SNR Regime Research Articles

Multiple RISs Assisted Cell-Free Networks With Two-Timescale CSI: Performance Analysis and System Design

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(M)$ </tex-math></inline-formula> is achieved, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively.

Efficient Partial Rewind of Successive Cancellation-Based Decoders for Polar Codes

The successive cancellation (SC) process in which symbols are decoded sequentially by processing some intermediate information is an essential component of various decoding algorithms used for polar codes and their variants. In some decoding schemes, we may need to redo this process from some specific symbol or from the first symbol. This operation is called rewinding. Rewinding the SC process seems trivial if we have access to all intermediate log-likelihood ratios (LLRs) and partial sums. However, as the block length increases, retaining all of the intermediate information becomes inefficient and impractical. Rewinding the SC process in a memory-efficient way is a problem that we address in this paper. As we store a fraction of all the intermediate information in the memory-efficient scheme, we may not be able to rewind the SC process to the target symbol index. The reason is that some of the stored intermediate information needed to decode the target symbol may have been overwritten. To recompute the lost information, we may need to rewind the process further. Before proposing the formal scheme for the rewinding process, we explore the known properties of the SC process based on the binary representation of the bit indices. Then, we introduce a new operator used for grouping the bit indices. This special grouping helps us in finding the closest bit index to the target index for rewinding. We also analytically prove that this approach gives access to the untouched intermediate information stored in the memory which is essential in resuming the SC process. Finally, we adapt the proposed approach to multiple rewinds and apply it to SC-flip decoding and shifted-pruning-based list decoding. The numerical evaluation of the proposed solution shows a significant reduction of ≥50% in the complexity of the additional decoding attempts at medium and high SNR regimes for SC-flip decoding and less for shifted-pruning based list decoding.

Secrecy performance analysis in the FSO communication system considering different eavesdropping scenarios.

In this paper, we numerically study the secrecy performance of the free-space optical (FSO) system by considering different eavesdropping scenarios. More precisely, we considered three possible eavesdropping scenarios for Eve: 1) Eve is between Alice and Bob; 2) Eve and Bob are in the same receiving plane; 3) Eve is behind Bob. We adopt the Málaga (M)-distribution channel to model atmospheric turbulence due to the presence of link blockage while considering the non-zero boresight pointing error and path loss. To do so, we obtain a novel probability density function (PDF) and cumulative distribution function (CDF) under different eavesdropping scenarios, based on which we derived the secrecy outage probability (SOP) analytical expressions as well as their asymptotic expressions at a high SNR regime. We verified the results using Monte Carlo simulations, which showed that the parameters related to atmospheric turbulence and pointing errors, as well as the location of the eavesdropper, have different effects on different eavesdropping scenarios.

Capacity Performance Analysis of 73 GHz Frequency Band for 5G Technology

Small cells, millimeter waves (mmW), and massive multiple-input multiple-output (MIMO) deployments have emerged as key technologies for mobile systems in the fifth generation (5G). However, a very few studies have been done on combining these three technologies into the cellular systems. In this paper, the authors provide an in-depth capacity analysis for the integrated small cells of mmW systems. Small cells are deployed for enhancing the capacity. It turns out that mmW signals are responsive to blockages, leading the line of sight (LOS) and non-line of sight (NLOS) conditions to have very different path loss rules. They divide power research into low signal-to-noise (SNR) and high SNR regimes based on signal-to-interference plus noise ratio. In the noise-dominated (low-SNR regime), the capacity analysis is derived by the simplest assumptions of the Shannon-Hartley theorem. The results of this study show that under NLOS and LOS scenarios, mmW frequency and distance between the user equipment and base station decrease logarithmically for system capacity.

Estimating axial diffusivity in the NODDI model

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2−2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

Distributed Antenna-and-Relay Selection Schemes for MIMO Cooperative Relay Network

Relay selection, antenna selection, and link selection are three potent means to enrich network capacity in MIMO relaying networks. To reduce feedback overhead, in this paper, three distributed antenna-and-relay selection schemes are proposed for AF multiple-relay network, which is equipped with multiple antennas at nodes. Closed-form formulations of system outage probability are derived for these schemes, as well as the lower bounds and upper bounds. Furthermore, the approximate expressions of outage probability at high SNR regime are also derived, showing that the proposed optimal strategy can achieve full diversity order. In addition, link selection scheme can improve spectral efficiency since it saves one time slot when direct link is selected.

