We consider a novel resource allocation framework designed to achieve optimal resource management for edgeassisted inference tasks. This is obtained by a new optimization approach, addressed as conformal Lyapunov optimization, which integrates online conformal risk control (O-CRC) with conventional Lyapunov optimization (LO). Unlike traditional LO, this approach ensures compliance with deterministic long-term reliability constraints. Simulation results, based on an edgeassisted segmentation task, demonstrate the effectiveness of the proposed method in balancing energy consumption and inference performance, while maintaining strict control over deterministic long-term constraints, related to the false negative segmentation rate.

This paper investigates a BER-aware goal-oriented system design, for digital wireless communications, enabling edge-assisted learning applications. Targeting image classification tasks, we exploit banks of encoders at the transmitter, and classifiers at the edge-server that, trained under different BER conditions and exploiting knowledge of the channel state and computation load, let us to dynamically adapt, in a goal-oriented fashion, the size of the data to be transmitted, the M-QAM scheme, and the system BER, to fulfill task-specific performance. Exploiting Lyapunov optimization, we propose a minimum-energy strategy, which trades information rates for BER, under delay and classification accuracy constraints.

Goal-oriented communications aim to deploy efficient learning architectures at the wireless edge, by parsimoniously using transmission resources, while possibly guaranteeing prescribed learning accuracy and delay. Aim of this paper is to propose a practical goal-oriented communications framework, which exploits an OFDM physical layer to cope with frequency selective fading channels. Targeting an image classification task, where images are scheduled according to an unknown arrival process, we propose a dynamic and goal-oriented data-loading of the wireless OFDM link that, exploiting banks of convolutional encoders at the user equipment, is capable to extract data features, with time-adaptive low dimension, that are meaningful to the classification task performed at the edge-server. The transmission of these features to the edge-server is dynamically optimized by exploiting knowledge of the channel state, computation load and images backlog, resorting to a Lyapunov stochastic optimization framework, which jointly optimizes the communication resources, the computation energy, and the encoders to be used. Specifically, we propose a minimum energy strategy under prescribed delay and accuracy of the edge-server learning task. Simulation results testify the effectiveness of the proposed strategy.

We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: 1) the accuracy of the recovery procedure and 2) the stability of the batteries around a prescribed operating level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet-of-Things scenarios.

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and financial network signals are correlated in both domains. Hence, in this work, we develop a sampling theory for time varying graph signals, named graph processes, to observe and track a process described by a linear state-space model. We provide a mathematical analysis to highlight the role of the graph, process bandwidth, and sample locations. We also propose sampling strategies that exploit the coupling between the topology and the corresponding process. Numerical experiments corroborate our theory and show the proposed methods trade well the number of samples with accuracy.

We study decentralized estimation of time-varying signals at a fusion center (FC), when energy harvesting sensors transmit sampled data over rate-constrained links. We propose a dynamic strategy based on stochastic optimization for selecting radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal with guaranteed performance while ensuring stability of the batteries around a prescribed operating level. Numerical results validate the proposed approach for dynamic signal estimation under communication and energy constraints.

We study centralized interpolation of bandlimited graph signals at a fusion center (FC), when sampled data are transmitted over rate-constrained links. In such a scenario, the performance of the reconstruction task is inevitably affected by several sources of errors such as observation noise and quantization due to source encoding. In this paper, we propose two strategies for optimally selecting transmission powers, quantization bits, and the sampling set, with the aim of interpolating a graph signal with guaranteed performance. Numerical results validate the proposed approach for interpolation of bandlimited graph signals under communication constraints.

This paper deals with a smart sensing strategy in an overlay cognitive radio network, to effectively exploit secondary-user (SU) transmitting opportunities, while preserving primary-user (PU) interference. Specifically, we consider a non-periodic sensing policy characterized by different transmitting and silence periods, and we derive closed-form expressions for the average SU throughput and interference in AWGN and fading channels, assuming a Poisson alternating renewal process for the PU traffic. Our analysis, confirmed by computer simulations, shows that the proposed strategy can increase the throughput with respect to a classical periodic sensing policy. Furthermore, optimal transmitting and silence periods can be identified for a given sensing time, trading off throughput for PU interference.

This letter derives the minimum mean square error (MMSE) Bayesian estimator for a Gaussian source impaired by additive Middleton's Class-A impulsive noise. Additionally, as low-complex alternatives, the letter considers two popular suboptimal estimators, such as the soft-limiter and the blanker. The optimum MMSE thresholds for these suboptimal estimators are obtained by iteratively solving fixed point equations. The theoretical findings are corroborated by simulation results, which highlight the MSE performance penalty of the suboptimal estimators may be negligible with respect to the optimal Bayesian estimator (OBE). Noteworthy, the proposed estimators can be extended to any noise, or observation error, that can be modeled as a Gaussian-mixture.

We investigate the error probability for Alamouti space-time coded orthogonal frequency-division multiplexing (OFDM) systems with carrier frequency offset (CFO) and multipath fading channels. Our main assumptions are: frequency-selective Rayleigh fading, a single receive antenna, and coherent detection. We derive new closed-form results for the (symbol or bit) error probability, for both spatially uncorrelated channels and spatially correlated channels. The proposed analysis shows that the error floor due to the CFO is strongly dependent on the power-delay profile of the multipath channel. Simulation results confirm the accuracy of the proposed theoretical analysis.

This paper analyzes the quality of service (QoS) of scheduling algorithms for heterogeneous users in multi-user (MU) wireless systems that take advantage from a cross-layer design with both adaptive modulation and coding (AMC) and automatic repeat request (ARQ). By developing a general theoretical framework based on a finite-state Markov chain, we analytically evaluate the average delay, the packet-loss rate (PLR) and the throughput of a scheduling algorithm based on the channel condition, the buffer occupancy, and the number of retransmissions, of users belonging to different service classes. The key assumption of our analysis, i.e., the independence of the stationary states of different users, highly reduces the computational complexity while preserving a sufficient accuracy. To present the proposed analysis, we also suggest an effective scheduling policy suitable for users belonging to different service classes, compliant with the WiMAX standard. The good match between analytical and simulated performance validates our theoretical findings, and enables the proposed approach to be used for cross-layer optimization.

A multibeam satellite system can be modelled as a multiple-input multiple-output (MIMO) system with an inter-beam interference matrix that is derived from the positions of the users in the different beams. This way it is possible to apply precoding techniques, such as Tomlinson-Harashima precoding (THP), to mitigate the interference. This paper presents an analytical framework to model the interference by a simple and effective parameter, which enables to assess the uncoded bit error rate (BER) of THP in such a scenario, and to derive useful hints on the optimization of the overall system capacity.

This paper deals with the analytical evaluation of the average delay, the packet-loss rate (PLR) and the throughput of a multi-user (MU) wireless system that capitalizes on a cross-layer design of adaptive modulation and coding (AMC) with automatic repeat request (ARQ). To this end, we propose a heuristic scheduling policy, which has the nice properties to effectively exploit the system resources and to be analytically tractable. Simulation results confirm the effectiveness of the proposed scheduler. Moreover, the excellent match between analytical and simulated performance allows the proposed approach to be used for cross-layer design optimization, avoiding time-consuming simulations.

We compare some block equalizers for single-carrier satellite systems subject to hard propagation conditions such as high Doppler spread, non-line-of-sight (NLOS), and non-negligible multipath fading. These impairments are relevant when the very high speed of a mobile receiver forces to use a non-directional antenna. First, we compare low-complexity time-domain and frequency-domain block equalizers. Then, we propose a novel frequency-domain equalizer based on a fast Fourier transform (FFT) with size higher than the length of the processed block. Thanks to the increased frequency resolution, the proposed equalizer yields improved performance with respect to the conventional frequency-domain equalizer. Simulation results validate the effectiveness of the proposed technique.

This paper considers a pilot-aided/data-aided Kalman channel estimator for OFDM in fast time-varying (TV) channels. Capitalizing on a basis expansion model (BEM) and on a frequency-domain estimation philosophy, the OFDM system is designed to periodically switch between a pilot-aided and a data-aided mode in order to reduce the rate penalty introduced by the training pilots. The suggested philosophy is effective in tracking the channel changes in the data-aided mode with a negligible channel mean squared error (MSE) penalty, if the Kalman filter prediction capability is coupled with an iterative data-aided estimation that is equipped by an opportune selection of the detected data. Appropriate data selection metrics for LMMSE and DFE equalizers are also provided, and the impact of the proposed channel estimation on the ultimate BER performance of each equalizer is investigated by simulations.

This paper develops a novel simulation model to characterize the bit error rate (BER) performance of coded orthogonal frequency-division modulation (OFDM) systems affected by Doppler spread. This simulation model, which acts directly in the frequency domain, greatly enhances the simulation efficiency, while maintaining a very good accuracy. By this model, the BER of two of the most popular wireless broadcasting standards, digital audio broadcasting (DAB) and terrestrial digital video broadcasting (DVB-T), has been assessed in Rayleigh and Rice channels. Simulation results show that the delay spread of the channel heavily affects the performance of DVB-T, while DAB is less sensitive to the frequency-selectivity of the channel. Moreover, DVB-T yields better performance than DAB in typical Rayleigh channels, whereas in Rice channels, for high signal-to-noise ratio (SNR), DAB outperforms DVB-T.

