High Signal-to-noise Ratio Regime Research Articles

Energy Efficient Wireless Signal Detection: A Revisit through the Lens of Approximate Computing

In the pursuit of energy efficiency in next-generation communication systems, approximate computing is emerging as a promising technique. In the proposed work, efforts are made to address the challenge of bridging the gap between the level of approximation and the Quality-of-Service (QoS) of the system. The application of approximate multiplication to wireless signal detection is explored systematically, illustrated by employing Truncated Multiplication (TM) on Quadrature Phase Shift Keying (QPSK) Minimum Mean Square Error (MMSE) detection. The irregularities induced by approximation in the multiplication operation employed in wireless signal detection are captured by the Approximate Multiplication Noise (AMN) model, which aids in the analysis of signal fidelity and resiliency of the system. The energy efficiency gains through approximation are highlighted in the approximation analysis. Signal fidelity analysis provides the capability to predict system output for varying levels of approximation, which aids in improving the stability of the system. The higher approximation levels are advantageous in low Signal-to-Noise Ratio (SNR) regimes, whereas lower approximation levels prove beneficial in high SNR regimes.

Effective capacity of rate-splitting multiple access with channel estimation errors

In view of its advantages to adeptly reduce interference and low complexity, rate-splitting multiple access (RSMA) is emerging as an innovative framework for the next generation of the wireless communication networks. In this paper, we conduct the performance analysis of a two user RSMA network under delay constrains, taking channel estimation errors into account as well. Utilizing the effective capacity (EC) as our metric, we investigate the effects of delay on a pair of RSMA users. Specifically, we derive analytical expressions for the EC concerning both the near user (SRn) and the far user (SRf). Additionally, we offer asymptotic results under high signal-to-noise ratio (SNR) conditions. From our analysis, several noteworthy conclusions can be drawn and outlined below: (1) The effective capacities (ECs) of SRn and SRf exhibit improvement with SNR increasing, while at high SNR, the ECs of SRn and SRf converge to the constants. (2) Compared to the non-orthogonal multiple access, the considered RSMA network exhibits superior EC performance due to its inherent advantages in interference management. (3) The presence of channel estimation errors adversely affects EC performance of RSMA networks, with this impact being more pronounced in the high SNR regime.

Performance Analysis of NOMA-RIS Aided Integrated Navigation and Communication (INAC) Networks

Satellite communication constitutes a promising solution for the sixth generation (6 G) wireless networks in terms of providing global communication services. In order to provide a cost-effective satellite network, we propose a novel medium-earth-orbit (MEO) satellite aided integrated-navigation-and-communication (INAC) network. To overcome the severe path loss of MEO satellites, we conceive a network for simultaneous serving navigation and communication for ground users by adopting the non-orthogonal multiple access (NOMA) technique and the reconfigurable intelligent surface technique. Based on the power allocation strategies, communication-oriented (CO-) and navigation-oriented (NO-) INAC scenarios are proposed. We first derive the closed-form expressions for the new channel statistics, outage probability and channel capacity of the INAC-user. For gleaning further insights, the diversity orders and navigation accuracy are evaluated for illustrating the performance of the INAC networks. According to our analysis, when RIS elements are sufficient, the proposed INAC network can perform better than conventional terrestrial communication networks in terms of channel capacity. Numerical results are provided for confirming that the NO-INAC and CO-INAC scenarios have superior performance for communication in the low signal-to-noise-ratio (SNR) regimes and high SNR regimes, respectively, which indicates a hybrid CO/NO-INAC network is preferable.

