting spatial channel characteristics at the sub-6 GHz band and then use them to reduce the mmWave beam training overhead. This approach still requires beam refinement at mmWave and does not normally account for the different dielectric properties at the different bands. In this paper, we first prove that under certain conditions, there exist mapping functions that can predict the optimal mmWave beam and blockage status directly from the sub-6 GHz channel. These mapping functions, however, are hard to characterize analytically which motivates exploiting deep neural network models to learn them. For that, we prove that a large enough neural network can predict mmWave beams and blockages with success probabilities that can be made arbitrarily close to one. Then, we develop a deep learning model and empirically evaluate its beam/blockage prediction performance using a publicly available dataset. The results show that the proposed solution can predict the mmWave blockages with more than 90% success probability and can predict the optimal mmWave beams to approach the upper bounds while requiring no beam training overhead. [ABSTRACT FROM AUTHOR]

Emerging recently as a novel concept in communication system design, end-to-end learning introduces deep neural networks (NNs) to represent the transmitter and receiver functions. Consequently, the whole system can be interpreted as an autoencoder (AE), which can be optimized from a holistic approach through a data-driven training method. Until now, the AE technique is mainly developed for point-to-point communication scenarios. In this paper, we aim to develop a novel NN-based AE scheme for relay-assisted cooperative communication systems. Specifically, three NN components are constructed to learn the behavior of the transmitter, relay node, and receiver, respectively. As the conventional end-to-end training is inapplicable, a novel two-stage training approach is proposed to indirectly solve the end-to-end training problem. The implicit approximations involved are analytically expressed based on information theory, offering insights on the achievable performance with the proposed training method. The proposed AE model eliminates the need for channel state information and noise variance of any link, and is adaptive to the variation in the input block length. Simulation results verify its advantages over the conventional decode-and-forward (DF) and amplify-and-forward (AF) schemes in various scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to design finite alphabet iterative decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over binary symmetric channel (BSC) via recurrent quantized neural networks (RQNN). We focus on the linear FAID class and use RQNNs to optimize the message update look-up tables by jointly training their message levels and RQNN parameters. Existing neural networks for channel coding work well over Additive White Gaussian Noise Channel (AWGNC) but are inefficient over BSC due to the finite channel values of BSC fed into neural networks. We propose the bit error rate (BER) as the loss function to train the RQNNs over BSC. The low precision activations in the RQNN and quantization in the BER cause a critical issue that their gradients vanish almost everywhere, making it difficult to use classical backward propagation. We leverage straight-through estimators as surrogate gradients to tackle this issue and provide a joint training scheme. We show that the framework is flexible for various code lengths and column weights. Specifically, in high column weight case, it automatically designs low precision linear FAIDs with superior performance, lower complexity, and faster convergence than the floating-point belief propagation algorithms in waterfall region. [ABSTRACT FROM AUTHOR]

Proactive caching at base stations (BSs) has been shown promising in offloading traffic, where most priori works consider full frequency reuse among cells and assume known file popularity. To facilitate proactive caching, recent works either adopt linear models or shallow neural networks to predict popularity, without explaining the rationale. In this paper, we consider cache-enabled multi-tier heterogenous networks. To show if full frequency reuse is superior, we consider underlay and overlay modes with random bandwidth allocation for interference management. After optimizing the caching policy and bandwidth allocation to maximize successful offloading probability, we show that the overlay mode outperforms the underlay mode. To confirm that complex non-linear prediction models are unnecessary for proactive caching when using the historical numbers of requests, we employ several linear and non-linear models to predict content popularity and user density, using MovieLens and Youku datasets. To interpret why the caching performance achieved by the optimized policies with the information predicted by the linear models is close to that using non-linear models, we prove that deterministic popularity with typical profiles can be predicted with linear models. Then, we show that most popular files are with these profiles in the real dataset. [ABSTRACT FROM AUTHOR]

In TV White Space, the unlicensed users are required to periodically access a database to acquire information on the spectrum usage of the licensed users. In addition, the unlicensed users can access the database on-demand, whenever they believe convenient, to update the spectrum availability information. In this paper, we design the optimal database access strategy, i.e., the strategy allowing the unlicensed users to jointly: 1) maximize the expected overall communication opportunities through on-demand accesses; and 2) respect the regulatory specifications. To this aim, we develop a stochastic analytical framework that allows us to account for: 1) the PU activity dynamics; 2) the quality dynamics among the different channels; and 3) the overhead induced by the database access. Specifically, at first, we prove that the database access problem can be modeled as a Markov decision process, and we show that it cannot be solved through brute-force search. Then, we prove that the optimal strategy exhibits a threshold structure, and we exploit this threshold property to design an algorithm able to efficiently compute the optimal strategy. The analytical results are finally validated through simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a general mathematical framework to compute the average downlink rate in a multiple connectivity context considering ultra-dense network (UDN) environment. UDN is a dense small cells network featured by the high density of small cells that may exceed the density of active users. In multiple association, a user connects to $M$ base stations (BSs) that provide the maximum average received power forming a multicell. This provides the user with a “data-shower,” where the user’s traffic is split into multiple paths, which helps overcoming the capacity limitations imposed by the backhaul links. The developed framework significantly simplifies the computation of the average downlink rate of the individual connections to the cells of a multicell. Moreover, the accuracy of the mathematical framework is confirmed by extensive simulations. The simulation results show a perfect match with the numerical results computed from the mathematical framework in different combinations of the system parameters including multicell size, small cells density, active users density, pathloss exponent, and fading channel distribution of the signal link. [ABSTRACT FROM AUTHOR]

