We study spike-based sensor node communication using runlength-limited (RLL) coding to encode information in the temporal distances of the spikes. For such systems integrate-and-fire time encoding machines (IF-TEMs) are considered as an energy-efficient alternative to uniform sampling analog-to-digital converters (ADCs) at the receiver. In this regard, we present a spike detector that employs an IF-TEM with periodic reset followed by a demapper calculating log-likelihood ratios of the transmitted RLL symbols. We assess the communication performance based on the achievable rate between the RLL encoder input and the RLL decoder output. A comparison to the use of 1-bit ADCs shows that the proposed spike detection enables communication at significantly lower energy per bit to noise power spectral density ratio Eb/N0.

We focus on low-power spike-based sensor node communication where runlength-limited (RLL) encoding is applied to map the information to the timing of the spikes. In this type of sensor communication, often rare events need to be communicated such that spikes are transmitted rarely, and the detection of those spikes has to be performed in an energyefficient way. Generally, standard analog-to-digital converter (ADC) based detectors are employed at the receiver, which are always active even if there is no spike being transmitted, resulting in unnecessary power consumption. To mitigate this issue, this paper studies an integrate-and-fire (IF) circuit followed by a counter and pre-processor as an energy-efficient spike detector. It counts the number of fires in each symbol interval, which is further used by the pre-processing unit to map the fire count within each symbol interval into transmitted RLL symbols' loglikelihood ratios. We evaluate a lower bound on the mutual information (MI) rate and the bit error rate of the communication system using this spike detector. Numerical results indicate that the proposed spike detection enables to receive RLL encoded spike sequences at a significantly lower energy per communicated bit Eb than traditional ADC based spike detection and alternative low-power IF time encoding machine (IF-TEM) based detection methods. Moreover, the required Eb decreases when increasing the minimum runlength constraint jointly with the signaling rate. Furthermore, a comparative discussion on the power consumption between the IF circuit based analog-todigital conversions and standard ADCs is presented.

This paper proposes a Gaussian mixture model (GMM) based access point (AP) clustering technique in cell-free massive MIMO (CFMM) communication systems. The APs are first clustered on the basis of large-scale fading coefficients, and the users are assigned to each cluster depending on the channel gain. As the number of clusters increases, there is a degradation in the overall data rate of the system, causing a trade-off between the cluster number and average rate per user. To address this problem, we present an optimization problem that optimizes both the upper bound on the average downlink rate per user and the number of clusters. The optimal number of clusters is intuitively determined by solving the optimization problem, and then grouping the APs and users. As a result, the computation expense is much lower than the current techniques, since the existing methods require evaluations of the network performance in multiple iterations to find the optimal number of clusters. In addition, we analyze the performance of both balanced and unbalanced clustering. Numerical results will indicate that the unbalanced clustering yields a superior rate per user while maintaining a lower level of complexity compared to the balanced one. Furthermore, we investigate the statistical analysis of the spectral efficiency (SE) per user in the clustered CFMM. The findings reveal that the SE per user can be approximated by the logistic distribution.

This paper focuses on precoder optimization of a spatially multiplexed multiple-input multiple-output (MIMO) system with noncoherent reception in a correlated Rayleigh fading environment. We consider a Kronecker product model for the channel correlation with a transmit equicorrelation matrix and a receive correlation matrix which is diagonal. The transmit symbol vector, in which the symbols are taken from two binary constellations {0,1} and {1,-r} (with 0leq r < 1 ), is premultiplied by a diagonal precoder matrix with positive precoder parameters. As the average signal-To-noise ratio per diversity branch becomes large, the symbol vector error probability (SVEP) tends to reach saturation. We minimize this saturation value with respect to the precoder parameters and the constellation parameter r. It is found from computation that the optimal precoder parameters are approximately in geometric progression for both constellations. By observing the patterns in optimal values obtained from computation, we simplify the optimization problem. This simplification reduces the computational effort required to solve the complex minimization problem without affecting the SVEP significantly. Furthermore, in the case of the constellation {1,-r} , it is found from computation that as the number of transmit antennas increases, the optimal value of r decreases.

