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.     

Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications

We have studied massive MIMO hybrid beamforming (HBF) for millimeter-wave (mmWave) communications, where the transceivers only have a few radio frequency chain (RFC) numbers compared to the number of antenna elements. We propose a hybrid beamforming design to improve the system’s spectral, hardware, and computational efficiencies, where finding the precoding and combining matrices are formulated as optimization problems with practical constraints. The series of analog phase shifters creates a unit modulus constraint, making this problem non-convex and subsequently incurring unaffordable computational complexity. Advanced deep reinforcement learning techniques effectively handle non-convex problems in many domains; therefore, we have transformed this non-convex hybrid beamforming optimization problem using a reinforcement learning framework. These frameworks are solved using advanced deep reinforcement learning techniques implemented with experience replay schemes to maximize the spectral and learning efficiencies in highly uncertain wireless environments. We developed a twin-delayed deep deterministic (TD3) policy gradient-based hybrid beamforming scheme to overcome Q-learning’s substantial overestimation. We assumed a complete channel state information (CSI) to design our beamformers and then challenged this assumption by proposing a deep reinforcement learning-based channel estimation method. We reduced hybrid beamforming complexity using soft target double deep Q-learning to exploit mmWave channel sparsity. This method allowed us to construct the analog precoder by selecting channel dominant paths. We have demonstrated that the proposed approaches improve the system’s spectral and learning efficiencies compared to prior studies. We have also demonstrated that deep reinforcement learning is a versatile technique that can unleash the power of massive MIMO hybrid beamforming in mmWave systems for next-generation wireless communication.     

All-optical analog-to-digital conversion system with a spatial coding method using designed filter

We propose and demonstrate an all-optical analog-to-digital conversion system with a spatial coding method using a designed filter. To make available codes more flexible, the design technique for a computer-generated hologram is introduced to our spatial coding scheme. By designing the spatial coding filter appropriately, this coding scheme enables us to output various digital signals as spatial patterns. By combining this coding method with the optical quantization method using soliton self-frequency shifting in a fiber, the proposed system converts analog sampled signals into arbitrary digital signals. Preliminary experimental results show that 3-bit Gray codes are successfully output as spatial patterns using the designed spatial coding filter.     

Deep learning-driven MIMO: Data encoding and processing mechanism

Large-scale Multiple-Input Multiple Output (MIMO) is the key technology of 5G communication. However, dealing with physical channels is a complex process. Machine learning techniques have not been utilized commercially because of the limited learning capabilities of traditional machine learning algorithms. We design a deep learning hybrid precoding scheme based on the attention mechanism. The method mainly includes channel modeling and deep learning encoding two modules. The channel modeling module mainly describes the problem formally, which is convenient for the subsequent method design and processing. The model design module introduces the design framework, details, and main training process of the model. We utilize the attention layer to extract the eigenvalues of the interference between multiple users through the output attention distribution matrix. Then, according to the characteristics of inter-user interference, the loss minimization function is used to study the optimal precoder to achieve the maximum reachable rate of the system. Under the same condition, we compare our proposed method with the traditional unsupervised learning-based hybrid precoding algorithm, the TTD-based (True-Time-Delay, TTD) phase correction hybrid precoding algorithm, and the deep learning-based method. Additionally, we verify the role of attention mechanism in the model. Extensive simulation results demonstrate the effectiveness of the proposed method. The results of this research prove that deep learning technology can play a driving role in the encoding and processing of MIMO with its unique feature extraction and modeling capabilities. In addition, this research also provides a good reference for the application of deep learning in MIMO data processing problems.     

Multichannel time-reversal processing for acoustic communications in a highly reverberant environment

The development of time-reversal (T/R) communication systems is a recent signal processing research area dominated by applying T/R techniques to communicate in hostile environments. The fundamental concept is based on time-reversing the impulse response or Green's function characterizing the uncertain communications channel to mitigate deleterious dispersion and multipath effects. In this paper, we extend point-to-point to array-to-point communications by first establishing the basic theory to define and solve the underlying multichannel communications problem and then developing various realizations of the resulting T/R receivers. We show that not only do these receivers perform well in a hostile environment, but they also can be implemented with a "1 bit" analog-to-digital converter design structure. We validate these results by performing proof-of-principle acoustic communications experiments in air. It is shown that the resulting T/R receivers are capable of extracting the transmitted coded sequence from noisy microphone array measurements with zero-bit error.     

