arXiv subjects

Zhaocheng Wang

Publications and source records attributed to Zhaocheng Wang.

At least 73 records · Page 4Linked to original sources

arXiv2021

Terahertz Wireless Communications with Flexible Index Modulation Aided Pilot Design

Terahertz (THz) wireless communication is envisioned as a promising technology, which is capable of providing ultra-high-rate transmission up to Terabit per second. However, some hardware imperfections, which are generally neglected in the existing literature concerning lower data rates and traditional operating frequencies, cannot be overlooked in the THz systems. Hardware imperfections usually consist of phase noise, in-phase/quadrature imbalance, and nonlinearity of power amplifier. Due to the time-variant characteristic of phase noise, frequent pilot insertion is required, leading to decreased spectral efficiency. In this paper, to address this issue, a novel pilot design strategy is proposed based on index modulation (IM), where the positions of pilots are flexibly changed in the data frame, and additional information bits can be conveyed by indices of pilots. Furthermore, a turbo receiving algorithm is developed, which jointly performs the detection of pilot indices and channel estimation in an iterative manner. It is shown that the proposed turbo receiver works well even under the situation where the prior knowledge of channel state information is outdated. Analytical and simulation results validate that the proposed schemes achieve significant enhancement of bit-error rate performance and channel estimation accuracy, whilst attaining higher spectral efficiency in comparison with its classical counterpart.

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arXiv2021

Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information

Huge overhead of beam training poses a significant challenge to mmWave communications. To address this issue, beam tracking has been widely investigated whereas existing methods are hard to handle serious multipath interference and non-stationary scenarios. Inspired by the spatial similarity between low-frequency and mmWave channels in non-standalone architectures, this paper proposes to utilize prior low-frequency information to predict the optimal mmWave beam, where deep learning is adopted to enhance the prediction accuracy. Specifically, periodically estimated low-frequency channel state information (CSI) is applied to track the movement of user equipment, and timing offset indicator is proposed to indicate the instant of mmWave beam training relative to low-frequency CSI estimation. Meanwhile, long-short term memory networks based dedicated models are designed to implement the prediction. Simulation results show that our proposed scheme can achieve higher beamforming gain than the conventional methods while requiring little overhead of mmWave beam training.

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arXiv2019

RIS-Aided Offshore Communications with Adaptive Beamforming and Service Time Allocation

Reconfigurable intelligent surfaces (RISs), which can deliberately adjust the phase of incident waves, have shown enormous potentials to reconfigure the signal propagation for performance enhancement. In this paper, we investigate the RIS-aided offshore system to provide a cost-effective coverage of high-speed data service. The shipborne RIS is placed offshore to improve the signal quality at the vessels, and the coastal base station is equipped with low-cost reconfigurable reflect-arrays (RRAs), instead of the conventional costly fully digital antenna arrays (FDAAs), to reduce the hardware cost. In order to meet the rate requirements of diversified maritime activities, the effective sum rate (ESR) is studied by jointly optimizing the beamforming scheme and the service time allocated to each vessel. The optimal allocation scheme is derived, and an efficient fixed-point based alternating ascent method is developed to obtain a suboptimal solution to the non-convex beamforming problem. Numerical results show that the ESR is considerably improved with the aid of the RIS, and the proposed scheme using the hardwareefficient RRAs has only a slight performance loss, compared to its FDAA-based counterpart.

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arXiv2017

Compressive Sensing Techniques for Next-Generation Wireless Communications

A range of efficient wireless processes and enabling techniques are put under a magnifier glass in the quest for exploring different manifestations of correlated processes, where sub-Nyquist sampling may be invoked as an explicit benefit of having a sparse transform-domain representation. For example, wide-band next-generation systems require a high Nyquist-sampling rate, but the channel impulse response (CIR) will be very sparse at the high Nyquist frequency, given the low number of reflected propagation paths. This motivates the employment of compressive sensing based processing techniques for frugally exploiting both the limited radio resources and the network infrastructure as efficiently as possible. A diverse range of sophisticated compressed sampling techniques is surveyed and we conclude with a variety of promising research ideas related to large-scale antenna arrays, non-orthogonal multiple access (NOMA), and ultra-dense network (UDN) solutions, just to name a few.