Performance analysis of mixed IRS-aided RF-FSO system with pointing errors and link blockage

This research investigates the performance of the mixed intelligent reflecting surface (IRS) aided radio frequency-free space optical (RF-FSO) communication network system. IRS-aided RF link experiences Nakagami-m distribution, while FSO link undergoes F distribution turbulence with pointing errors and link blockage. Based on amplify-and-forward (AF) relay scheme, the end-to-end signal-to-noise ratio (SNR), the probability density function (PDF) and cumulative distribution function (CDF) are obtained. Novel expressions about the performance metrics including outage probability, average bit error rate (BER) and ergodic capacity are deduced and asymptotic expression for outage probability is also given at high SNR regime. By using these expressions, the system performance influenced by the number of IRS elements, pointing errors, blocking probability, atmospheric turbulence, detection techniques are further studied. The results demonstrate that IRS can effectively improve the mixed network performance. Eventually, using Monte Carlo simulations verify the results well.

Short-Packet Communications for UAV-Based NOMA Systems Under Imperfect CSI and SIC

This paper studies the performance of unmanned aerial vehicle (UAV)-aided short-packet non-orthogonal multiple access (NOMA) systems under imperfect channel state information (ipCSI) and successive interference cancellation (ipSIC). Closed-form expressions for the average block-error rate (BLER), throughput, goodput, latency, reliability, and age of information (AoI) are derived. To gain useful insights, an asymptotic BLER analysis is carried out at high signal-to-noise ratio (SNR) regime, from which users’ diversity order and the impact of the UAV’s trajectory are deduced. Three optimization problems are formulated in order to: 1) minimize the overall BLER of the user with high quality-of-service by optimizing the power allocation (PA) coefficients; 2) maximize the system throughput by jointly optimizing PA and coding rate (CR) coefficients; 3) minimize AoI by optimizing block-length transmission per-hop transmission. Numerical results show that, at high SNR regime, users’ performance is not influenced by UAVs’ radius and horizontal flight angle when ipCSI occurs. The proposed system achieves better throughput than the baseline schemes and can be further enhanced by jointly optimizing PA and CR. To achieve the minimum AoI, the source and UAV should transmit a moderate block-length for low transmit power, while for moderate and high transmit power, 100 is sufficient.

Ultra-Reliable and Low-Latency Multiple-Antenna Communications in the High SNR Regime

Emerging time-sensitive applications such as automated driving, factory automation, and telesurgery have stimulated an increased interest in Ultra-Reliable and Low-Latency Communications (URLLC). Meanwhile, Multiple-Input Multiple-Output (MIMO) techniques have played a vital role in radio access networks since the 2000s, due to their spatial multiplexing and diversity gains over fading channels. Thus, it is of interest to consider the fundamental performance limits of URLLC operating over MIMO channels. In this letter, we consider this problem in the asymptotic regime of high Signal-to-Noise Ratio (SNR) for short packets involving finite-blocklength codes. In particular, we show that a fundamental tradeoff exists among service capability, latency, and error probability in the high SNR regime. We present a piecewise linear gain conservation equation to characterize this cross-layer tradeoff, which is also validated by extensive numerical results.

Deep Learning of Damped AMP Decoding Networks for Sparse Superposition Codes via Annealing

This paper addresses short-length sparse superposition codes (SSCs) over the additive white Gaussian noise channel. Damped approximate message-passing (AMP) is used to decode short SSCs with zero-mean independent and identically distributed Gaussian dictionaries. To design damping factors in AMP via deep learning, this paper constructs deep-unfolded damped AMP decoding networks. An annealing method for deep learning is proposed for designing nearly optimal damping factors with high probability. In annealing, damping factors are first optimized via deep learning in the low signal-to-noise ratio (SNR) regime. Then, the obtained damping factors are set to the initial values in stochastic gradient descent, which optimizes damping factors for slightly larger SNR. Repeating this annealing process designs damping factors in the high SNR regime. Numerical simulations show that annealing mitigates fluctuation in learned damping factors and outperforms exhaustive search based on an iteration-independent damping factor.

