Tian Xie

Comparison study of non-orthogonal multiple access schemes for 5G

With the development of mobile Internet and Internet of things (IoT), the 5th generation (5G) wireless communications will foresee explosive increase in mobile traffic. To address challenges in 5G such as higher spectral efficiency, massive connectivity, and lower latency, some non-orthogonal multiple access (NOMA) schemes have been recently actively investigated, including power-domain NOMA, multiple access with low-density spreading (LDS), sparse code multiple access (SCMA), multiuser shared access (MUSA), pattern division multiple access (PDMA), etc. Different from conventional orthogonal multiple access (OMA) schemes, NOMA can realize overloading by introducing some controllable interferences at the cost of slightly increased receiver complexity, which can achieve significant gains in spectral efficiency and accommodate much more users. In this paper, we will discuss basic principles and key features of three typical NOMA schemes, i.e., SCMA, MUSA, and PDMA. Whats more, their performance in terms of uplink bit error rate (BER) will be compared. Simulation results show that in typical Rayleigh fading channels, SCMA has the best performance, while the BER performance of MUSA and PDMA are very close to each other. In addition, we also analyze the performance of PDMA using the same factor graph as SCMA, which indicates that the performance gain of SCMA over PDMA comes from both the difference of factor graph and the codebook optimization.

Fast Channel Tracking for Terahertz Beamspace Massive MIMO Systems

The recent concept of beamspace multiple input multiple output (MIMO) with discrete lens array can utilize beam selection to reduce the number of radio-frequency chains (RF) required in terahertz (THz) massive MIMO systems. However, to achieve the capacity-approaching performance, beam selection requires information on a beamspace channel of large size. This is difficult to obtain since the user mobility usually leads to the fast variation of THz beamspace channels, and the conventional real-time channel estimation schemes involve unaffordable pilot overhead. To solve this problem, in this paper, we propose the a priori aided (PA) channel tracking scheme. Specifically, by considering a practical user motion model, we first excavate a temporal variation law of the physical direction between the base station and each mobile user. Then, based on this law and the special sparse structure of THz beamspace channels, we propose to utilize the obtained beamspace channels in the previous time slots to predict the prior information of the beamspace channel in the following time slot without channel estimation. Finally, aided by the obtained prior information, the time-varying beamspace channels can be tracked with low pilot overhead. Simulation results verify that to achieve the same accuracy, the proposed PA channel tracking scheme requires much lower pilot overhead and signal-to-noise ratio (SNR) than the conventional schemes.

Low-Complexity SSOR-Based Precoding for Massive MIMO Systems

With the increase of the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems, linear precoding schemes are able to achieve the near-optimal performance, and thus are more attractive than nonlinear precoding techniques. However, conventional linear precoding schemes such as zero-forcing (ZF) precoding involve the matrix inversion of large size with high computational complexity, especially in massive MIMO systems. To reduce the complexity, in this letter, we propose a low-complexity linear precoding scheme based on the symmetric successive over relaxation (SSOR) method. Moreover, we propose a simple way to approximate the optimal relaxation parameter of the SSOR-based precoding by exploiting the channel property of asymptotical orthogonality in massive MIMO systems. We show that the proposed SSOR-based precoding can reduce the complexity of the classical ZF precoding by about one order of magnitude without performance loss, and it also outperforms the recently proposed linear approximate precoding schemes in typical fading channels.

A Low-Complexity Linear Precoding Scheme Based on SOR Method for Massive MIMO Systems

Conventional linear precoding schemes in massive multiple-input-multiple-output (MIMO) systems, such as regularized zero-forcing (RZF) precoding, have near-optimal performance but suffer from high computational complexity due to the required matrix inversion of large size. To solve this problem, we propose a successive overrelaxation (SOR)-based precoding scheme to approximate the matrix inversion by exploiting the asymptotically orthogonal channel property in massive MIMO systems. The proposed SOR- based precoding can reduce the complexity by about one order of magnitude, and it can also approach the classical RZF precoding with negligible performance loss. We also prove that the proposed SOR-based precoding enjoys a faster convergence rate than the recently proposed Neumann-based precoding. In addition, to guarantee the performance of SOR-based precoding, we propose a simple way to choose the optimal relaxation parameter in practical massive MIMO systems. Simulation results verify the advantages of SOR-based precoding in convergence rate and computational complexity in typical massive MIMO configurations.