Artificial neural stimulation of a peripheral nerve can create an in-body data communications channel. We propose the stimulation of a peripheral nerve using energy-harvesting arrays of nanodevices, embedded in biocompatible tissue patches. The resulting extracellular compound action potential (CAP) pulse can provide a data bit-stream for communicating with an embedded receiver. Our objective is to determine the maximum achievable transmission range of a CAP along a nerve and the maximum sustainable bit rate. We model the generation of a CAP and then compute the reduction in amplitude and the spreading of the pulse with propagation distance. The channel capacity is calculated for ON–OFF keying (OOK) and digital pulse interval modulation. We show that the transmission range depends on the number and diameters of the activated neurons contributing to the CAP amplitude and width. Our modulation analysis demonstrates the effects of attenuation, background noise, the neural refractory period, and pulse broadening on the achievable bit rate. We show how a maximum OOK bit rate of 200 bit/s can be sustained over transmission distances greater than 100 mm. The proposed approach provides a low bit rate, unidirectional asynchronous transmission system that could, for example, deliver simple instructions to an embedded drug-delivery system. [ABSTRACT FROM AUTHOR]

Recurrent neural networks (RNNs) have been successfully applied to communications channel equalization because of their modeling capability for nonlinear dynamic systems. Major problems of gradient-descent learning techniques commonly employed to train RNNs are slow convergence rates and long training sequences required for satisfactory performance. This paper presents decision-feedback equalizers using an RNN trained with Kalman filtering algorithms. The main features of the proposed recurrent neural equalizers, using the extended Kalman filter (EKF) and unscented Kalman filter (UKF), are fast convergence and good performance using relatively short training symbols. Experimental results for various time-varying channels are presented to evaluate the performance of the proposed approaches over a conventional recurrent neural equalizer. [ABSTRACT FROM AUTHOR]

The problem of finding an optimum conflict-free transmission schedule for a broadcast packet radio network (PRNET) is NP-complete. In addition to a host of heuristic algorithms, recently, neural network and simulated annealing approaches to solve this problem were reported. We show that the standard genetic algorithm, though able to solve small problems, performed poorly for large networks. This is because classical crossover and mutation operations create invalid members, which flood the whole population, hindering the progress of the search for valid solutions In this paper, special crossover and mutation operations are defined, such that the members of the population always remain valid solutions of the problem. Excellent results were obtained in a few generations, even for very large networks with 400 nodes. The results were compared with recently reported neural network and mean field annealing approaches. [ABSTRACT FROM AUTHOR]

Presents a neural network approach for modeling nonlinear memoryless communication channels. Analytical models for approximating traveling-wave tube (TWT) amplifiers; Simulation examples; Comparison of neural network approach with classical methods; Conclusions.

Optical communications systems’ performance is limited by physical layer nonlinearities inherent in some of their transmitter components. In order to overcome some of these limitations, several approaches utilizing artificial neural networks to digitally pre-distort the transmitted signal have been proposed in the last few years, alongside other more classical models. However, their performance was strongly dependent on the specific training sequence used. This work improves the performance of neural network-based direct learning approach for digital pre-distortion applications. The proposed method, which is based on the curriculum learning approach, aims to construct a training paradigm of gradually increasing complexity based on the statistical properties of the input signal, in order to achieve better convergence of the network. We prove that the proposed method defines a proper curriculum regardless of the training data distribution, considering the proposed wide-sense curriculum conditions and the case of binary weighting functions. A comparative analysis examines a pre-distorter for a coherent optical transmitter modelled by a Long Short-Term Memory neural network. An improvement of 4.8dB in terms of normalized mean-square error is demonstrated for the case of a 3-bits linear quantizer, compared to the conventional training scheme. [ABSTRACT FROM AUTHOR]

This paper presents a queueing model for the polling based service classes of WiMAX/IEEE 802.16 based wireless networks. Models are presented for both single channel and multiple channel (OFDMA based) operations. The models evaluate the MAC layer packet delays as a function of various system parameters. [ABSTRACT FROM AUTHOR]

Spectrum sensing is one of the enabling functionalities for cognitive radio systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the cognitive radio is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we study a spectrum sensing technique based on spectral correlation for detection of television (TV) broadcasting signals. The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal. We show that this sensing technique is asymptotically equivalent to the likelihood ratio test (LRT) at very low SNR, but with less computational complexity. That is, the spectral correlation-based detector is asymptotically optimal according to the Neyman-Pearson criterion. From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance. Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing. Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR levels as low as -20 dB. [ABSTRACT FROM AUTHOR]

