Performance evaluation and enhancement of massive MIMO communication systems
Tran, Xuan Tuong
Date of Issue2017
School of Electrical and Electronic Engineering
The increasing popularity of smart terminals and their multimedia applications, as well as social networks, leads to tremendous growth in demands for capacity and quality of service (QoS) in cellular networks. To meet these demands, multiple-input multiple-output (MIMO) wireless systems have been developed and they are now a part of current standards such as IEEE 802.11 and the fourth generation (4G) long term evolution (LTE). However, there are several challenges, i.e., spectral crisis, spectral efficiency (SE) and high energy consumption, which cannot be accommodated by current generation networks. The capacity of 4G networks with current technologies is reaching theoretical limit soon. Therefore, we focus on performance analyses and enhancement of massive MIMO system, which is considered as a potential technology for future wireless and mobile communication systems. With massive MIMO technology, a base station (BS) is equipped with a large antenna array to serve users simultaneously over the same radio resources. The main contribution of this thesis is to investigate fundamental performance in terms of SE and energy efficiency (EE) for downlink massive MIMO systems with different precoding schemes under perfect and imperfect channel state information (CSI). In particular, in single-cell scenarios, asymptotic achievable capacities of block diagonalization (BD) precoding and BD-based with space-time block code (STBC) are analyzed. Assuming that the number of BS antennas and the number of antennas per users are large while their ratio remains bounded, the capacity expressions for these two schemes are derived. The optimal number of users and optimal length of training sequence for maximizing the SE are derived for practical implementation. For the case of single-antenna users, the SE of a massive MIMO system with signal-to-leakage-plus-noise ratio precoding scheme (SLNR-PS) is studied. By applying a realistic power consumption model, under the assumption of equal power allocation for all users, the EE maximization problem with respect to number of BS antennas, transmit power and length of training sequence is investigated. Through analysis, the optimal value of each parameter for maximizing the EE is derived when the other parameters are assumed to be known. Moreover, a closed-form expression of the optimal length of training sequence for maximizing SE is derived at high signal-to-noise ratio (SNR) region. Based on these expressions, an alternating optimization algorithm is used to solve the EE maximization problem. It has been shown that the SLNR-PS is lower bounded by that of the zero-forcing precoding scheme (ZF-PS). The optimum EE of SLNR-PS is obtained by a massive MIMO setup with optimal values of transmit power and training sequence. If the quality of channel estimation is low, the SLNR-PS needs to use more BS antennas to achieve the optimum EE, as compared to that with better channel qualities. In addition, under the constraints of transmit power and QoS, we also obtain an energy-efficient power allocation scheme to optimize the EE. The problem of rate profile optimization is also considered while satisfying the QoS and EE target constraints. In multicell scenarios, the pilot contamination affects significantly the performance of massive MIMO systems. This thesis also presents a scheme, which consists of a two-layer approach of optimal tilt adaptation based on the users’ locations and optimal 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 exchange among 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. A coordinated multipoint transmission for two-tier HetNets with massive MIMO technology and practical deployment is also proposed in this thesis. The small cell base stations (SBSs) are utilized outside the inner region of macrocell base stations (MBSs) to improve the performance of macrocell edge users. Based on the stochastic geometry approach, the SE and EE of the proposed system are analyzed. The EE maximization problem is formulated and solved effectively by using the alternating optimization algorithm under the constraints of QoS, density of SBS, available number of MBS antennas and MBS transmit power. The algorithm is shown to converge quickly. The impacts of the small cell density, inner region size and massive MIMO on the network performance are explicitly examined. By approximating the interference distribution by moment matching with the Gamma distribution, the coverage probability of the two-tier Hetnet is also derived. Numerical results are provided to validate the theoretical analysis. It has been shown that the proposed scheme outperforms the conventional maximum receive power association scheme. Based on the combinational use of the small cell deployment, massive MIMO and joint transmission, it has been shown that the two-tier HetNet can provide high spectral efficiency and energy efficiency.
DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems