Multi-cell Massive MIMO Research Articles

Network Sum-Rate Optimization for Multicell Massive MIMO Downlink Based on Coordinated Tilt Adaptation and Game Theory Approach

Performance of massive multiple-input multiple-output systems is mainly limited by the pilot contamination. This letter presents a scheme, which consists of a two-layer approach of coordinated tilt adaptation based on the users’ locations and power allocation based on the game theory, to minimize the effect of pilot contamination, and hence to maximize the network sum rate. The proposed scheme has low computational complexity and it can avoid a heavy signalling exchanging between base stations via backhaul links. Numerical results show that the proposed scheme outperforms the conventional fixed-downtilt systems with equal power allocation, and waterfilling power allocation, respectively.

Sectorization based pilot reuse for improving net spectral efficiency in the multicell massive MIMO system

A sectorization method using the uniform circular array (UCA) is proposed to improve the net spectral efficiency (SE) of the multicell massive MIMO system, which is an important index for evaluating the performance of a communication system. We derive the ergodic achievable uplink net SE per cell of a general sectorized system and obtain its deterministic approximation based on the large random matrix theory. Different weight matrices are considered for the sectorized system and the one with the best performance is utilized for further analysis. The consistency of the deterministic approximation with the result of Monte-Carlo simulation is proved at the same time. At last, numerical results indicate that the net SE per cell can be greatly improved compared to the conventional multicell massive MIMO system, which validates the effectiveness of the sectorization method. Moreover, comparisons with other pilot reuse methods are also made in this paper.

Statistical Beamforming for Interference Mitigation in Multi-cell Massive MIMO Systems

AbstractThis paper proposes a statistical beamforming approach to mitigate interference in multi-cell massive multiple-input multiple-output systems. The proposed approach projects the signal subspace onto the null space of the interference to form the weight vector with the statistical channel state information. In contrast to the existing methods which only utilize the partial null space of the interference, the proposed approach can collect more signal when mitigating the interference. In addition, the large system analysis demonstrates that the interference is eliminated with uniform rectangular arrays. Numerical results show that the proposed approach can achieve higher sum rate than the existing methods and verify the performance analysis.

Channel Estimation and Data Detection for Multicell Massive MIMO Systems in Correlated Channels

In this study, multicell multiuser multiple-input multiple-output systems are considered with very large number of antennas at base station. We consider a physical channel model comprised finite number of angle of arrivals, and propose a channel estimation scheme based on the eigenvalue decomposition of the covariance matrix of the received signal vector. To improve the channel estimation performance under strong intercell interference, a joint channel estimation and optimal multiuser detection scheme is proposed. Furthermore, an optimal linear receiver is developed for the uplink by taking the channel estimation error into account, and derive a closed-form expression for the sum achievable rate of the optimal receiver. Moreover, using the channel reciprocity of a time-division duplex system, we present how to form an optimal precoding vector for the downlink, and how to compute the sum achievable rate in the downlink. Numerical results are provided to verify the analysis and evaluate the performance of the channel estimation and data detection.

Multi-cell massive MIMO transmission with coordinated pilot reuse

In this paper, we propose a coordinated pilot reuse (CPR) approach to reduce the pilot overhead for multi-cell massive multi-input multi-output transmission. Unlike the conventional multi-cell pilot reuse approach in which pilots can only be reused among different cells, the proposed CPR approach allows pilots to be reused among both inter-cell and intra-cell user equipments, and thus, pilot overhead can be efficiently reduced. For spatially correlated Rayleigh fading channels, we first present a CPR-based channel estimation method and a low complexity pilot allocation algorithm. Because CPR might lead to additional pilot interference, we develop a statistically robust uplink receiver and downlink precoder that takes channel estimation errors into account. The proposed uplink receiver and downlink precoder are robust to channel state information inaccuracy, and thus, can guarantee a certain transmission performance. Monte-Carlo simulations illustrate the significant performance improvement in net spectral efficiency offered by the proposed CPR approach.