Photonics-assisted compressive sampling system for wideband spectrum sensing

Compressive sampling(CS) has attracted considerable attention in microwave and radio frequency(RF) fields in recent years. It enables the acquisition of high-frequency signals at a rate much smaller than their Nyquist rates.Combined with photonics technology, traditional CS systems can significantly enlarge their operating bandwidth, which offers great potential for spectrum sensing in cognitive radios. In this Letter, we review our recent work on photonic CS systems for wideband spectrum sensing. First, a proof-of-concept photonics-assisted CS system is demonstrated; it is capable of acquiring numerous radar pulses in an instantaneous bandwidth spanning from 500 MHz to 5 GHz with a 500-MHz analog-to-digital converter(ADC). To further reduce the acquisition bandwidth, multi-channel photonics-assisted CS systems are proposed for the first time, enabling the acquisition of multi-tone signals with frequencies up to 5 GHz by using 120-MHz ADCs. In addition, the system bandwidth is increased from 5 to 20 GHz by employing time-interleaved optical sampling.     

Successive approximation-like 4-bit full-optical analog-to-digital converter based on Kerr-like nonlinear photonic crystal ring resonators

Implementing of photonic sampling and quantizing analog-to-digital converters (ADCs) enable us to extract a single binary word from optical signals without need for extra electronic assisting parts. This would enormously increase the sampling and quantizing time as well as decreasing the consumed power. To this end, based on the concept of successive approximation method, a 4-bit full-optical ADC that operates using the intensity-dependent Kerr-like nonlinearity in a two dimensional photonic crystal (2DPhC) platform is proposed. The Silicon (Si) nanocrystal is chosen because of the suitable nonlinear material characteristic. An optical limiter is used for the clamping and quantization of each successive levels that represent the ADC bits. In the proposal, an energy efficient optical ADC circuit is implemented by controlling the system parameters such as ring-to-waveguide coupling coefficients, the ring’s nonlinear refractive index, and the ring’s length. The performance of the ADC structure is verified by the simulation using finite difference time domain (FDTD) method.     

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.     

The trousers problem revisited

Anderson and DeWitt considered the quantization of a massless scalar field in a spacetime whose spacelike hypersurfaces change topology and concluded that the topology change gives rise to infinite particle and energy production. We show here that their calculations are insufficient and that their propagation rule is unphysical. However, our results using a more general propagation rule support their conclusion.     

Experimental study of the directional characteristics of a vector-scalar array

We analyze the spatial spectra of a vector-scalar array when the signals are processed using methods with different resolution. The method of presenting the signals used in the research allowed us to apply the previously developed method of detecting and estimating the parameters of signal sources obtained in the calculation of the theoretical characteristics of detecting the signal sources and realization of the algorithms of signal processing. We compared the resolution of vector-scalar and scalar arrays with the same aperture. The experiments were carried out in stationary conditions and in the towing regime. During the towing regime, the accuracy of determination of the source location was controlled using the GPS receivers. It was shown experimentally that the signal to noise ratio at the output of the receiving array is three times greater if the vector-scalar array is used rather than the scalar one. The level of the lateral background appeared to be three times smaller during operation with the vector-scalar array than with the scalar one even in the towing regime. The results of measurements are confirmed by theoretical calculations.     

Spectral and energy efficiency for uplink massive MIMO systems with mixed-ADC architecture

In this paper, we introduce a mixed- analog-to-digital converter (ADC) architecture for massive multiple-input multiple-output (MIMO) systems and study the system’s performance mainly includes the achievable spectral efficiency and energy efficiency. In principle, the mixed-ADC architecture permits the one part of antennas at the base station (BS) are connected to speed and expensive full-resolution ADCs and the remaining part of the antennas are connected to the cheap low-resolution ADCs. By applying the general maximum-ratio combining detector, a tractable approximate expression for the achievable SE is obtained. Leveraging on the derived results, the effects of the number of BS antennas and the percent of the full-resolution ADCs on the achievable SE are investigated. Results show that the achievable SE increases with the percent of the full-resolution ADCs and the number of BS antennas. Based on a realistic power consumption model, we evaluate the energy efficiency for the considered mixed-ADC architecture. Moreover, under the certain achievable SE constraint, we maximize the energy efficiency by adjusting the number of low-resolution ADCs and the resolution bits of the corresponding ADC device. Numerical results showcase that the energy efficiency can be improved by enhancing the average transmitted power, and there exists an optimal number of resolution bits and the number of antennas to maximize the energy efficiency, which indicates that the application of mixed-ADC architecture has a great potential in future mobile communication system.     

