Multiple signal classification (MUSIC) has been widely applied in multiple-input multiple-output (MIMO) receivers for direction-of-arrival (DOA) estimation. To reduce the cost of radio frequency (RF) chains operating at millimeter-wave bands, hybrid
analog-digital structure has been adopted in massive MIMO transceivers. In this situation, the received signals at the antennas are unavailable to the digital receiver, and as a consequence, the spatial covariance matrix (SCM), which is essential in MUSIC algorithm, cannot be obtained using traditional sample average approach. Based on our previous work, we propose a novel algorithm for SCM reconstruction in hybrid massive MIMO systems with multiple RF chains. By switching the analog beamformers to a group of predetermined DOAs, SCM can be reconstructed through the solutions of a set of linear equations. In addition, based on insightful analysis on that linear equations, a low-complexity algorithm, as well as a careful selection of the predetermined DOAs, will be also presented in this paper. Simulation results show that the proposed algorithms can reconstruct the SCM accurately so that MUSIC algorithm can be well used for DOA estimation in hybrid massive MIMO systems with multiple RF chains.
As the next generation cellular system, 5G network is required to provide a large variety of services for different kinds of terminals, from traditional voice and data services over mobile phones to small packet transmission over massive machine-type
terminals. Although orthogonal-subcarrier based waveform has been widely used nowadays in many practical systems, it can hardly meet the future requirements in the coming 5G networks. Therefore, more flexible waveforms have been proposed to address the unprecedented challenges. In this article, we will provide comprehensive analysis and comparison for the typical waveform candidates. To obtain insightful analysis, we will not only introduce the basic principles of the waveforms but also reveal the underlying characteristics of each waveform. Moreover, a comprehensive comparison in terms of different performance metrics will be also presented in this article, which provide an overall understanding of the new waveforms.
This paper investigates user cooperation in massive multiple-input multiple-output (MIMO) systems with cascaded precoding. The high-dimensional physical channel in massive MIMO systems can be converted into a low-dimensional effective channel through
the inner precoder to reduce the overhead of channel estimation and feedback. The inner precoder depends on the spatial covariance matrix of the channels, and thus the same precoder can be used for different users as long as they have the same spatial covariance matrix. Spatial covariance matrix is determined by the surrounding environment of user terminals. Therefore, the users that are close to each other will share the same spatial covariance matrix. In this situation, it is possible to achieve user cooperation by sharing receiver information through some dedicated link, such as device-to-device communications. To reduce the amount of information that needs to be shared, we propose a decoding codebook based scheme, which can achieve user cooperation without the need of channel state information. Moreover, we also investigate the amount of bandwidth required to achieve efficient user cooperation. Simulation results show that user cooperation can improve the capacity compared to the non-cooperation scheme.
This paper investigates downlink channel estimation in frequency-division duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems. To reduce the overhead of downlink channel estimation and uplink feedback in FDD systems, cascaded pre
coding has been used in massive MIMO such that only a low-dimensional effective channel needs to be estimated and fed back. On the other hand, traditional channel estimations can hardly achieve the minimum mean-square-error (MMSE) performance due to lack of the a priori knowledge of the channels. In this paper, we design and analyze a strategy for downlink channel estimation based on the parametric model in massive MIMO with cascaded precoding. For a parametric model, channel frequency responses are expressed using the path delays and the associated complex amplitudes. The path delays of uplink channels are first estimated and quantized at the base station, then fed forward to the user equipment (UE) through a dedicated feedforward link. In this manner, the UE can obtain the a priori knowledge of the downlink channel in advance since it has been demonstrated that the downlink and the uplink channels can have identical path delays. Our analysis and simulation results show that the proposed approach can achieve near-MMSE performance.
In this paper, we investigate the quantization and the feedback of downlink spatial covariance matrix for massive multiple-input multiple-output (MIMO) systems with cascaded precoding. Massive MIMO has gained a lot of attention recently because of it
s ability to significantly improve the network performance. To reduce the overhead of downlink channel estimation and uplink feedback in frequency-division duplex massive MIMO systems, cascaded precoding has been proposed, where the outer precoder is implemented using traditional limited feedback while the inner precoder is determined by the spatial covariance matrix of the channels. In massive MIMO systems, it is difficult to quantize the spatial covariance matrix because of its large size caused by the huge number of antennas. In this paper, we propose a spatial spectrum based approach for the quantization and the feedback of the spatial covariance matrix. The proposed inner precoder can be viewed as modulated discrete prolate spheroidal sequences and thus achieve much smaller spatial leakage than the traditional discrete Fourier transform submatrix based precoding. Practical issues for the application of the proposed approach are also addressed in this paper.
Large-scale antenna (LSA) has gained a lot of attention recently since it can significantly improve the performance of wireless systems. Similar to multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM,
LSA can be also combined with OFDM to deal with frequency selectivity in wireless channels. However, such combination suffers from substantially increased complexity proportional to the number of antennas in LSA systems. For the conventional implementation of LSA-OFDM, the number of inverse fast Fourier transforms (IFFTs) increases with the antenna number since each antenna requires an IFFT for OFDM modulation. Furthermore, zero-forcing (ZF) precoding is required in LSA systems to support more users, and the required matrix inversion leads to a huge computational burden. In this paper, we propose a low-complexity recursive convolutional precoding to address the issues above. The traditional ZF precoding can be implemented through the recursive convolutional precoding in the time domain so that only one IFFT is required for each user and the matrix inversion can be also avoided. Simulation results show that the proposed approach can achieve the same performance as that of ZF but with much lower complexity.
Large-scale antenna (LSA) or massive multiple-input multiple-output (MIMO) has gained a lot of attention due to its potential to significantly improve system throughput. As a natural evolution from traditional MIMO-orthogonal frequency division multi
plexing (OFDM), LSA has been combined with OFDM to deal with frequency selectivity of wireless channels in most existing works. As an alternative approach, single-carrier (SC) has also been proposed for LSA systems due to its low implementation complexity. In this article, a comprehensive comparison between LSA-OFDM and LSA-SC is presented, which is of interest to the waveform design for the next generation wireless systems.
Large-scale antenna (LSA) has gained a lot of attention due to its great potential to significantly improve system throughput. In most existing works on LSA systems, orthogonal frequency division multiplexing (OFDM) is presumed to deal with frequency
selectivity of wireless channels. Although LSA-OFDM is a natural evolution from multiple-input multiple-output OFDM (MIMO-OFDM), the drawbacks of LSA-OFDM are inevitable, especially when used for the uplink. In this paper, we investigate single-carrier (SC) modulation for the uplink transmission in LSA systems based on a novel waveform recovery theory, where the receiver is designed to recover the transmit waveform while the information-bearing symbols can be recovered by directly sampling the recovered waveform. The waveform recovery adopts the assumption that the antenna number is infinite and the channels at different antennas are independent. In practical environments, however, the antenna number is always finite and the channels at different antennas are also correlated when placing hundreds of antennas in a small area. Therefore, we will also analyze the impacts of such non-ideal environments.