Compressed Random Access for Noncoherent Massive Machine-Type Communications With Energy Modulation

Massive machine-type communications (mMTC) for the Internet of Things (IoT) are expected to support a large number of devices/users for short packet transmissions with low complexity and low energy consumption. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the desired data for mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to eliminate the multi-user interference, and a denoiser is designed to minimize the mean-squared error (MSE) of the transmitted signals. Then, maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">posteriori</i> (MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellation for each user is designed, and it is shown to be asymptotically optimal as the number of the receiver antennas goes to infinity. Finally, simulation results reveal that the proposed noncoherent scheme outperforms the coherent one in the low SNR regime and for short packet transmissions.

Energy Efficient Hybrid Relay-IRS-Aided Wireless IoT Network for 6G Communications

Intelligent Reflecting Surfaces (IRS) have been recognized as presenting a highly energy-efficient and optimal solution for future fast-growing 6G communication systems by reflecting the incident signal towards the receiver. The large number of Internet of Things (IoT) devices are distributed randomly in order to serve users while providing a high data rate, seamless data transfer, and Quality of Service (QoS). The major challenge in satisfying the above requirements is the energy consumed by IoT network. Hence, in this paper, we examine the energy-efficiency (EE) of a hybrid relay-IRS-aided wireless IoT network for 6G communications. In our analysis, we study the EE performance of IRS-aided and DF relay-aided IoT networks separately, as well as a hybrid relay-IRS-aided IoT network. Our numerical results showed that the EE of the hybrid relay-IRS-aided system has better performance than both the conventional relay and the IRS-aided IoT network. Furthermore, we realized that the multiple IRS blocks can beat the relay in a high SNR regime, which results in lower hardware costs and reduced power consumption.

An Improved Sliding Matched Filter Method for Interrupted Sampling Repeater Jamming Suppression Based on Jamming Reconstruction

The development of the digital radio frequency memory (DRFM) has led to the interrupted sampling repeater jamming (ISRJ) becoming increasingly popular in electronic countermeasure (ECM). It is coherent with the emitted signal and extremely limits radar target detection which significantly obstructs radar electronic countercountermeasure (ECCM). In this paper, we study the ISRJ suppression for pseudo random code continuous wave (PRC-CW) radars. First, the relationship between the ISRJ and the radar waveform is obtained by analyzing the ISRJ principle. Second, the intermittent feature of the ISRJ with matched filter sliding is discussed and used to determine the retransmitted sampled slices (RSS). Third, the jamming signal is reconstructed using the minimum residual criterion and excluded from the echo signal. In the proposed method, we make the most of the information of the jamming signal for improving the anti-ISRJ performance in the low SNR regimes. This information pertains to the amplitude of the jamming signal being considerably higher than that of the real target signal. Fourth, utilizing this characteristic of the jamming signal, we propose an improved sliding matched filter method based on the RSS reconstruction. Last, numerical simulations illustrate the effectiveness of the proposed method and validation of the theoretical analysis.

Likelihood Maximization and Moment Matching in Low SNR Gaussian Mixture Models

AbstractWe derive an asymptotic expansion for the log‐likelihood of Gaussian mixture models (GMMs) with equal covariance matrices in the low signal‐to‐noise regime. The expansion reveals an intimate connection between two types of algorithms for parameter estimation: the method of moments and likelihood optimizing algorithms such as Expectation‐Maximization (EM). We show that likelihood optimization in the low SNR regime reduces to a sequence of least squares optimization problems that match the moments of the estimate to the ground truth moments one by one. This connection is a stepping stone towards the analysis of EM and maximum likelihood estimation in a wide range of models. A motivating application for the study of low SNR mixture models is cryo‐electron microscopy data, which can be modeled as a GMM with algebraic constraints imposed on the mixture centers. We discuss the application of our expansion to algebraically constrained GMMs, among other example models of interest. © 2022 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.