机械抖动激光陀螺的高分辨率信号处理

 机械抖动激光陀螺（MDRLG）信号是两路相位差为π/2的正弦拍频信号,一般采用4倍频鉴相输出计数脉冲,利用FIR滤波和抖动剥除等方法解调抖动信号,从而得到外界输入的角速度信息。高速采集MDRLG信号,并细分为相位依次相差π/16的16路信号,实现MDRLG的8倍频、16倍频和32倍频鉴相输出。输出角速率的均方差随着倍频数的提高而减小,信号处理的分辨率得到提高。实验测试和Allan方差分析表明：32倍频时的量化误差Q从4倍频鉴相时的0.327″减小到0.170″,增加MDRLG信号鉴相的倍频数可以减小量化误差。在对MDRLG信号进行抖动剥除解调时,由于量化误差是主要误差源之一,高分辨率信号处理能够有效提高MDRLG角速度测量的精度。     

迭代法MIMO声纳目标检测——一种凸显弱目标的方法

通常MIMO声纳接收机收到的信号会直接送入匹配滤波器组,然后测量信号到达时间。当遇到强弱两个目标散射信号混叠的情况时,弱目标的输出峰值可能被淹没在强目标的旁瓣内而无法分辨。为提高MIMO声纳对弱目标的分辨能力,本文提出一种迭代信号分析方法,可以由强到弱依次对目标进行估计。通过数值仿真分析了本方法的性能。结果表明:采用迭代分析方法可以有效提高系统对弱目标的分辨能力。     

Photonic analog-to-digital converter using soliton self-frequency shift and interleaving spectral filters

We propose a novel ultrafast photonic analog-to-digital converter that uses the soliton self-frequency shift in an optical fiber as an optical power-to-frequency conversion mechanism and a set of interleaving spectral filters as the optical comparators. Our method does all the signal processing in the optical domain and requires binary receivers in only the electronic domain. In contrast to the usual exponential scaling, the simultaneous binary search architecture that we propose results in a flash analog-to-digital converter with remarkable linear scaling between the number of comparators and the number of bits resolved.     

Heterodyne direct-detection OOFDM system with low sampling rate

A method of multi-channel receiving for high bit rate heterodyne direct-detection optical orthogonal frequency-division-multiplexing （OOFDM） system is proposed to reduce the sampling rate demand of analog-to-digital converter （ADC）. The sampling rate of ADCs can be reduced to 1/N that of the original signal bandwidth in an N-channel receiving system. Aided by a succeeding digital signal processing （DSP） at the receiver, aliasing free signal can be recovered. A back-to-back experimental result is given for a 4-channel system, based on which, a down conversion process for heterodyne can be reduced. The signal rebuilding algorithm is given and analyzed in its complexity and noise tolerance.     

Multi-level quantization and blind equalization based direct transmission method of digital baseband signal

The direct transmission of digital baseband signals has practical significance in the field of Ethernet terminal connection, high-speed digital communication, data transmission of various types of information peripherals. The signal amplitude gradually decays while the transmission distance increases. Also the attenuation is proportional to the signal frequency, resulting in signal distortion and receiving error. It is a common method for digital baseband signal transmission to use pre-emphasis chip and equalizer chip to improve the transmission quality with a wide range of mature applications. This paper describes a new type of digital signal transmission method, as the receiver using analog-to-digital converter, instead of equalizer chip, to achieve the multi-level quantization of receiving time-domain data waveform. The waveform of the transmitted digital high and low level signal is sampled into multi-bit values. Then, the paper realizes adaptive frequency domain equalization based on soft threshold and makes use of multi-level quantization soft information for error correction. Error correcting code is mainly used to correct the error caused by the channel bandwidth limit, external noise or interference in the process of data transmission, so as to improve the stability and reliability of the transmission. The paper uses the two-stage error correcting codec system based on both Turbo and BCH coding, to achieve the high performance of Turbo code, and good characters of respond time and complexity. The transmitter outputs 12.5 MHz pseudo-random sequence through a 199.93 meter unshielded balanced twisted pair transmission medium. And the receiver circuit using a 62.5MSPS analog-to-digital converter over-samples the waveform to 8-level quantity. The output error of a 65536 bit pseudo-random sequence is less than 8 bits, and the error correction can be further improved by 8b-10b codec. Compared with the traditional pre-emphasis and balanced interface ICs connection, the method described in this article has the advantages of longer transmission distance, better flexibility and wider scope of use.     