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arXiv2017

Spectrum and Energy Efficient Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens Antenna Array

The recent concept of beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio-frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems without obvious performance loss. However, the fundamental limit of existing beamspace MIMO is that, the number of supported users cannot be larger than the number of RF chains at the same time-frequency resources. To break this fundamental limit, in this paper we propose a new spectrum and energy efficient mmWave transmission scheme that integrates the concept of non-orthogonal multiple access (NOMA) with beamspace MIMO, i.e., beamspace MIMO-NOMA. By using NOMA in beamspace MIMO systems, the number of supported users can be larger than the number of RF chains at the same time-frequency resources. Particularly, the achievable sum rate of the proposed beamspace MIMO-NOMA in a typical mmWave channel model is analyzed, which shows an obvious performance gain compared with the existing beamspace MIMO. Then, a precoding scheme based on the principle of zero-forcing (ZF) is designed to reduce the inter-beam interferences in the beamspace MIMO-NOMA system. Furthermore, to maximize the achievable sum rate, a dynamic power allocation is proposed by solving the joint power optimization problem, which not only includes the intra-beam power optimization, but also considers the inter-beam power optimization. Finally, an iterative optimization algorithm with low complexity is developed to realize the dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA can achieve higher spectrum and energy efficiency compared with existing beamspace MIMO.

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arXiv2017

Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems

Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, previous work on multiuser MIMO has shown that the codebook size for channel feedback should scale exponentially with the number of base station (BS) antennas, which is greatly increased in massive MIMO systems. To reduce the codebook size and feedback overhead, we propose an angle-of-departure (AoD)-adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. We also provide performance analysis of the proposed codebook in the large-dimensional regime, where we prove that to limit the capacity degradation within an acceptable level, the required number of feedback bits only scales linearly with the number of resolvable (path) AoDs, which is much smaller than the number of BS antennas. Moreover, we compare quantized channel feedback using the proposed AoD-adaptive subspace codebook with analog channel feedback. Extensive simulations that verify the analytical results are provided.

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arXiv2017

Codebook Design for Channel Feedback in Lens-Based Millimeter-Wave Massive MIMO Systems

The number of radio frequency (RF) chains can be reduced through beam selection in lens-based millimeter-wave (mmWave) massive MIMO systems, where the equivalent channel between RF chains and multiple users is required at the BS to achieve the multi-user multiplexing gain. However, to the best of our knowledge, there is no dedicated codebook for the equivalent channel feedback in such systems. In this paper, we propose the dimension-reduced subspace codebook, which achieves a significant reduction of the feedback overhead and codebook size. Specifically, we firstly utilize the limited scattering property of mmWave channels to generate the high-dimensional vectors in the channel subspace. Then, according to the function of lens and beam selector, we propose the dimension-reduced subspace codebook to quantize the equivalent channel vector.Moreover, the performance analysis of the proposed codebook is also provided.Finally, simulation results show the superior performance of the proposed dimension-reduced subspace codebook compared with conventional codebooks.

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arXiv2017

On the Performance of Channel Statistics-Based Codebook for Massive MIMO

The channel feedback overhead for massive MIMO systems with a large number of base station (BS) antennas is very high, since the number of feedback bits of traditional codebooks scales linearly with the number of BS antennas. To reduce the feedback overhead, an effective codebook based on channel statistics has been designed, where the required number of feedback bits only scales linearly with the rank of the channel correlation matrix. However, this attractive conclusion was only intuitively explained and then verified through simulation results in the literature, while no rigorous theoretical proof has been provided. To fill in the gap between the theoretical conclusion and simulation results, in this paper, we quantitatively analyze the performance of the channel statistics-based codebook. Specifically, we firstly introduce the rate gap between the ideal case of perfect channel state information at the transmitter and the practical case of limited channel feedback, where we find that the rate gap is dependent on the quantization error of the codebook. Then, we derive an upper bound of the quantization error, based on which we prove that the required feedback bits to ensure a constant rate gap only scales linearly with the rank of the channel correlation matrix. Finally, numerical results are provided to verify this conclusion. To the best of our knowledge, our work is the first one to provide a rigorous proof of this conclusion.