This paper focuses on symbol detection for a multi-user single-input multiple-output (MU-SIMO) uplink using supervised machine learning techniques. An extreme learning machine (ELM) is chosen as the equalizer at the receiver end due to its super-fast learning ability and minimum training error. To tune the parameters, a pilot based online training for an ELM network is proposed, and those parameters are further used to detect the unknown transmitted symbols. Conventionally, the channel matrix is estimated using known pilot symbols explicitly and then the transmitted symbol is recovered using a linear equalizer. Here, instead of directly estimating the channel coefficients, the symbols are detected using an ELM based direct equalizer followed by a hard decision rule. To reduce the pilot requirement of the proposed model, a method is discussed where the channel is estimated using a small number of pilot symbols and the ELM network is trained using a separate training dataset along with the channel estimate. Furthermore, the performance comparison between the proposed ELM based equalizers, linear equalizers, and nonlinear detection techniques is shown.

In this paper, we study deterministic linear precoding and combining for a single-user multiple-input multiple-output communication system in flat Rayleigh fading. We consider differential encoding at the transmitter and differential detection using a maximum likelihood decision rule at the receiver. The precoding matrix is designed to maximize the bound on the average received signal-to-noise ratio, whereas the combining matrix is structured to minimize the union bound on the symbol error probability (SEP). In addition, we present a comprehensive evaluation of the error performance in terms of the pairwise error probability and the SEP.

Wireless Sensor Networks (WSNs) are networks formed using a large number of low-cost sensor nodes that have limited energy sources, limited processing capability, low storage capacity, and generate a large amount of sensed data with high temporal coherency. Due to high node density in sensor networks, the same data is sensed by many nodes, which results in data redundancy. The problem becomes worse if the redundant transmission contains both normal and faulty data. This creates the issue of differentiating between normal and faulty behavior. This redundancy can be eliminated by using data fusion based techniques. Data aggregation based data fusion is considered an important technique that can reduce the repetitive transmission of the sensed data and can improve the network lifetime. Hence for maintaining the reliability and longevity of the sensor network, in this article, we propose a novel combination of data aggregation based data fusion with effective fault detection by utilizing the properties of Grey Model (GM) and Kernel-based Extreme Learning Machine (KELM). Here, GM is utilized as a data fusion scheme that records the single datum pattern by rejecting the repetitive data received from the different sensor nodes. Trained KELM is utilized for effective detection of fault thus maintaining high confidentiality of the network. The proposed technique is trained and tested using the standard WSN datasets recorded from different laboratories. The simulation results show that the proposed technique can effectively reduce the repetitive transmission and can efficiently detect the fault in the network. The solved problems result in extending the lifetime of the network by taking the low computational time and fast speed.

The adoption of data aggregation depending on data fusion and data acquisition for wireless sensor networks (WSN) is increasing these days. While in WSN, the sensor node senses data and send them to the end node. The application of WSN gets limited due to its features such as low-cost sensor nodes, limited battery backups. The usage of sensor nodes in WSN becomes prone to faulty behavior due to its resource constraint and easily gets defected. Predictive detection using data fusion can be a better choice in order to detect the fault with low transmission energy and low power usage. Considering the conditions of the sensor node with its limited capacity of storing and processing of data, a hybrid predictive classification technique is proposed by using the Kalman filter with Extreme learning machine. Here for data fusion Kalman filter is used to train the sink node with the faulty pattern of data in place of training it with the larger amount. In addition, Extreme learning machine (ELM) is used as a predictive classifier, which can provide a high prediction with low communication overhead. The proposed work is evaluated using standard WSN data by inserting random anomalies to it. The performance is measured in terms of detection accuracy and computational time.