Low complexity signal detector based on SSOR method for massive MIMO systems

For massive MIMO systems, linear signal detectors, such as zero forcing (ZF) signal detector, can achieve the near-optimal performance, but usually involve high complexity due to the required matrix inversion of large size. In this paper, we propose a signal detector based on symmetric successive over-relaxation (SSOR) method without matrix inversion, which can reduce the complexity by one order of magnitude. To guarantee the performance of SSOR-based signal detector in practice, we also propose a simple quantified relaxation parameter for SSOR-based signal detector, which only depends on the MIMO system configuration. Analysis verifies that the proposed SSOR-based signal detector can reduce the complexity of ZF signal detector by about one order of magnitude. Meanwhile, simulation results show that SSOR-based signal detector can achieve the near-optimal performance of ZF signal detector with only a small number of iterations.

Richardson Method Based Linear Precoding with Low Complexity for Massive MIMO Systems

For massive MIMO system with hundreds of antennas at the base station (BS), zero forcing (ZF) precoding can achieve the near-optimal capacity due to the asymptotically orthogonal channel, but it involves complicated matrix inversion of large size. In this paper, we propose a Richardson Method (RM) based precoding to avoid the complicated matrix inversion in an iterative way, which can reduce the complexity by one order of magnitude. We also prove that the optimal relaxation parameter to RM can be approached by a simple and quantified value to maximize the convergence rate of RM-based precoding, which only depends on the number of BS antennas and the number of users. Simulation results show that RM-based precoding can achieve the near-optimal performance of ZF precoding with only a small number of iterations.

Low-Complexity LSQR-Based Linear Precoding for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) using a large number of antennas at the base station (BS) is a promising technique for the next-generation 5G wireless communications. It has been shown that linear precoding schemes can achieve near-optimal performance in massive MIMO systems. However, classical linear precoding schemes such as zero- forcing (ZF) precoding suffer from high complexity due to the fact they require the matrix inversion of a large size. In this paper, we propose a low-complexity precoding scheme based on the least square QR (LSQR) method to realize the near-optimal performance of ZF precoding without matrix inversion. We show that the proposed LSQR-based precoding can reduce the complexity of ZF precoding by about one order of magnitude. Simulation results verify that the proposed LSQR-based precoding can provide a better tradeoff between complexity and performance than the recently proposed Neumann-based precoding.

Precoding for mmWave massive MIMO

Abstract Precoding plays an indispensable role in multiple-input multiple-output (MIMO) systems because it can compensate the path loss and eliminate the interference. While the fundamentals of precoding are the same regardless of carrier frequency, signal processing in millimeter-wave (mmWave) massive MIMO systems is subject to a set of nontrivial practical constraints, leading to different hardware architecture. This means that traditional precoding is no longer suitable, and we need to explore some new precoding schemes for mmWave massive MIMO systems. In this chapter, we first provide a brief introduction of the characteristics of the mmWave channel. After that, we review the traditional digital precoding and analog beamforming, and explain why they cannot be directly extended to mmWave massive MIMO systems. Then, we investigate a novel precoding scheme called hybrid analog and digital precoding. The key idea of hybrid precoding is to divide the conventional digital precoder into a small-size digital precoder (realized by a small number of radio frequency (RF) chains) to cancel interference and a large-size analog beamformer (realized by a large number of analog phase shifters) to increase the antenna array gain. Thanks to the low-rank characteristics of the mmWave channel in the spatial domain, a small-size digital precoder is enough to achieve the spatial multiplexing gain, making hybrid precoding enjoy the satisfying sum-rate performance with only a small number of RF chains. Finally, we summarize this chapter by comparing hybrid precoding with traditional digital precoding and analog beamforming, and highlight some other potential precoding schemes for mmWave massive MIMO systems.

A Prefiltering C-RAN Architecture with Compressed Link Data Rate in Massive MIMO

Massive multiple-input multiple-output (MIMO) is a promising technology in the next 5G communications. Directly merging massive MIMO with cloud radio access network (C-RAN) systems will cause disastrous link data overload, which greatly exceeds the limitation of current 4G wireless standards. To solve this problem, we propose a pre-filtering C-RAN architecture in this paper to compress the inter connection link data rate between remote radio units (RRUs) and baseband units (BBUs), which is based on the structure of linear data detection algorithms and able to achieve lossless performance if perfect channel information (CSI) can be acquired. To make the pre-filtering architecture feasible in practical environments, we further propose two channel estimation methods. One using demodulation reference signal (DMRS) can achieve the data rate compression without performance loss, while the other using sounding reference signal (SRS) can keep the thin structures of RRUs as much as possible. Analysis on practical systems and simulation results show that the proposed architecture can provide a better trade off between hardware implementation cost, system performance and traffic load reduction than conventional architectures.