An Investigation of the MIMO Space Time Block Code Based Selective Decode and Forward Relaying Network over n-u Fading Channel Conditions

In this paper, we examine the end-to-end average pairwise error probability (PEP) and output probability (OP) performance of the maximum ratio combining (MRC) based selective decode and forward (S-DF) system over an η–µ scattering environment considering additive white Gaussian noise (AWGN). The probability distribution function (PDF) and cumulative distribution function (CDF) expressions have been derived for the received signal-to-noise (SNR) ratio and the moment generating function (MGF) technique is used to derive the novel closed-form (CF) average PEP and OP expressions. The analytical results have been further simplified and are presented in terms of the Lauricella function for coherent complex modulation schemes. The asymptotic PEP expressions are also derived in terms of the Lauricella function, and a convex optimization (CO) framework has been developed for obtaining optimal power allocation (OPA) factors. Through simulations, it is also proven that, depending on the number of multi-path clusters and the modulation scheme used, the optimized power allocation system was essentially independent of the power relation scattered waves from the source node (SN) to the destination node (DN). The graphs show that asymptotic and accurate formulations are closely matched for moderate and high SNR regimes. PEP performance significantly improves with an increase in the value of η for a fixed value of µ. The analytical and simulation curves are in close agreement for medium-to-high SNR values.

Performance analysis of non‐orthogonal multiple access assisted cooperative maritime communication system over two‐wave with diffuse power fading

AbstractNon‐orthogonal multiple access (NOMA) and millimeter‐wave (mmWave) communication are two promising technologies to fulfill the high throughput and reliable communication requirements. Maritime communication systems use a generalized fading at near‐sea‐surface channels (for seashore to ship/boat/fishing fleet communication) to provide reliable communication due to atmospheric turbulence. Experimental research on generalized two‐wave diffuse power (TWDP) fading reveal the footprints of mmWave. This article proposes novel exact and asymptotic closed‐form expressions of average symbol error probability (ASEP) of NOMA users for cooperative maritime communication systems over TWDP fading with the different modulations. The end‐to‐end closed‐form expressions are derived in terms of Appell's and Lauricella's hypergeometric functions. Also, the ASEP is shown using the power allocation factor for different modulation schemes. The proposed system provides better transmission reliability using generalized TWDP fading. Further, the outage probability of the system described above is analyzed using the distance, path loss exponent, transmission rate, and Rician factor as performance metrics for both the NOMA users. Effects of fading outage parameters on the system performance are studied. The end‐to‐end exact numerical results have been verified with the Monte‐Carlo simulations that validate with asymptotic results at a high SNR regime.

Performance Analysis of UAV-Enabled Over-the-Air Computation Under Imperfect Channel Estimation

Over-the-air computation (AirComp) is a powerful technique to combine data collected from an enormous number of nodes with limited bandwidth availability. When unmanned aerial vehicles are involved in AirComp as flying fusion centers (FFCs), ultrafast data aggregation can be achieved for ubiquitous sensing and environmental surveillance applications. However, one of the main challenges in AirComp is channel estimation. Whereas most of existing studies assume perfect channel estimation, in this letter, considering the common channel estimation approach using reference signals sent by the FFC, we investigate how imperfect channel state information (CSI) impacts the mean square error (MSE) performance of AirComp. We derive a closed-form expression of MSE, in which the degradation due to the amplitude and phase errors can be identified. Both analytical and simulation results show that the MSE floors are incurred by the imperfect CSI even in the high SNR regime. We observe that the average MSE becomes about 100 times worse compared to that with perfect CSI, which exhibits the significance of accurate channel estimation.