Bayesian Over-the-Air Computation

As an important piece of the multi-tier computing architecture for future wireless networks, over-the-air computation (OAC) enables efficient function computation in multiple-access edge computing, where a fusion center aims to compute a function of the data distributed at edge devices. Existing OAC relies exclusively on the maximum likelihood (ML) estimation at the fusion center to recover the arithmetic sum of the transmitted signals from different devices. ML estimation, however, is much susceptible to noise. In particular, in the misaligned OAC where there are channel misalignments among received signals, ML estimation suffers from severe error propagation and noise enhancement. To address these challenges, this paper puts forth a Bayesian approach by letting each edge device transmit two pieces of statistical information to the fusion center such that Bayesian estimators can be devised to tackle the misalignments. Numerical and simulation results verify that, 1) For the aligned and synchronous OAC, our linear minimum mean squared error (LMMSE) estimator significantly outperforms the ML estimator. In the low signal-to-noise ratio (SNR) regime, the LMMSE estimator reduces the mean squared error (MSE) by at least 6 dB; in the high SNR regime, the LMMSE estimator lowers the error floor of MSE by 86.4%; 2) For the asynchronous OAC, our LMMSE and sum-product maximum a posteriori (SP-MAP) estimators are on an equal footing in terms of the MSE performance, and are significantly better than the ML estimator. Moreover, the SP-MAP estimator is computationally efficient, the complexity of which grows linearly with the packet length.

Effective capacity analysis of NOMA with transceiver hardware and SIC imperfections

In this paper, we study the performance of the non-orthogonal multiple access (NOMA) networks. By considering two practical factors of residual hardware impairments (RHIs) and imperfect serial interference cancellation (ipSIC), we adopt effective capacity as a metric to characterize the effects of latency on the performance of NOMA networks and derive the analytical expressions of the effective capacity for the near user (NU) and the far user (FU). For further insights, we provide asymptotic analysis by invoking high signal-to-noise ratio (SNR) slope and high SNR power offset. Numerous analytical and simulated results have shown that: (1) The effective capacities of NU and FU are positively proportional to the SNR at low SNR, while at high SNR, the effective capacities approach to the constants; (2) Comparing the two users of the considered NOMA network, the effective capacity of NU shows pronounced advantages under the requirements of low quality of service. (3) RHIs are detrimental to the effective capacities of both NU and FU, especially for the high SNR regime. (4) The effective capacity of NU is limited by ipSIC.

Time–frequency fusion for enhancement of deep learning-based physical layer identification

Existing studies on deep learning-based physical layer identification have mainly exploited raw in-phase/ quadrature (IQ) samples or power spectral density (PSD) samples as inputs independently. The raw IQ and PSD samples represent the information in the time and frequency domains, respectively. It has been observed from the results of existing studies that identification using raw IQ samples outperforms that using PSD in low signal-to-noise ratio (SNR) regimes, and that identification using PSD outperforms that using raw IQ in high SNR regimes. In this paper, we propose to use the fusion of raw IQ and PSD samples to enhance deep learning-based physical layer identification. In particular, we design three general fusion frameworks, i.e., input, feature, and decision fusions, and integrate them with three typical deep neural network architectures of fully connected neural network, convolutional neural network, and recurrent neural network to form fusion identification schemes. We conduct experiments using 50 off-the-shelf Wi-Fi devices to validate the concerned fusion schemes and investigate their performance gains in identification and model training. Our experimental results verify that the proposed fusion identification schemes can achieve comparable or superior identification performances to the state-of-the-art schemes in the entire SNR regime. Moreover, for the considered fusion schemes, we further investigate the impacts of fusion strategies, deep-learning networks, and SNR conditions on the identification performance and training time.

A General Framework for UAV-aided THz Communications Subject to Generalized Geometric Loss

In this article, we develop a general framework for the performance analysis of unmanned aerial vehicle (UAV) aided terahertz (THz) communications under generalized geometric loss, which considers the fluctuations of the UAV's position and orientation alongside the non-orthogonality of the THz beam with respect to the detector plane. In particular, we derive general and novel analytical expressions for the probability density function and the cumulative distribution function of the instantaneous signal-to-noise ratio (SNR) under generalized fading channels subject to generalized geometric loss. Consequently, novel, general, and accurate analytical expressions for the outage probability, average symbol error rate, and average channel capacity are derived. Furthermore, approximate asymptotic expressions in the high SNR regime are also derived. Moreover, A very accurate approximate expression for the channel capacity is also provided in the low SNR regime. As an example, the system performance under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> fading channel model is analyzed in terms of the above-mentioned performance metrics.