Multicarrier systems are seriously affected by time-varying frequency-selective channels. Recently, windowing and decision-feedback equalization (DFE) have separately been proven to boost the performance of minimum mean-squared error (MMSE) block equalization, while maintaining a very low complexity by capitalizing on a band LDL factorization. This paper jointly considers receiver windowing and DFE, as well as the impact on the performance of a pilot-based frequency-domain channel estimation technique that relies on a basis expansion model (BEM) approach. We show that the combination of windowing and DFE still allows the use of a low-complexity band LDL factorization. Therefore, we can further improve the BER of OFDM systems affected by severe Doppler spread, while preserving linear complexity in the number of subcarriers

In this paper, we deal with channel estimation for Orthogonal Frequency-Division Multiplexing (OFDM) systems. The channels are assumed to be Time-Varying (TV) and approximated by a Basis Expansion Model (BEM). Due to the time-variation, the resulting channel matrix in the frequency domain is no longer diagonal, but can be approximated as banded. Based on this band approximation, we propose novel channel estimators to combat both the noise and the out-of-band interference. Our claims are supported by simulation results, which are obtained based on realistic TV channels with a fairly high Doppler spread.

Abstract-A multicarrier direct-sequence code-division multiple-access (MC-DS-CDMA) downlink system with linear precoding over a group of subcarriers is considered. This scheme preserves user orthogonality independently of the underlying frequency-selective channel, collects the channel diversity and enables low-complexity decoding. In this context, we examine a local maximum-likelihood (LML) detection technique that searches for the maximum-likelihood (ML) solution in the neighborhood of the output provided by the minimum mean-squared error (MMSE) detector. By exploiting the soft information of the MMSE detector output and the precoder structure, we introduce useful criteria to reduce the computational complexity of the LML search. Simulations illustrate that the LML-MMSE detector with minimum neighborhood size yields considerable BER improvement with respect to MMSE, and outperforms a block decision-feedback equalization (DFE) approach at comparable complexity.

We present a low-complexity equalizer for multicarrier code-division multiple-access (MC-CDMA) downlink systems over time-varying (TV) multipath channels with non-negligible Doppler spread. The equalization algorithm, which is based on a block minimum mean-squared error (MMSE) approach, exploits the band structure of the frequency-domain channel matrix by means of a band LDL/sup H/ factorization. The complexity of the proposed block MMSE equalizer is linear in the number of subcarriers, and smaller with respect to a serial MMSE equalizer characterized by a similar performance.

We introduce an analytical approach to evaluate the error probability of orthogonal frequency-division-multiplexing (OFDM) systems subject to carrier frequency offset (CFO) in frequency-selective channels, characterized by Rayleigh or Rician fading. By properly exploiting the Gaussian approximation of the intercarrier interference (ICI), we show that the bit-error rate (BER) for an uncoded OFDM system with quadrature amplitude modulation (QAM) can be expressed by the sum of a few integrals, whose number depends on the constellation size. Each integral can be evaluated numerically, or, in Rayleigh fading, by using a series expansion that involves generalized hypergeometric functions. Simulation results illustrate that the theoretical analysis is quite accurate, especially for Rayleigh channels, and also with nonlinear amplifiers.

Multicarrier systems are highly sensitive to the intercarrier interference (ICI) caused by a carrier frequency offset (CFO) at the receiver and to the intermodulation distortion (IMD) introduced by the nonlinear amplifier (NLA) at the transmitter. We analyze how these impairments degrade the bit-error rate (BER) of multicarrier direct-sequence code-division multiple-access (MC-DS-CDMA) downlink systems in frequency-selective Rayleigh fading channels.

This paper presents an analytical approach to evaluate the error probability of orthogonal frequency-division multiplexing (OFDM) systems subject to carrier frequency offset (CFO) in frequency-selective channels, characterized by Rayleigh or Rician fading. By exploiting the Gaussian approximation of the intercarrier interference (ICI), we show that the bit-error rate (BER) for quadrature amplitude modulation (QAM) can be expressed by the sum of few integrals, whose number depends on the constellation size. Each integral can be evaluated numerically, or, in Rayleigh fading, by using a series expansion that involves generalized hypergeometric functions. Simulation results illustrate that the theoretical analysis is quite accurate, especially for Rayleigh channels.