Intelligent Reflecting Surface Aided Secure Communications for NOMA Networks

Intelligent reflecting surface (IRS) is deemed a promising technique for future wireless and mobile technologies due to its ability to reconfigure the radio propagation environment. In this paper, we analyze the physical layer security (PLS) of a downlink non-orthogonal multiple access (NOMA) network through an IRS intended for assisting the wireless transmission, taking into account the external and internal eavesdropping scenarios. Considering the hardware limitations, a 1-bit coding scheme is utilized to investigate the secrecy performance of the IRS-aided NOMA (IRS-NOMA) network. Exact and asymptotic expressions in terms of the secrecy outage probability (SOP) and the effective secrecy throughput (EST) are presented, which are in consideration of the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC). Based on the analytical results, the secrecy diversity orders are related to the number of reflecting elements and EST ceilings are observed in the high SNR regime. Monte Carlo simulations are presented to corroborate the correctness of the analyses and illustrate that the IRS-NOMA scheme is superior in the secrecy outage performance to the IRS-aided orthogonal multiple access scheme. In addition, the SOP performance enhancement through the increased number of reflecting elements and the improvement of EST through proper target secrecy rate are also evidenced by the simulations.

Adaptive Dijkstra’s Search Algorithm for MIMO detection

Employing Maximum Likelihood (ML) algorithm for signal detection in a large-scale Multiple-Input- Multiple-Output (MIMO) system with high modulation order is a computationally expensive approach. In this paper an adaptive best first search detection algorithm is proposed. The proposed Adaptive Dijkstra’s Search (ADS) algorithm exploits the resources available in the search procedure to reduce the required number of nodes to be visited in the tree. A tunable parameter is used to control the number of the best possible candidate nodes required. Unlike the conventional DS, the ADS algorithm results in signal detection with low computation complexity and quasi-optimal performance for systems under low and medium SNR regimes. Simulation results demonstrate a 25% computational complexity reduction, compared to the conventional DS.

Enhanced Signal Area Estimation Based on Edge Detection and Flood Fill

Spectrograms are a common form of time-frequency representation of wireless communication signals. In many practical scenarios spectrograms need to be processed to identify accurately the time-frequency region occupied by each individual radio transmission, which in this work is referred to as Signal Area (SA). Several methods have been proposed in the literature for Signal Area Estimation (SAE), however their performance degrades significantly in the low SNR regime. In this context, this work proposes a novel approach for SAE based on the use of two well-known techniques from the field of image processing, namely edge detection and flood fill. Edge detection is first employed to identify the edges of potential SAs and flood fill is then used to fill the area inside the estimated edges in order to produce a more accurate estimation of the SAs present in a spectrogram. The performance of three popular edge detection methods (gradient magnitude, Laplacian of Gaussian and Canny) is assessed both with simulations and experimental data. The obtained results show that the proposed strategy can improve significantly the performance of existing SAE methods in the low SNR regime (with estimation accuracy improvements up to 38&#x2013;45&#x0025; within the SNR interval from &#x2212;20 dB to &#x2212;10 dB) when used as a pre/post-processing stage, thus improving their sensitivity and effectively extending their overall SNR range of operation.

Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing

Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we consider a classical hypothesis testing problem in order to develop fundamental insight into defending against such adversarial perturbations. We interpret an adversarial perturbation as a nuisance parameter, and propose a defense based on applying the generalized likelihood ratio test (GLRT) to the resulting composite hypothesis testing problem, jointly estimating the class of interest and the adversarial perturbation. While the GLRT approach is applicable to general multi-class hypothesis testing, we first evaluate it for binary hypothesis testing in white Gaussian noise under $\ell_{\infty}$ norm-bounded adversarial perturbations, for which a known minimax defense optimizing for the worst-case attack provides a benchmark. We derive the worst-case attack for the GLRT defense, and show that its asymptotic performance (as the dimension of the data increases) approaches that of the minimax defense. For non-asymptotic regimes, we show via simulations that the GLRT defense is competitive with the minimax approach under the worst-case attack, while yielding a better robustness-accuracy tradeoff under weaker attacks. We also illustrate the GLRT approach for a multi-class hypothesis testing problem, for which a minimax strategy is not known, evaluating its performance under both noise-agnostic and noise-aware adversarial settings, by providing a method to find optimal noise-aware attacks, and heuristics to find noise-agnostic attacks that are close to optimal in the high SNR regime.