Performance Analysis of RIS Assisted NOMA Networks over Rician Fading Channels

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) network assisted by two reconfigurable intelligent surfaces (RISs) over Rician fading channels, in which each user communicates with the base station by the virtue of a RIS to enhance the reliability of the received signal. To evaluate the system performance of our proposed RIS-NOMA network, we first derive the exact and asymptotic expressions for the outage probability and ergodic rate of two users. Then, we derive the exact and asymptotic upper bound expressions for the ergodic rate of the nearby user. Based on asymptotic analytical results, the diversity orders for the outage probability and the high signal-to-noise ratio (SNR) slopes for the ergodic rate of the two users are obtained in the high SNR regime. Moreover, we derive the system throughputs of the proposed RIS-NOMA network in delay-limited and delay-tolerant transmission modes. Numerical results confirm our analysis and demonstrate that: 1) The outage probability and ergodic rate of RIS-NOMA networks are superior to that of RIS-assisted orthogonal multiple access (OMA) networks; 2) The RIS-NOMA networks have ability to achieve a larger system throughput compared to RIS-OMA networks; and 3) The system performance of RIS-NOMA networks can be significantly improved as the number of reflecting elements and Rician factor increases.

Performance Analysis of Reconfigurable Intelligent Surface Assisted Two-Way NOMA Networks

This article investigates the performance of reconfigurable intelligent surface assisted two-way non-orthogonal multiple access (RIS-TW-NOMA) networks, where a pair of users exchange their information through a RIS. The influence of imperfect successive interference cancellation on RIS-TW-NOMA is taken into account. To evaluate the potential performance of RIS-TW-NOMA, we derive the exact and asymptotic expressions of outage probability and ergodic rate for a pair of users. Based on the analytical results, the diversity orders and high signal-to-noise ratio (SNR) slopes are obtained in the high SNR regime, which are closely related to the number of RIS elements. Additionally, we analyze the system throughput and energy efficiency of RIS-TW-NOMA networks in both delay-limited and delay-tolerant transmission modes. Numerical results indicate that: 1) The outage behaviors and ergodic rate of RIS-TW-NOMA are superior to that of RIS-TW-OMA and two-way relay OMA (TWR-OMA); 2) As the number of RIS elements increases, the RIS-TW-NOMA networks are capable of achieving the enhanced outage performance; and 3) By comparing with RIS-TW-OMA and TWR-OMA networks, the energy efficiency and system throughput of RIS-TW-NOMA has obvious advantages.

Capacity Bounds for One-Bit MIMO Gaussian Channels With Analog Combining

The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising approach to significantly reduce the power consumption and hardware cost of multiple-input multiple-output (MIMO) receivers. However, the nonlinear distortion due to 1-bit quantization fundamentally changes the optimal communication strategy and also imposes a capacity penalty to the system. In this paper, the capacity of a Gaussian MIMO channel in which the antenna outputs are processed by an analog linear combiner and then quantized by a set of zero threshold ADCs is studied. A new capacity upper bound for the zero threshold case is established that is tighter than the bounds available in the literature. In addition, we propose an achievability scheme which configures the analog combiner to create parallel Gaussian channels with phase quantization at the output. Under this class of analog combiners, an algorithm is presented that identifies the analog combiner and input distribution that maximize the achievable rate. Numerical results are provided showing that the rate of the achievability scheme is tight in the low signal-to-noise ratio (SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs analog temporal and spatial processing is proposed. The proposed receiver attains the capacity in the high SNR regime.