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arXiv2016

GMD-Based Hybrid Precoding For Millimeter-Wave Massive MIMO Systems

Hybrid precoding can significantly reduce the number of required radio frequency (RF) chains and relieve the huge energy consumption in mmWave massive MIMO systems, thus attracting much interests from academic and industry. However, most existing hybrid precoding schemes are based on singular value decomposition (SVD). Due to the very different sub-channel signal-to-noise ratios (SNRs) after SVD, complicated bit allocations is usually required to match the sub-channel SNRs. To solve this problem, we propose a geometric mean decomposition (GMD)-based hybrid precoding scheme to avoid the complicated bit allocation. Its basic idea is to seek a pair of analog and digital precoding matrices that are sufficiently close to the optimal unconstrained GMD precoding matrix. Specifically, we design the analog (digital) precoding matrix while keeping the digital (analog) precoding matrix fixed. Further, the principle of basis pursuit is utilized in the design of analog precoding matrix, while we obtain the digital precoding matrix by projecting the GMD operation on the digital precoding matrix. Simulation results verify that the proposed GMD-based hybird precoding scheme outperforms conventional SVD-based hybrid precoding schemes and achieves much better bit error rate (BER) performance with low complexity.

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arXiv2016

Channel Estimation for mmWave Massive MIMO Based Access and Backhaul in Ultra-Dense Network

Millimeter-wave (mmWave) massive MIMO used for access and backhaul in ultra-dense network (UDN) has been considered as the promising 5G technique. We consider such an heterogeneous network (HetNet) that ultra-dense small base stations (BSs) exploit mmWave massive MIMO for access and backhaul, while macrocell BS provides the control service with low frequency band. However, the channel estimation for mmWave massive MIMO can be challenging, since the pilot overhead to acquire the channels associated with a large number of antennas in mmWave massive MIMO can be prohibitively high. This paper proposes a structured compressive sensing (SCS)-based channel estimation scheme, where the angular sparsity of mmWave channels is exploited to reduce the required pilot overhead. Specifically, since the path loss for non-line-of-sight paths is much larger than that for line-of-sight paths, the mmWave massive channels in the angular domain appear the obvious sparsity. By exploiting such sparsity, the required pilot overhead only depends on the small number of dominated multipath. Moreover, the sparsity within the system bandwidth is almost unchanged, which can be exploited for the further improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterpart, and it can approach the performance bound.

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arXiv2016

Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO

Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the non-orthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multi-cell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.

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arXiv2016

Compressive Sensing Based Multi-User Detector for the Large-Scale SM-MIMO Uplink

Conventional spatial modulation (SM) is typically considered for transmission in the downlink of small-scale MIMO systems, where a single one of a set of antenna elements (AEs) is activated for implicitly conveying extra bits. By contrast, inspired by the compelling benefits of large-scale MIMO (LS- MIMO) systems, here we propose a LS-SM-MIMO scheme for the uplink (UL), where each user having multiple AEs but only a single radio frequency (RF) chain invokes SM for increasing the UL-throughput. At the same time, by relying on hundreds of AEs but a small number of RF chains, the base station (BS) can simultaneously serve multiple users whilst reducing the power consumption. Due to the large number of AEs of the UL-users and the comparably small number of RF chains at the BS, the UL multi-user signal detection becomes a challenging large-scale under-determined problem. To solve this problem, we propose a joint SM transmission scheme and a carefully designed structured compressive sensing (SCS)-based multi-user detector (MUD) to be used at the users and BS, respectively. Additionally, the cyclic- prefix single-carrier (CPSC) is used to combat the multipath channels, and a simple receive AE selection is used for the improved performance over correlated Rayleigh-fading MIMO channels. We demonstrate that the aggregate SM signal consisting of SM signals of multiple UL-users in one CPSC block appears the distributed sparsity. Moreover, due to the joint SM transmission scheme, aggregate SM signals in the same transmission group exhibit the group sparsity. By exploiting these intrinsically sparse features, the proposed SCS-based MUD can reliably detect the resultant SM signals with low complexity. Simulation results demonstrate that the proposed SCS-based MUD achieves a better signal detection performance than its counterparts even with higher UL-throughtput.