In cognitive networks, since nodes generally belong to different authorities and pursue different goals, they will not cooperate with others unless cooperation can improve their own performance. Thus, how to stimulate cooperation among nodes in cognitive networks is very important. However, most of existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, according to the well-known Prisoner's Dilemma and the backward induction principle, the unique Nash equilibrium (NE) is to always play non-cooperatively. In this paper, we propose a cooperation stimulation scheme for the scenario where the number of interactions between any pair of players are finite. The proposed algorithm is based on indirect reciprocity game modelling where the key concept is "I help you not because you have helped me but because you have helped others". We formulate the problem of finding the optimal action rule as a Markov Decision Process (MDP) and propose a modified value iteration algorithm to find the optimal action rule. Using the packet forwarding game as an example, we show that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy (ESS). Finally, simulations are shown to verify the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

High quality data is essential in deep learning to train a robust model. While in other fields data is sparse and costly to collect, in error decoding it is free to query and label thus allowing potential data exploitation. Utilizing this fact and inspired by active learning, two novel methods are introduced to improve Weighted Belief Propagation (WBP) decoding. These methods incorporate machine-learning concepts with error decoding measures. For BCH(63,36), (63,45) and (127,64) codes, with cycle-reduced parity-check matrices, improvement of up to 0.4dB at the waterfall region, and of up to 1.5dB at the error-floor region in FER, over the original WBP, is demonstrated by smartly sampling the data, without increasing inference (decoding) complexity. The proposed methods constitutes an example guidelines for model enhancement by incorporation of domain knowledge from error-correcting field into a deep learning model. These guidelines can be adapted to any other deep learning based communication block. [ABSTRACT FROM AUTHOR]

Near-capacity Non-coherent Cooperative Network-coding aided Multi-user (NNCNM) systems are designed with the aid of Extrinsic Information Transfer (EXIT) charts for the sake of approaching the Differential Discrete-input Continuous-output Memoryless Channel's (D-DCMC) system capacity\footnotePart of this work was presented at VTC2011-Fall .. The upper and lower Frame Error Ratio (FER) performance bounds are derived for aiding our network coding design. The outage capacity of the D-DCMC channel is also calculated and used for computing the best-case performance bounds of both the corresponding single-link scheme and of the proposed NNCNM system. Moreover, a new technique referred to as the Pragmatic Algebraic Linear Equation Method (PALEM) was proposed for determining the exact number of information sources that may be recovered from the composite NNCNM stream, which constitutes a lower-complexity evaluation of the attainable FER performance of the NNCNM systems without resorting to high-complexity Monte-Carlo simulations. The NNCNM systems advocated are capable of operating within 0.3-0.5 dB from the corresponding D-DCMC capacity. A joint treatment of channel and network coding is considered in our system. The design principles presented in this contribution may be extended to a vast range of NNCNM based systems using arbitrary channel coding schemes. [ABSTRACT FROM PUBLISHER]

Presents a novel chaotic coding/decoding strategy based on radial basis function artificial neural networks. Discussion of chaotic signals; Benefits of using the power spectral density of chaotic signals in channel estimation and echo cancellation; Information on demodulation; Details of a proposed dynamic feedback method.

Introduces a novel equalization algorithm applying soft-decision feedback and designed for binary transmission. Description of the algorithm; Interpretation of the proposed scheme as a discrete-time Hopfield neural network; Numerical results and discussion; Effects of nonideal channel estimation.

Suggests two ideas to speedup `neural-gas' network sequential realization. Information about neural gas algorithm; Information on ranking determination phase and the weights updating phase; Results of the experiment; Conclusions.

Describes an approach for the decision feedback equalizer (DFE) based on the functional-link neural network. Description of the system model; Decision feedback functional-link equalizer (DFFLE) and its learning algorithm; Simulation results and comparison with other equalizers.

The long-historied feedforward technology was applied to an HFC network and, let's say, this FFCC(Fiber Feedforward Coaxial Cascade) HFC network acquired significant performance improvement of 3.4 dB in CNR, 4 dB in CSO and 10 dB in CTB respectively. The improvement of 3.4 dB in CNR permits more than doubling of the number of coaxial cascade i.e., 7 stages which result in the extension of coaxial path approximately 3 km in distance, with the existing same configuration. Thus, FFCC-HFC network would be very useful in upgrading or improving conventional HFC networks with minimum modification, either in the occasions of extending the network distance coverage, increasing the number of subscribers connected to the system, or expanding the frequency bandwidth for more transmission channels or emerging services. [ABSTRACT FROM PUBLISHER]

In this letter, a novel pipelined decision feedback RNN equalizer (PDFRNE) with low computational complexity is proposed. Since each module is a DFRNN with the decision feedback structure so that it can eliminate the past error remaining in the network. Moreover, the performance can be further improved. At the same time, it can overcome the unstableness due to its nature of the infinite impulse response (IIR) structure. [ABSTRACT FROM PUBLISHER]