Low-resolution Analog-to-digital Converters Research Articles

User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access.

This paper proposes a heuristic association algorithm between access points (APs) and user equipment (UE) in user-centric cell-free massive multiple-input-multiple-output (MIMO) systems, specifically targeting scenarios where UEs share the same frequency and time resources. The proposed algorithm prevents overserving APs and ensures the connectivity of all UEs, even when the number of UEs is significantly greater than the number of APs. Additionally, we assume the use of low-resolution analog-to-digital converters (ADCs) to reduce fronthaul capacity. While realistic massive access scenarios, such as those in Internet-of-Things (IoT) environments, often involve hundreds or thousands of UEs per AP using multiple access techniques to allocate different frequency and time resources, our study focuses on scenarios where UEs within each AP cluster share the same frequency and time resources to highlight the impact of pilot contamination in dense network environments. The proposed algorithm is validated through simulations, confirming that it guarantees the connection of all UEs and prevents overserving APs. Furthermore, we analyze the required fronthaul capacity based on quantization bits and confirm that the proposed algorithm outperforms existing algorithms in terms of SE and average SE performance for UEs.

Research on antenna design based on low-resolution digital-to-analog converters

In the ever-evolving field of communication, the optimization of antenna design and structure has garnered significant attention from professionals. Effective antenna design plays a crucial role in ensuring reliable and efficient communication systems. However, the challenge lies in overcoming the limitations imposed by current low-resolution digital-to-analog converters (DACs) on the attainment of highly efficient antenna structures. This study aims to address this issue by exploring the potential of low-resolution DACs for improving the structural design of antennas. To achieve this, we propose gradient-based and low-complexity heuristic solutions that leverage global optimization techniques. By optimizing the antenna structure using these innovative approaches, we aim to enhance the overall performance and efficiency of communication systems. The significance of this research lies in its potential to revolutionize antenna design by overcoming the constraints of low-resolution DACs. By incorporating Sigma-Delta modulation in the filter design, we anticipate significant improvements in performance when the number of transmitting antennas is comparable to the number of users. Furthermore, our investigation reveals that the application of Compressed Sensing techniques yields results that closely align with the optimal solution in scenarios where the number of transmitting antennas greatly exceeds the number of users.

Power Optimization of Cell-Free Massive MIMO With Full-Duplex and Low-Resolution ADCs

This paper analyzes the spectral/energy efficiency (SE/EE) of a full-duplex (FD) cell-free (CF) massive multiple-input multiple-output (mMIMO) over Rician fading channels. Due to the FD radios, the access points (APs) suffer from self-interference (SI) and inter-AP interference (IAI) while the downlink (DL) users’ signals are corrupted by the uplink (UL) users’ transmissions. We consider the case, where low-resolution analog-to-digital converters (ADCs) are utilized at the APs and DL users, which introduces the quantization noise (QN). The combined effects of Rician κ-factor, residual SI/IAI, UL-to-DL interference, multi-user interference, and QN on the UL/DL SEs are characterized. The UL SE is degraded by the increase in DL power, whereas the growth in UL power deteriorates the DL SE. The effects of the residual SI/IAI and UL-to-DL interference are worsened by the low-resolution ADCs. We optimize the UL/DL transmit powers to maximize the overall sum SE of the network. It is observed that the proposed power allocation algorithm brings substantial sum SE gain. A trade-off analysis between the EE and SE, as a function of the ADCs’ resolution, shows that the entire envelope of the operating region of FD CF mMIMO is enhanced in Rician channels.

Performance Analysis of Cell-Free Massive MIMO Network With Large-Scale-Fading Decoding and Low-Resolution ADCs

Concentrating on cell-free massive multiple-input multiple-output (MIMO) networks with low-resolution analog-to-digital converters (ADCs), we derive the theoretical achievable uplink spectral efficiency (SE) for the minimum mean-squared error (MMSE)-based large-scale fading decoding (LSFD). Moreover, we investigate the bit allocation (BA) among access points (APs) to maximize the asymptotic sum SE under the constraint of total ADC quantization bits. Simulation results confirm that our theoretical analysis is accurate and validate that the proposed BA technique is preferable to the random and fixed counterparts.