Power Allocation Schemes for Multicell Massive MIMO Systems

This paper investigates the sum-rate gains brought by power allocation strategies in multicell massive multipleinput multiple-output systems, assuming time-division duplex transmission. For both uplink and downlink, we derive tractable expressions for the achievable rate with zero-forcing receivers and precoders respectively. To avoid high complexity joint optimization across the network, we propose a scheduling mechanism for power allocation, where in a single time slot, only cells that do not interfere with each other adjust their transmit powers. Based on this, corresponding transmit power allocation strategies are derived, aimed at maximizing the sum rate per-cell. These schemes are shown to bring considerable gains over equal power allocation for practical antenna configurations (e.g., up to a few hundred). However, with fixed number of users (N), these gains diminish as M turns to infinity, and equal power allocation becomes optimal. A different conclusion is drawn for the case where both M and N grow large together, in which case: (i) improved rates are achieved as M grows with fixed M/N ratio, and (ii) the relative gains over the equal power allocation diminish as M/N grows. Moreover, we also provide applicable values of M/N under an acceptable power allocation gain threshold, which can be used as to determine when the proposed power allocation schemes yield appreciable gains, and when they do not. From the network point of view, the proposed scheduling approach can achieve almost the same performance as the joint power allocation after one scheduling round, with much reduced complexity.

On Channel Estimation for Massive MIMO With Pilot Contamination

This letter proposes a simple channel estimator for a multi-cell massive MIMO system with pilot contamination. Under moderate or strong pilot contamination, the existing least square (LS) channel estimator suffers from substantial performance degradation while the existing minimum mean square error (MMSE) channel estimator offers much improved performance over the LS estimator. However, the existing MMSE estimator assumes perfect knowledge of cross-cell large-scale channel coefficients and such an assumption is practically unjustifiable. The proposed estimator overcomes the issues of both existing estimators and provides estimation performance close to that of the ideal MMSE estimator. An analytical mean square error (MSE) expression is also derived for the proposed estimator.

On Design of Non-Orthogonal Pilot Signals for a Multi-Cell Massive MIMO System

In this letter, the error variance of the Minimal Mean-Square Error (MMSE) channel estimator is analyzed, and its analytic formula is given. Based on the analytic formula, a designing criterion on the pilot signals is proposed. When training time slots can be large enough, our criterion confirms the well-known fact that orthogonal pilot signals are optimal. But if training time slots are not enough to support this orthogonality, our results show that the error variance is lower-bounded away from zero, no matter what kind of pilot signals are used. In this case, the design of pilot signals should be implemented by using the line packing on a complex Grassmannian manifold.

Resource Allocation Optimization in Multi-User Multi-Cell Massive MIMO Networks Considering Pilot Contamination

In this paper, we study the joint pilot assignment and resource allocation for system energy efficiency (SEE) maximization in the multi-user and multi-cell massive multi-input multi-output network. We explicitly consider the pilot contamination effect during the channel estimation in the SEE maximization problem, which aims to optimize the power allocation, the number of activated antennas, and the pilot assignment. To tackle the SEE maximization problem, we transform it into a subtractive form, which can be solved more efficiently. In particular, we develop an iterative algorithm to solve the transformed problem where optimization of power allocation and number of antennas is performed, and then pilot assignment optimization is conducted sequentially in each iteration. To tackle the first sub-problem, we employ a successive convex approximation (SCA) technique to attain a solvable convex optimization problem. Moreover, we propose a novel iterative low-complexity algorithm based on the Hungarian method to solve the pilot assignment sub-problem. We also describe how the proposed solution approach can be useful to address the sum rate (SR) maximization problem. In addition to the algorithmic developments, we characterize the optimal structure of both SEE and SR maximization problems. The numerical studies are conducted to illustrate the convergence of the proposed algorithms, impacts of different parameters on the SR and SEE, and significant performance gains of the proposed solution compared the conventional design.

Two-Tier Precoding for FDD Multi-Cell Massive MIMO Time-Varying Interference Networks

Massive MIMO is a promising technology in future wireless communication networks. However, it raises a lot of implementation challenges, for example, the huge pilot symbols and feedback overhead, requirement of real-time global CSI, large number of RF chains needed and high computational complexity. We consider a two-tier precoding strategy for multi-cell massive MIMO interference networks, with an outer precoder for inter-cell/inter-cluster interference cancellation, and an inner precoder for intra-cell multiplexing. In particular, to combat with the computational complexity issue of the outer precoding, we propose a low complexity online iterative algorithm to track the outer precoder under time-varying channels. We follow an optimization technique and formulate the problem on the Grassmann manifold. We develop a low complexity iterative algorithm, which converges to the global optimal solution under static channels. In time-varying channels, we propose a compensation technique to offset the variation of the time-varying optimal solution. We show with our theoretical result that, under some mild conditions, perfect tracking of the target outer precoder using the proposed algorithm is possible. Numerical results demonstrate that the two-tier precoding with the proposed iterative compensation algorithm can achieve a good performance with a significant complexity reduction compared with the conventional two-tier precoding techniques in the literature.