Search

Different from the existing works, in this paper we investigate the pilot allocation in multi-cell massive multiple-input multiple-output (MIMO) systems, aiming to maximize the spectral efficiency (SE) subject to the constraint that the number of user equipments (UEs) satisfying the quality of service (QoS) is maximized. Since the considered bilevel optimization problem that is tough to handle, we resort to the greedy algorithms with low computational complexity. We propose a QoS constrained (QC) pilot allocation scheme, which sequentially allocates the pilot sequence having large interference power to the UE with small signal power so that the number of UEs satisfying QoS can be maximized and then the SE can also be maximized. Simulation results show that the propose QC scheme outperforms the conventional scheme and smart pilot allocation (SPA) scheme. [ABSTRACT FROM AUTHOR]

For massive multiuser multiple input multiple output (MIMO) (MU-MIMO) systems, pilot contamination (PC) and data interference (DI) are two main factors that affect the accuracy of the superimposed pilot (SP) based channel estimation, provided that the channel coherence time is less than the total number of users. In this paper, to tackle the effect of the PC, we propose a block-diagonal Grassmannian line packing (GLP) approach, in which the specific sequences for different cells are first designed based on the GLP, and then are block-diagonally extended to form the SP matrices for the users in different cells. To also alleviate the effect of DI on the channel estimation, with the designed SP matrices, an iterative channel estimation (ICE) method based on Tikhonov regularization is presented. The impact that the proposed ICE method has on the signal-to-interference-plus-noise ratio with matched filtering receiver is theoretically analyzed. Moreover, the optimal power allocation factor to the SP by maximizing the spectral efficiency (SE) of the target cell is also derived. Numerical results show that the proposed ICE method with the designed SP matrices effectively mitigates the effects of the PC and DI, and hence, improves the accuracy of channel estimation and further the SE of the massive MU-MIMO systems. [ABSTRACT FROM AUTHOR]

In a massive multiple-input multiple-output (MIMO) system, the serious pilot contamination problem severely restricts its ability to boost the spectrum utilization. To tackle this problem, the conventional concept of softly reusing the spectrum resources for the inner regions of the neighboring cells has been extended to reuse the pilot resources so as to accommodate more users. Alternatively, properly selecting the users to reuse the pilots among the neighboring cells can alleviate the pilot contamination problem as well. In this paper, we delicately integrate these two concepts to properly reuse the pilots allocated to the outer regions of the cells. By well designing the hazard- and secure-edge regions to manage the pilot reuse, the increment of interference can be effectively suppressed, and, consequently, the more accommodated users can contribute to the remarkably higher sum data rates. Furthermore, via some adaptive power adjustment for the users within different regions, the user data rates can further be significantly enhanced. [ABSTRACT FROM AUTHOR]

This paper is concerned with the sum-rate maximization problem in multiple-input multiple-output interference channels. We design the transmit covariance matrix, associated with each transmitter to maximize the sum-rate of the network when the number of data symbols is unknown. The design problem is nonconvex and then hard to be solved. We devise a semidistributed design methodology according to majorization–minimization technique to deal with the optimization problem. This leads us to a convex quadratically constrained quadratic program at each iteration with a semiclosed form solution. The proposed method is computationally efficient and converges to stationary points of the problem. The fact that the method is based on a distributed processing framework along with relatively low computational burden ( $\mathcal{O}(\max \lbrace M,N\rbrace ^{2.3})$ per iteration with $M$ and $N$ being the number of antennas at the transmitter and receiver, respectively) for the user terminals can make it a potential candidate for practical implementations. Finally, we extend the proposed method for the case with channel estimation errors. Simulation results verify the efficiency of the proposed method, and in a typical scenario we have around 60 times lower run-time when compared to the counterparts of the method. [ABSTRACT FROM AUTHOR]

Two-stage beamforming is a transmit strategy that uses two types of beamformers to reduce the feedback overhead of frequency-division-duplexing massive multiple-input multiple-output systems that are spatially correlated. In this paper, we present large system analysis of two-stage beamforming when a transmitter has limited channel information from feedback and when regularized-zero-forcing (RZF) is used as a second-stage beamformer. We consider two random-vector-quantization-based feedback schemes that can be respectively applied when users have perfect channel information or perfect effective channel information. For each feedback method, we analyze both expected signal-to-interference-plus-noise ratio (SINR) and expected rate loss and then characterize their bounds as a function of the number of feedback bits. From the characterization, we reveal that the sum rate of two-stage beamforming is very sensitive to the regularization parameter of RZF, especially when the number of feedback bits is limited. Motivated by this, we derive the optimal regularization parameter that maximizes the SINR of two-stage beamforming with limited feedback. Using simulations, we verify the tightness of the characterized bounds and quantify how the use of the optimal regularization parameter improves the sum rate of two-stage beamforming. [ABSTRACT FROM AUTHOR]

This paper considers a novel virtual pilot-assisted channel estimation algorithm for multiple input multiple output single carrier frequency domain equalization (MIMO-SCFDE) systems over fast time-varying multipath channels. Given that the fast time-varying channels can no longer support the assumption that the channel frequency-domain response remains invariant for the duration of one SC-FDE symbol, most of the channel estimation methods cannot be directly applied to MIMO-SCFDE systems. We put forward an iterative interfrequency interference cancellation method for the time-varying multipath channels over one SC-FDE symbol. The proposed method utilizes the virtual pilot-assisted channel estimation and partial minimum-mean-square-error equalization to improve the accuracy of the estimation. Simulation results validate the improved performance of the proposed method for MIMO-SCFDE systems over fast time-varying multipath channels. [ABSTRACT FROM AUTHOR]