One-Bit Downlink Precoding for Massive MIMO OFDM System

Massive multiple-input multiple-output (MIMO) is a key technology in next generation wireless communication. However, the increasing number of radio frequency (RF) chains results in higher cost and power consumption. Given that hundreds or even thousands of transmit antennas are equipped at the base station (BS), low resolution digital-to-analog converters (DACs) are preferred to reduce the power consumption on both DACs and power amplifiers (PAs). Currently, there have been some studies about the application of low-resolution DACs for single-carrier systems. For multi-carrier systems, this problem hasn’t been fully investigated. This paper aims to design a 1-bit downlink precoding algorithm for massive multi-user MIMO orthogonal frequency division multiplexing (OFDM) systems. A nonlinear precoding algorithm is proposed, which can address the non-convex optimization problem with discrete output constraint and guarantee convergence. Meanwhile, the proposed algorithm factors in the different path-losses experienced by different users. Furthermore, some approximation schemes can be applied to bring down the computational complexity of the proposed algorithm further. Simulation results illustrate that our algorithm performs the best among other nonlinear precoding methods in OFDM systems.

New look at secure performance of massive MIMO with low-resolution DACs

Since the requirement of secure transmission for massive multiple-input–multiple-output (mIMO) is high, we consider a secure massive MIMO system in presence of multiple digital to analog converters (DACs). We deal with complicated performance analysis of the proposed system including a base station, a legitimate user, and an eavesdropper while these nodes are equipped with multiple antennas. In particular, we intend to present the closed-form expression for the ergodic secrecy rate of the system and also calculate the optimal power allocation for better performance gain. The simulation analysis shows that quantization bits, transmit signal to noise ratio at the base station, and power allocation plays a major role in enhancing the performance of the system.

Pulse-Based ISAC: Data Recovery and Ranging Estimation for Multi-Path Fading Channels

Pulse-based integrated sensing and communication (ISAC) systems have the advantages of high ranging resolution and strong resistance to self-interference, compared with continuous wave (CW) based systems. However, for pulse-based ISAC systems, multi-path channels pose various challenges to data recovery and ranging by providing diversity gain for data recovery but incurring the interference to the identification of the first path in ranging. In this paper, we design a pulse-based ISAC receiver for multi-path channels. The designed receiver can obtain the diversity gain by correlating the received signal with the estimated template signal. Meanwhile, it can detect the arrival of the first path by using a threshold detection method based on a constant false alarm rate (CFAR). Furthermore, we extend the pulse-based ISAC design to a low-resolution analog-to-digital converter (ADC) scenario. A low-cost receiver design is provided for the pulse-based ISAC system that can recover data and estimate range simultaneously considering the non-linear effect caused by the low-resolution ADC. Simulation results show that compared with the generalized maximum likelihood (GML) based receiver, the proposed full-resolution pulse-based ISAC receiver has 1dB signal-to-noise ratio (SNR) loss in bit error rate (BER) and almost the same mean squared error (MSE) performance with the significantly reduced computational complexity. Also, compared with the full-resolution ISAC receiver, the ISAC receiver with 3-level quantization incurs only 0.8dB SNR loss in BER and 1dB SNR loss in MSE.

Bayesian Receiver Design via Bilinear Inference for Cell-Free Massive MIMO With Low-Resolution ADCs

We propose a novel joint channel and data estimation (JCDE) scheme to combat the rate limitation in fronthaul links of cell-free massive MIMO (CF-mMIMO) systems introduced by the use of analog-to-digital converters (ADCs) at access points (APs), which makes channel estimation and multi-user detection at the central AP (CAP) challenging. The latter problem is solved here via the new JCDE scheme which differs from state-of-the-art (SotA) alternatives due to two contributions. The first is the design and incorporation of de-quantization (DQ) step which relies only on scalar Gaussian approximation (SGA) assumptions in conformity with mild central limit theorem (CLT), in contrast to the much harder asymptotic conditions required by the classic bilinear generalized approximate message passing (BiGAMP) algorithm. The second is a modification of bilinear Gaussian belief propagation (BiGaBP), whereby quantized outputs are linearized via the Bussgang decomposition enabling tractable signal processing. The resulting DQ-aided JCDE method achieves both low-complexity and high-accuracy by exploiting both the spatial degrees of freedom (DoF) obtained from, and the observations at the CAP to compensate for the low-resolution distortion introduced by, the distributed APs. The efficacy of the proposed method over the SotA is confirmed via computer simulations.

