Bayesian Estimation of a Gaussian source in Middleton's Class-A Impulsive Noise

The paper focuses on minimum mean square error (MMSE) Bayesian estimation for a Gaussian source impaired by additive Middleton's Class-A impulsive noise. In addition to the optimal Bayesian estimator, the paper considers also the soft-limiter and the blanker, which are two popular suboptimal estimators characterized by very low complexity. The MMSE-optimum thresholds for such suboptimal estimators are obtained by practical iterative algorithms with fast convergence. The paper derives also the optimal thresholds according to a maximum-SNR (MSNR) criterion, and establishes connections with the MMSE criterion. Furthermore, closed form analytic expressions are derived for the MSE and the SNR of all the suboptimal estimators, which perfectly match simulation results. Noteworthy, these results can be applied to characterize the receiving performance of any multicarrier system impaired by a Gaussian-mixture noise, such as asymmetric digital subscriber lines (ADSL) and power-line communications (PLC).Comment: 30 pages, 13 figures, part of this work has been submitted to IEEE Signal Processing Letter

Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies

The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly time-varying) subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely, the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed to design the sampling probability at each node in the graph, with the aim of optimizing the tradeoff between steady-state performance, graph sampling rate, and convergence rate of the adaptive algorithms. Finally, a distributed RLS strategy is derived and is shown to be convergent to its centralized counterpart. Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and reconstruction strategies for (possibly distributed) adaptive learning of signals defined over graphs.Comment: Submitted to IEEE Transactions on Signal Processing, September 201

Probability of Error of Linearly Modulated Signals with Gaussian Cochannel Interference in Maximally Correlated Rayleigh Fading Channels

We evaluate the probability of error of linearly modulated signals, such as phase-shift keying (PSK) and quadrature amplitude modulation (QAM), in the presence of Gaussian cochannel interference (CCI) and Rayleigh fading channels. Specifically, we assume that the fading channel of the CCI is maximally correlated with the fading channel of the signal of interest (SOI). In practical applications, the maximal correlation of the CCI channel with the SOI channel occurs when the CCI is generated at the transmitter, such as the multiuser interference in downlink systems, or when a transparent repeater relays some thermal noise together with the SOI. We analytically evaluate the error probability by using a series expansion of generalized hypergeometric functions. A convenient truncation criterion is also discussed. The proposed theoretical approach favorably compares with alternative approaches, such as numerical integration and Monte Carlo estimation. Among the various applications of the proposed analysis, we illustrate the effect of nonlinear amplifiers in orthogonal frequency-division multiplexing (OFDM) systems, the downlink reception of code-division multiple-access (CDMA) signals, and the outdoor-to-indoor relaying of Global Positioning System (GPS) signals

Joint Impact of Frequency Synchronization Errors and Intermodulation Distortion on the Performance of Multicarrier DS-CDMA Systems

The performance of multicarrier systems is highly impaired by intercarrier interference (ICI) due to frequency synchronization errors at the receiver and by intermodulation distortion (IMD) introduced by a nonlinear amplifier (NLA) at the transmitter. In this paper, we evaluate the bit-error rate (BER) of multicarrier direct-sequence code-division multiple-access (MC-DS-CDMA) downlink systems subject to these impairments in frequency-selective Rayleigh fading channels, assuming quadrature amplitude modulation (QAM). The analytical findings allow to establish the sensitivity of MC-DS-CDMA systems to carrier frequency offset (CFO) and NLA distortions, to identify the maximum CFO that is tolerable at the receiver side in different scenarios, and to find out the optimum value of the NLA output power backoff for a given CFO. Simulation results show that the approximated analysis is quite accurate in several conditions

Adaptive resource optimization for edge inference with goal-oriented communications

AbstractGoal-oriented communications represent an emerging paradigm for efficient and reliable learning at the wireless edge, where only the information relevant for the specific learning task is transmitted to perform inference and/or training. The aim of this paper is to introduce a novel system design and algorithmic framework to enable goal-oriented communications. Specifically, inspired by the information bottleneck principle and targeting an image classification task, we dynamically change the size of the data to be transmitted by exploiting banks of convolutional encoders at the device in order to extract meaningful and parsimonious data features in a totally adaptive and goal-oriented fashion. Exploiting knowledge of the system conditions, such as the channel state and the computation load, such features are dynamically transmitted to an edge server that takes the final decision, based on a proper convolutional classifier. Hinging on Lyapunov stochastic optimization, we devise a novel algorithmic framework that dynamically and jointly optimizes communication, computation, and the convolutional encoder classifier, in order to strike a desired trade-off between energy, latency, and accuracy of the edge learning task. Several simulation results illustrate the effectiveness of the proposed strategy for edge learning with goal-oriented communications