On the Performance of Multihop THz Wireless System Over Mixed Channel Fading With Shadowing and Antenna Misalignment

The existing relay-assisted terahertz (THz) wireless system is limited to dual-hop transmission with pointing errors and short-term fading without considering the shadowing effect. This paper analyzes the performance of a multihop-assisted backhaul communication mixed with an access link under the shadowed fading with antenna misalignment errors. We derive novel probability density and distribution functions of the signal-to-noise ratio (SNR) of the multihop link employing channel-assisted (CA) and fixed-gain (FG) amplify-and-forward (AF) relaying for each hop by considering independent but not identically distributed (i.ni.d) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> fading channel with pointing errors. We present analytical expressions for the outage probability and average bit-error-rate (ABER) and develop asymptotic analysis in high SNR regime to derive the diversity order for both CA-multihop and FG-multihop backhaul links. We use the derived results of the multihop systems to analyze the statistical performance of the integrated backhaul-access relaying for both uplink and downlink transmission considering the generalized- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> shadowed fading model in the access link. We also present exact and asymptotic expressions of the outage probability for the considered mixed system. We perform computer simulations to provide design and deployment aspects of employing multiple relays to enhance THz wireless transmissions.

UAV-Enabled Non-Orthogonal Multiple Access Networks for Ground-Air-Ground Communications

Both unmanned aerial vehicle (UAV) and non-orthogonal multiple access (NOMA) have gradually become promising technologies for the fifth generation (5G) driven green Internet-of-Things (IoT) networks on account of their unique advantages of massive connections, higher spectral efficiency and flexibility. Motivated by this, we propose a 3-hop NOMA UAV-aided green communication network framework, where UAVs serve as aerial relays to support two groups of ground users. A stochastic geometry approach is invoked to model the spatial positions of the two group users. Under the realistic assumption, imperfect successive interference cancelation (ipSIC) is considered. To evaluate the performance of the proposed framework, theoretical expressions are derived to facilitate the outage performance evaluation of the far user (FU) and the near user (NU). Moreover, the asymptotic behaviors for the outage probability (OP) of both the FU and the NU in the high signal-to-noise ratio (SNR) regime are explored by obtaining diversity orders. Finally, the system throughputs under the delay-limited transmission mode are investigated. Numerical results confirm that: 1) For uplink transmission, there exist outage floors for the OP of both ipSIC and perfect SIC (pSIC) due to interference from the NU; 2) For downlink transmission, an outage floor exists for the OP of the NU under the condition of ipSIC; 3) For uplink NOMA/orthogonal multiple access (OMA) transmission, the outage performances of both the FU and the NU with NOMA outperform OMA in the low SNRs, while OMA has better performance in the high SNR regime; 4) For downlink NOMA/OMA, the outage performances of both the FU and the NU under pSIC outperform OMA.

NOMA-based cognitive radio network with hybrid FD/HD relay in industry 5.0

The continued growth of the industrial Internet of Things (IoT) paves the way for the fifth industrial revolution (Industry 5.0), which is expected to further enhance the wireless connectivity and human machine collaboration. Nonetheless, Industry 5.0 will inevitably face the great challenges such as large scale connectivity and explosive data traffic because of the proliferation of IoT devices. To meet these challenges, we propose a novel non-orthogonal multiple access (NOMA)-based overlay cognitive radio (OCR) network for Industry 5.0. Specifically, the hybrid full-duplex (FD)/ half-duplex (HD) mode at the secondary user (SR) is adopted and in-phase and quadrature phase imbalance (IQI) is introduced to better comply with the practical scenarios. We investigate the outage probability (OP) and ergodic sum rate (ESR) of the considered network with the Nakagami-m channels. Extensive simulation experiments are performed to verify the accuracy of the analysis. From the simulation results, we can conclude: i) Increasing the signal-to-noise ratio (SNR), OP decreases but ESR increases and tends to a fixed value at high SNR regimes, indicating that the existence of IQI leads to a ceiling for the ESR; ii) The primary system has higher reliable performance at low SNR regions when SR is in FD mode, and for ergodicity, the system with FD relay is superior to HD relay; iii) The reliable performance of OMA system for primary users will be better since there is no interference at the relay.