Ergodic Rate and Outage Performance of Full-Duplex NOMA Relaying with Channel Estimation Errors and Low-Resolution ADCs

In this paper, we analyze the performance of a full-duplex (FD) cooperative non-orthogonal multiple access (C-NOMA) relaying system with an amplify-and-forward (AF) protocol in the presence of loopback interference in FD transceivers. Particularly, by considering channel estimation errors and quantization noise in low-resolution analog-to-digital converters (ADCs), the accurate approximation expression for the ergodic rate and closed-form solution for the outage probability are derived, respectively. The validity of the theoretical results is verified by Monte Carlo simulations, which show that both channel estimation errors and quantization noise have deleterious effects on ergodic rate and outage performance for moderate and high signal-to-noise ratios (SNR). In the second phase of the C-NOMA system, both the outage performance and ergodic sum rate decrease at high SNRs due to the effects of loop interference. When the ADC dynamic range reaches a certain level, the system performance is more affected by loopback interference and channel estimation errors compared to the quantization noise of the ADCs.

Performance Analysis and Optimization of Network-Assisted Full-Duplex Systems Under Low-Resolution ADCs

Network-assisted full-duplex (NAFD) distributed massive multiple-input–multiple-output (M-MIMO) enables the in-band full-duplex with existing half-duplex devices at the network level, which exceptionally improves spectral efficiency. This article analyzes the impact of low-resolution analog-to-digital converters (ADCs) on NAFD distributed M-MIMO and designs an efficient bit allocation algorithm for low-resolution ADCs. The beamforming training mechanism relieves the heavy pilot overhead for channel estimation, which remarkably enhances the system performance by guiding the interference cancellation and coherence detection. Furthermore, closed-form expressions for spectral and energy efficiency with low-resolution ADCs are derived. The multiobjective optimization problem for spectral and energy efficiency is solved by the deep Q network and the nondominated sorting genetic algorithm II. The simulation results corroborate the theoretical derivation and verify the effectiveness of introducing low-resolution ADCs in NAFD distributed M-MIMO systems. Meanwhile, a set of Pareto-optimal solutions for ADC accuracy flexibly provide guidelines for deploying in a practical NAFD distributed M-MIMO system.

Downlink NOMA for Short-Packet Internet of Things Communications With Low-Resolution ADCs

In this article, we propose a precoding design to maximize the sum achievable rate in downlink nonorthogonal multiple access (NOMA)-aided short-packet Internet of Things (IoT) communications, wherein each IoT device is equipped with low-resolution analog-to-digital converters (ADCs). Due to intertwined effects caused from low-resolution ADCs, short packets, and NOMA, it is challenging to find efficient precoding vectors. To resolve the difficulties, we first linearize quantization distortion by adopting an additive quantization noise model. Thereafter, we approximate nonsmooth functions by using a LogSumExp technique. With the transformed problem, we derive a first-order optimality condition and propose a novel precoding algorithm which identifies an efficient local optimal solution with low complexity. Based on the proposed algorithm, we also investigate an efficient NOMA decoding ordering method for the considered system. Via simulations, we demonstrate that the proposed method outperforms other baseline methods.

Bayesian Joint Channel-and-Data Estimation for Quantized OFDM Over Doubly Selective Channels

This paper investigates the design of orthogonal frequency division multiplexing (OFDM) receiver, which operates over time-frequency doubly selective (DS) channels and uses a low-resolution analog-to-digital converter (ADC) to quantize the received signal. The time-varying multipaths in DS channel and the nonlinear quantization distortion caused by low-resolution ADC, destroy the orthogonality among OFDM subcarriers, incurring severe inter-carrier interference (ICI). Therefore, joint channel-and-data estimation is preferred, since in this way one can effectively tackle the ICI and make the best of the quantized signal in each OFDM symbol. In this paper, we elaborately develop the joint estimation scheme by casting the problem into the Bayesian inference framework. The proposed scheme not only takes into account of the nonlinear quantization in a probabilistic manner, but also exploits the channel sparsity enabled by the basis expansion model (BEM) approximation to DS channels. Furthermore, to benchmark the proposed scheme, we derive the asymptotic theoretical bounds in the large-system regime. Simulation results demonstrate that the proposed scheme can achieve the performance close to the theoretical bounds, thereby verifying the effectiveness of the proposed scheme.