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arXiv2016

Near-Optimal Signal Detector Based on Structured Compressive Sensing for Massive SM-MIMO

Massive spatial modulation (SM)-MIMO, which employs massive low-cost antennas but few power-hungry transmit radio frequency (RF) chains at the transmitter, is recently proposed to provide both high spectrum efficiency and energy efficiency for future green communications. However, in massive SM-MIMO, the optimal maximum likelihood (ML) detector has the prohibitively high complexity, while state-of-the-art low-complexity detectors for conventional small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM signals, we propose a low-complexity signal detector based on structured compressive sensing (SCS) to improve the signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM signals by leveraging the structured sparsity. In addition, we also propose the SM signal interleaving to permute SM signals in the same transmission group, whereby the channel diversity can be exploited to further improve the signal detection performance. Theoretical analysis quantifies the performance gain from SM signal interleaving, and simulation results demonstrate the near-optimal performance of the proposed scheme.

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arXiv2016

On the Spectral Efficiency of Massive MIMO Systems with Low-Resolution ADCs

The low-resolution analog-to-digital convertor (ADC) is a promising solution to significantly reduce the power consumption of radio frequency circuits in massive multiple-input multiple-output (MIMO) systems. In this letter, we investigate the uplink spectral efficiency (SE) of massive MIMO systems with low-resolution ADCs over Rician fading channels, where both perfect and imperfect channel state information are considered. By modeling the quantization noise of low-resolution ADCs as an additive quantization noise, we derive tractable and exact approximation expressions of the uplink SE of massive MIMO with the typical maximal-ratio combining (MRC) receivers. We also analyze the impact of the ADC resolution, the Rician $K$-factor, and the number of antennas on the uplink SE. Our derived results reveal that the use of low-cost and low-resolution ADCs can still achieve satisfying SE in massive MIMO systems.

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arXiv2015

Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems

Channel state information at the transmitter (CSIT) is essential for frequency-division duplexing (FDD) massive MIMO systems, but conventional solutions involve overwhelming overhead both for downlink channel training and uplink channel feedback. In this letter, we propose a joint CSIT acquisition scheme to reduce the overhead. Particularly, unlike conventional schemes where each user individually estimates its own channel and then feed it back to the base station (BS), we propose that all scheduled users directly feed back the pilot observation to the BS, and then joint CSIT recovery can be realized at the BS. We further formulate the joint CSIT recovery problem as a low-rank matrix completion problem by utilizing the low-rank property of the massive MIMO channel matrix, which is caused by the correlation among users. Finally, we propose a hybrid low-rank matrix completion algorithm based on the singular value projection to solve this problem. Simulations demonstrate that the proposed scheme can provide accurate CSIT with lower overhead than conventional schemes.

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arXiv2015

Joint Channel Training and Feedback for FDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we firstly exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead.

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arXiv2015

Structured Compressive Sensing Based Superimposed Pilot Design in Downlink Large-Scale MIMO Systems

Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large-scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, we propose a spectrum-efficient superimposed pilot design for downlink large-scale MIMO scenarios, where frequency-domain pilots of different transmit antennas occupy the completely same subcarriers in the freqency domain. Meanwhile, spatial-temporal common sparsity of large-scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realized by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound.

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arXiv2015

Asymptotic Orthogonality Analysis of Time-Domain Sparse Massive MIMO Channels

The theoretical analysis of downlink massive MIMO usually assumes the ideal Gaussian channel matrix with asymp- totic orthogonality of channel vectors associated with different users, since it can provide the favorable propagation condition. Meanwhile, recent experiments have shown that massive MIMO channels between a certain user and massive base station antennas appear the spatial common sparsity (SCS) in both the time domain and angle domain. This motivates us to investigate whether realistic sparse massive MIMO channels could provide the favorable propagation condition, and reveal the capacity gap between massive MIMO systems over realistic sparse channels and that under the ideal Gaussian channel matrix assumption. This paper theoretically proves that channel vectors associated with different users in massive MIMO over realistic sparse channels satisfy the asymptotic orthogonality, which indicates that the favorable propagation condition can also be provided. Moreover, the simulation results confirm the theoretical analysis.

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