Joint Design of Improved Spectrum and Energy Efficiency With Backscatter NOMA for IoT

To develop emerging transmission techniques for potential applications of Internet of Things (IoT), the system performance analysis of a cognitive radio (CR)-enabled ambient backscatter (AmBC) system will be studied in this paper with functionality of non-orthogonal multiple access (NOMA). In the proposed scheme, a base station communicates with two destinations via a designated backscatter device. It is assumed that the relay node is fitted with two different interfaces and can simultaneously collect/decode and backscatter the received source signals. Such transmission mechanism benefits to design various applications in IoT as well as wireless systems with improved performance. To exhibit system performance metrics, the outage probability and the ergodic capacity of the recipient nodes are derived analytically. Furthermore, it is shown that employing AmBC NOMA together with CR for secondary communication can significantly improve overall network performance in terms of the achievable throughput in delay-limited and delay-tolerant modes and outage probability. Numerical results show that: <i>1) The proposed system can improve the spectrum efficiency by employing both CR and NOMA techniques; 2) Compared with the orthogonal multiple access (OMA)-aided AmBC systems, the considered CR AmBC system relying on NOMA can obtain better reliability in the whole range of SNR; 3) There are error floors for the outage probability in the high SNR regime due to required target rates; 4) There exists a trade-off between system performance of IoT devices and power allocation coefficients associated with NOMA; 5) We find energy efficiency factor as evident of further improvement in such system</i>.

An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime.

With the recent explosion in the number of wireless communication technologies, the frequency spectrum has become a scarce resource. The need of the hour is an efficient method to utilize the existing spectrum and Cognitive Radio is one such technology that can mitigate the spectrum scarcity. In a cognitive radio system, the unlicensed secondary user accesses the spectrum allotted to licensed primary users when it lies vacant. To implement dynamic or opportunistic access of spectrum, secondary users perform spectrum sensing, which is a quintessential part of a Cognitive radio. From the Cognitive user’s point of view, lesser error probability means an increased likelihood of channel reuse when it is vacant, and a higher detection probability signifies better protection to the licensed users. In both cases the decision threshold plays a pivotal role in determining the fate of the unused spectrum. In this paper, we study the difficulty of selecting an appropriate threshold to minimize the error probability in an uncertain low SNR regime. The sensing failure issue is analyzed, and an optimal threshold is computed that yields minimum error rate. An adaptive double threshold concept has been proposed to make the detection robust and a closed-form equation for optimal threshold has been derived to minimize the error. The novel findings through simulation results exhibit improvement in Probability of detection and reduction in probability of error at low SNR in the presence of noise uncertainty factor.

Cell-Free Massive MIMO Systems With Oscillator Phase Noise: Performance Analysis and Power Control

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising technology for satisfying higher rate requirement of users in beyond-5G networks. This paper investigates the impact of phase noise on the performance of cell-free MIMO systems which employ a large number of multi-antenna access points (APs). For both downlink and uplink transmissions, we derive the tractable closed-form achievable rate expressions when signal detection with channel state information (CSI) and channel distribution information (CDI), respectively, by utilizing the extended deterministic equivalence method in random matrix theory. The closed-form achievable rate expressions provide efficient evaluation for the impact of phase noise on system performance. The analytical results indicate that the phase noise influences the power of the desired signal, the beamforming uncertainty gain, and the inter-user interference increment caused by the pilot contamination. In addition, the special cases that only APs have phase noise and only users have phase noise are discussed. It is found that the impact of phase noise at users is more severe than that at APs. Besides, we discuss the system performance at low and high SNR regimes. It demonstrates that the phase noise is the major limitation of the system performance in high SNR regime while it can be neglected in low SNR regime. Furthermore, we formulate the max-min power control problem for both downlink and uplink transmissions to guarantee the users’ fairness and prove that the downlink max-min problem is a quasi-concave problem while the uplink max-min problem is a geometric programming (GP) problem. Then, the phase noise-aware max-min power control schemes for both downlink and uplink transmissions are proposed to ensure the users’ fairness. Finally, numerical results are provided to validate the analytical results and the proposed phase noise-aware power control scheme.