Channel Estimation for RIS-Aided mmWave Massive MIMO System Using Few-Bit ADCs

Millimeter wave (mmWave) massive multiple-input multiple-output (massive MIMO) is one of the most promising technologies for the fifth generation and beyond wireless communication system. However, a large number of antennas incur high power consumption and hardware costs, and high-frequency communications place a heavy burden on the analog-to-digital converters (ADCs) at the base station (BS). Furthermore, it is too costly to equipping each antenna with a high-precision ADC in a large antenna array system. It is promising to adopt low-resolution ADCs to address this problem. In this letter, we investigate the cascaded channel estimation for a mmWave massive MIMO system aided by a reconfigurable intelligent surface (RIS) with the BS equipped with few-bit ADCs. Due to the low-rank property of the cascaded channel, the estimation of the cascaded channel can be formulated as a low-rank matrix completion problem. We introduce the Bayesian optimal inference framework to tackle with the information loss caused by quantization. To implement the estimator and achieve the matrix completion, we use efficient bilinear generalized approximate message passing (BiG-AMP) algorithm. Extensive simulation results verify that our proposed method can accurately estimate the cascaded channel for the RIS-aided mmWave massive MIMO system with low-resolution ADCs.

Massive MIMO With Group SIC Receivers and Low-Resolution ADCs Over Rician Fading Channels

This paper investigates the performance of uplink massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) operating over Rician fading channels. We study zero-forcing (ZF) group successive interference cancellation (GSIC) receivers, which cover the ZF and ZF-SIC receivers as two special cases. The spectrum efficiency (SE) is analyzed for general power allocation under both perfect and imperfect channel state information (CSI). We further derive the optimal power allocation for a given quality-of-service (QoS) for the GSIC receivers. Based on random matrix theory, the corresponding asymptotic approximations of the signal-to-interference-plus-noise-ratio (SINR) for the system and the power allocation are derived for both perfect and imperfect CSI. The derived approximations are verified and shown highly accurate, providing fast characterization of the performance and near-optimal resource allocation for different system parameters. The numerical and approximation results both indicate that power allocation improves the overall energy efficiency (EE), while the GSIC receiver can yield a high EE with a very small number of groups. Besides, the EE of the system can be improved with intermediate-resolution ADCs and ordered SIC (OSIC).

Massive MIMO Systems with Low-Resolution ADCs: Achievable Rates and Allocation of Quantization Bits

In massive multiple-input multiple-output (MIMO) systems, the large number of high-resolution analog-to-digital converters (ADCs) lead to high hardware cost and power consumption. In this work, the uplink achievable rates of massive MIMO systems with low-resolution ADCs are studied with consideration of both “Uniform-ADC” that uses ADCs with the same number of quantization bits and “Mixed-ADC” that allows the use of ADCs with different resolutions. By leveraging an additive quantization noise model (AQNM), the asymptotic achievable rates are obtained for maximum ratio combining (MRC), zero-forcing (ZF), and linear minimum mean squared error (LMMSE) receivers in very simple forms. Taking advantages of the theoretical results, we propose two criteria for allocation of quantization bits. It is found that the optimal quantization bits allocation for LMMSE is Mixed-ADCs with number of quantization bits that are polarized, while Uniform-ADC is optimal for MRC and ZF. When there is a constraint on the total ADC power consumption, the proposed quantization-bit allocation scheme for LMMSE becomes Uniform-ADC when the transmit signal-to-noise ratio (SNR) is below a threshold, which is related to the system scale and the ADC power consumption. The theoretical results are verified by Monte-Carlo simulations.

ADMM Based Symbol-Level Precoding for MU-MISO Downlink With Low-Resolution DACs

In this letter, we investigate the problem of symbol-level precoder design with low-resolution digital-to-analog converters (DACs) to implement wireless multiuser multiple-input single-output (MISO) downlink communications. Specifically, we aim to maximize the minimum distance between the noise-free received signal and its decision boundaries via a min-max fairness design. Due to the nondifferentiable objective and finite-alphabet constraint, the problem is NP-hard and difficult to tackle. To handle this problem, we first reduce the dimension of the variables by solving a relaxed problem. Then, the problem is transformed to a smoothed form which can be efficiently solved using the alternative direction method of multipliers (ADMM)-based framework. In addition, we propose a closed-from solution based on majorization-minimization method to provide a low-complexity near-optimal solution. We further provide the convergence and computational complexity analyses, and validate the performance by simulations.