Digital detection and processing of multiple quadrature harmonics for EPR spectroscopy

A quadrature digital receiver and associated signal estimation procedure are reported for L-band electron paramagnetic resonance (EPR) spectroscopy. The approach provides simultaneous acquisition and joint processing of multiple harmonics in both in-phase and out-of-phase channels. The digital receiver, based on a high-speed dual-channel analog-to-digital converter, allows direct digital down-conversion with heterodyne processing using digital capture of the microwave reference signal. Thus, the receiver avoids noise and nonlinearity associated with analog mixers. Also, the architecture allows for low-Q anti-alias filtering and does not require the sampling frequency to be time-locked to the microwave reference. A noise model applicable for arbitrary contributions of oscillator phase noise is presented, and a corresponding maximum-likelihood estimator of unknown parameters is also reported. The signal processing is applicable for Lorentzian lineshape under nonsaturating conditions. The estimation is carried out using a convergent iterative algorithm capable of jointly processing the in-phase and out-of-phase data in the presence of phase noise and unknown microwave phase. Cramér-Rao bound analysis and simulation results demonstrate a significant reduction in linewidth estimation error using quadrature detection, for both low and high values of phase noise. EPR spectroscopic data are also reported for illustration.     

Nonlinear dynamic filtering of logarithmically amplified fringe signals in optical coherence tomography applied to paper measurements

Application of the nonlinear Kalman filtering method to logarithmically amplified low-coherence fringe signals measured from paper samples is considered. Experimental results of dynamic fringe envelope recovery in optical coherence tomography (OCT) systems are presented. The analog fringe envelope and digital dynamic fringe envelope recoveries from noisy signals are compared. The results show that the nonlinear discrete Kalman filtering method can be applied to estimate envelopes of logarithmically transformed low-coherence fringe signals with high noise immunity. Logarithmic amplification reduces quantization error in dealing with small signal values. The experimental results obtained demonstrate the possibility of purely digital signal processing in OCT. The text was submitted by the authors in English.     

Proposal for photonic quantization with differential encoding using a phase modulator and delay-line interferometers

A photonic quantization approach to implementing analog-to-digital conversion (ADC) in the optical domain with differential encoding employing a phase modulator and delay-line interferometers (DLIs) is proposed and demonstrated. In the proposed ADC system, the phase-modulated signal is sent to an array of DLIs that have identical time delay difference, but different phase shifts, which are employed to achieve quantization with differential encoding. A proof-of-concept experiment is performed. The quantization of a 10 GHz sinusoidal signal with a bit length of 4 is experimentally demonstrated.     

Data detection based on matrix decomposition for massive MIMO systems in realistic channel scenarios

Massive multiple-input multiple-output (MIMO) is a key technology for modern wireless communication systems. In massive MIMO receivers, data detection is a computationally expensive task. In this paper, we explore the performance and the computational complexity of matrix decomposition based detectors in realistic channel scenarios for different massive MIMO configurations. In addition, data detectors based on decomposition algorithms are compared to the approximate-inversion detection (AID) methods. It is shown that the alternating-direction-method-of-multipliers-based-Infinity-Norm (ADMIN) detection is promising in realistic channel environment and the performance is stable even when the ratio of the base-station (BS) antenna elements to the number of users is small. In addition, this paper studies the performance of several detectors in imperfect channel state information (CSI) and correlated channels. Our work provides valuable insights for massive MIMO systems and very large-scale integration (VLSI) designers to select the appropriate massive MIMO detector based on their specifications.