Linear precoding is a well known effective technique to boost the performance of orthogonal frequency-division multiplexing (OFDM) systems. A drawback of linearly precoded OFDM (LP-OFDM) systems is the high computational complexity required by maximum-likelihood (ML) detection, which is mandatory to capture all the channel diversity. Conversely, low-complexity techniques, such as the linear minimum mean-squared error (MMSE) detection, suffer from nonnegligible performance loss with respect to the ML performance. This paper proposes a detection technique that performs a local ML (LML) search in the neighborhood of the output provided by the MMSE detector. The trade-off between performance and complexity of the proposed LML-MMSE detector, which fall between the ones of the MMSE and ML detectors, can be nicely adjusted by appropriately setting the neighborhood size. Simulation results show that the LML-MMSE detector with minimum neighborhood size outperforms a block decision-feedback equalization (DFE) approach, while preserving a similar complexity.

We analyze the effects of the nonlinear distortions, introduced by the transmitting high-power amplifier (HPA), on the performance of the linear decorrelating multiuser detector in direct-sequence code-division multiple-access (DS-CDMA) downlink systems. By assuming and motivating the Gaussian distribution of the nonlinear distortion noise, the symbol-error rate (SER) and the total degradation (TD) has been derived theoretically in additive white Gaussian noise (AWGN) and frequency-flat Rayleigh fading channels. Simulation results show that the analytical model is quite accurate in many scenarios.

The linear minimum mean-squared error (MMSE) detector for direct-sequence code-division multiple-access (DS-CDMA) systems relies on the inverse of the covariance matrix of the received signal. In multiuser environments, the matrix ill-conditioning may produce large performance degradation. In order to cope with this effect, we propose a modified MMSE detector based on the covariance matrix tapering (CMT). This regularization technique modifies the estimated covariance matrix, thereby reducing its eigenvalue spread. Two different tapering matrices are introduced. The performance comparison with other regularization techniques is carried out by simulations, which show the effectiveness of the proposed technique.

This paper presents an analytical framework to calculate the average symbol-error rate (SER) of uncoded orthogonal frequency-division multiplexing (OFDM) systems in realistic scenarios impaired by transmitter nonlinearity and frequency-selective fading channels. The results are applicable to cyclically extended OFDM signals characterized by a high number of carriers, which can be modeled as complex Gaussian processes. To avoid intercarrier interference, we also assume that the symbol duration is shorter than the channel coherence time. We derive analytical SER results in Rayleigh and Rice frequency-selective fading channels, for both the nonlinear amplification and the ideal predistortion case. Simulations results demonstrate the validity of the analytical results.

The aim of this paper is to examine the effects of the nonlinear distortions introduced by high-power amplifiers (HPAs) in the downlink of direct-sequence code-division multiple-access (DS-CDMA) systems. By modelling the nonlinear distortion noise as coloured Gaussian noise, a semi-analytical expression for the symbol-error rate (SER) of linear multiuser detectors (LMDs) in frequency-selective fading channels has been derived. Simulation results confirm the effectiveness of the theoretical approach.

Abstract- This paper analyses the effects of the nonlinear distortions introduced by high-power amplifiers (HPAs) on the performance of the linear decorrelating multiuser detector (MUD) in synchronous direct-sequence code-division multiple-access (DS-CDMA) systems. By assuming and motivating the Gaussian distribution of the nonlinear distortion noise, the system bit-error rate (BER) has been derived theoretically in additive white Gaussian noise (AWGN) and frequency-flat Rayleigh fading channels. Simulation results are provided in order to validate the theoretical analysis for ideally predistorted amplifiers. I.

The minimum mean square error (MMSE) multiuser detector has received great attention as the optimum linear solution for reducing the multiple access interference (MAI) in DS-CDMA systems. In asynchronous frequency selective channels the covariance matrix estimation errors may introduce BER performance degradation. The aim of this paper is to outline such degradation and to improve the MMSE receiver performance by convenient covariance matrix estimations. Two different approaches are introduced. Simulation results are shown in order to validate the effectiveness of the proposed techniques.

Orthogonal frequency-division multiplexing (OFDM) baseband signals may be modeled by complex Gaussian processes with Rayleigh envelope distribution and uniform phase distribution, if the number of carriers is sufficiently large. The output correlation function of instantaneous nonlinear amplifiers and the signal-to-distortion ratio can be derived and expressed in an easy way. As a consequence, the output spectrum and the bit-error rate (BER) performance of OFDM systems in nonlinear additive white Gaussian noise channels are predictable both for uncompensated amplitude modulation/amplitude modulation (AM/AM) and amplitude modulation/pulse modulation (AM/PM) distortions and for ideal predistortion. The aim of this work is to obtain the analytical expressions for the output correlation function of a nonlinear device and for the BER performance. The results in closed-form solutions are derived for AM/AM and AM/PM curves approximated by Bessel series expansion and for the ideal predistortion case.