A Fast Quantized Precoding Algorithm for Massive MU-MISO Systems With Low-Resolution DACs and PSK Signaling: A Safety Margin Fitting Approach

In this correspondence, we propose a new symbol-level quantized precoder design for massive multi-user multiple-input-single-output (MU-MIMO) systems employing low-resolution digital-to-analog converters (DACs) and phase-shift-keying (PSK) signaling. A new problem formulation based on the idea of safety margin fitting is proposed, from which a low-complexity precoding algorithm is derived. Both the complexity analysis and simulation results indicate that the proposed precoder has very low computational complexity while still performing favorably. Because symbol-level precoders perform precoding on a symbol-by-symbol basis, they need to be of low complexity in their real world use. Our proposed precoder therefore stands out as a strong candidate for applications among existing quantized precoders.

Performance analysis of massive MIMO systems with low-resolution ADCs and IQI

In massive multiple-input multiple-output (MIMO) systems, the substantial increase in the number of antennas leads to a surge in hardware cost and power consumption. Meanwhile, the impact of IQ imbalance (IQI) on system performance also tends to be serious. In this paper, closed-form expressions for the achievable rates of maximum-ratio combining (MRC) receivers are derived for uplink massive MIMO systems with both low-resolution analog-to-digital converters (ADCs) and IQI. Based on the derived closed-form expression, the influence of system parameters on the achievable rate is analyzed. The simulation results verify the accuracy of the theoretical results. It is found that low-resolution ADC and IQI will degrade the achievable rate compared with employing high resolution ADCs, but this loss can be compensated for by increasing the number of base station (BS) antennas, so as to significantly increase the energy efficiency.

Spectral efficiency for IRS-assisted uplink mmWave massive MISO systems with low-resolution ADCs

In this paper, we investigate the uplink achievable spectral efficiency (SE) for an intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input single-output (MISO) system, where all antennas at the BS are equipped with cheaper low-resolution analog-to-digital converters (ADCs) and an IRS adjusts its reflecting phase shifts to facilitate information transfer. To maximize the uplink achievable SE, we design the phase shift of each reflecting element and obtained the optimal phase shift matrix in the terms of the statistical channel state information (CSI). An theoretical expression of the uplink achievable SE of massive MISO system is obtained with the zero forcing (ZF) detector is derived. Based on this derived theoretical result, the behaviors of the achievable SE with respect to several physical parameters are revealed that includes the number of antennas, the transmit power, the number of reflecting elements, and the quantization bits of the low-resolution ADCs. Finally, we provide the numerical results by Monte Carlo simulation to verify that our theoretical analysis is accurate. Results show that the achievable SE increases with the number of antennas, the number of quantization bits of the ADC and the reflecting elements of the IRS, but tends to a saturation rate. In addition, we find that the SE inevitably suffers a certain loss due to the phase shift noise is unavoidable and the quantization accuracy of IRS, which can be compensated by increasing the transmit power and the number of reflecting elements.

Multi-band DFT-S 100 Gb/s 32 QAM-DMT transmission in intra-DCI using 10 G-class EML and low-resolution DAC.

In this paper, 100 Gb/s/λ 32 quadrature amplitude modulation discrete multi-tone (QAM-DMT) transmission using 10 G-class electro-absorption modulated laser (EML) and 4/5-bit digital-to-analog converters (DACs) are experimentally demonstrated to meet the requirement of intra-datacenter interconnection (intra-DCI). Unequal length multi-band (ULM) discrete Fourier transform spread (DFT-S) precoding is investigated to alleviate the distortion induced by the high peak-to-average power ratio (PAPR) of DMT. The results show that the required computational complexity of ULM DFT-S precoding with 2-bands (k1=256, k2=64) decreases sharply compared to the traditional DFT-S technique with only about 0.5 dB receiver sensitivity penalty. In addition, compared to the equal length multi-band (ELM) DFT-S precoding, the ULM DFT-S precoding can bring about 2.5 dB receiver sensitivity improvement with slight added computational complexity. With the assistance of ULM DFT-S precoding and noise shaping (NS) technique, the bit-error ratio (BER) of 100 Gb/s 32 QAM-DMT signal generated by 5-bit DAC over 2-km single-mode fiber (SMF) transmission can reach the hard-decision forward error correction (HD-FEC) threshold with received optical power (ROP) of -6.5 dBm, with only additional 39.9% multiplier and 33.7% adder.