Outage Probability and Finite-SNR DMT Analysis for IRS-Aided MIMO Systems: How Large IRSs Need to be?

Intelligent reflecting surfaces (IRSs) are promising enablers for high-capacity wireless communication systems by constructing favorable channels between the transmitter and receiver. However, general, accurate, and tractable outage probability analysis for IRS-aided multiple-input-multiple-output (MIMO) systems is not available in the literature. In this paper, we first characterize the distribution of the mutual information (MI) for IRS-aided MIMO systems by capitalizing on large random matrix theory (RMT). Based on this result, a closed-form approximation for the outage probability is derived and a gradient-based algorithm is proposed to minimize the outage probability with statistical channel state information (CSI). We also investigate the diversity-multiplexing tradeoff (DMT) with finite signal-to-noise ratio (SNR). Based on these theoretical results, we further study the impact of the IRS size on system performance. In the high SNR regime, we provide closed-form expressions for the ergodic mutual information (EMI) and outage probability as a function of the IRS size, which analytically reveal that the benefit of increasing the IRS size saturates quickly. Simulation results validate the accuracy of the theoretical analysis and confirm the increasing cost to improve system performance by deploying larger IRSs. For example, for an IRS-aided MIMO system with 20 antennas at both the transmitter and receiver, we need to double the size of the IRS to increase the throughput from 90% to 95% of its maximum value.

Deconvolution-Based Partial Volume Correction for Volumetric Blood Flow Measurement.

Ultrasound-based blood flow (BF) monitoring is vital in the diagnosis and treatment of a variety of cardiovascular and neurologic conditions. Finite spatial resolution of clinical color flow (CF) systems, however, has hampered measurement of vessel cross Section areas. We propose a resolution enhancement technique that allows reliable determination of BF in small vessels. We leverage sparsity in the spatial distribution of the frequency spectrum of routinely collected CF data to blindly determine the point spread function (PSF) of the imaging system in a robust manner. The CF data are then deconvolved with the PSF, and the volumetric flow is computed using the resulting velocity profiles. Data were collected from phantom blood vessels with diameters between 2 and 6 mm using a clinical ultrasound system at 2 MHz insonation frequency. The proposed method yielded a flow estimation bias of 0 mL/min, standard deviation of error (SDE) of 22 mL/min, and a root-mean-square error (RMSE) of 22 mL/min over a 150 mL/min range of mean flows. Recordings were also obtained in low signal-to-noise ratio (SNR) conditions using a skull mimicking element, resulting in an estimation bias of -13 mL/min, SDE of 23 mL/min, and an RMSE of 26 mL/min. The effect of insonation frequency was also investigated by obtaining recordings at 4.3 MHz, yielding an estimation bias of -16 mL/min, SDE of 16 mL/min, and an RMSE of 22 mL/min. The results indicate that our technique can lead to clinically acceptable flow measurements across a range of vessel diameters in high and low SNR regimes.

Joint Impact of Channel Estimation Errors and Pointing Errors on FSO Communication Systems Over $\mathcal {F}$ Turbulence Channel

In this paper, we provide the performance analysis of free-space optical (FSO) communication systems over Fisher-Snedecor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {F}$</tex-math></inline-formula> fading channel with estimation errors in the presence of pointing errors. The probability density function (PDF) and cumulative distribution function (CDF) of signal-to-noise ratio (SNR) are derived in the presence of channel estimation errors and pointing errors. Subsequently, the novel expressions for the outage probability, average bit error rate (BER) and ergodic capacity are obtained. The asymptotic expressions for the outage probability and average BER are also presented to provide further insights into the FSO system performance in high SNR regime. Through numerical simulations, the impact of imprecise channel on the performance of the FSO communication systems is analyzed as compared with precise channel. Finally, Monte Carlo simulations are presented to validate the proposed analysis.