Performance Gains of Optimal Antenna Deployment for Massive MIMO Systems

We consider the uplink of a single-cell multi-user multiple-input multiple-output (MIMO) system with several single antenna transmitters/users and one base station with $N$ antennas in the $N\rightarrow\infty$ regime. The base station antennas are evenly distributed to $n$ admissable locations throughout the cell. First, we show that a reliable (per-user) rate of $O(\log n)$ is achievable through optimal locational optimization of base station antennas. We also prove that an $O(\log n)$ rate is the best possible. Therefore, in contrast to a centralized or circular deployment, where the achievable rate is at most a constant, the rate with a general deployment can grow logarithmically with $n$, resulting in a certain form of "macromultiplexing gain." Second, using tools from high-resolution quantization theory, we derive an accurate formula for the best achievable rate given any $n$ and any user density function. According to our formula, the dependence of the optimal rate on the user density function $f$ is curiously only through the differential entropy of $f$. In fact, the optimal rate decreases linearly with the differential entropy, and the worst-case scenario is a uniform user density. Numerical simulations confirm our analytical findings.Comment: GLOBECOM 201

Performance Analysis of Millimeter Wave Massive MIMO Systems in Centralized and Distributed Schemes

This paper considers downlink multi-user millimeter-wave massive multiple-input multiple-output (MIMO) systems in both centralized and distributed configurations, referred to as C-MIMO and D-MIMO, respectively. Assuming the fading channel is composite and comprised of both large-scale fading and small-scale fading, a hybrid precoding algorithm leveraging antenna array response vectors is applied into both the C-MIMO system with fully connected structure and the D-MIMO system with partially connected structure. First, the asymptotic spectral efficiency (SE) of an arbitrary user and the asymptotic average SE of the cell for the C-MIMO system are analyzed. Then, two radio access unit (RAU) selection algorithms are proposed for the D-MIMO system, based on minimal distance (D-based) and maximal signal-to-interference-plus-noise-ratio (SINR) (SINR-based), respectively. For the D-MIMO system with circular layout and D-based RAU selection algorithm, the upper bounds on the asymptotic SE of an arbitrary user and the asymptotic average SE of the cell are also investigated. Finally, numerical results are provided to assess the analytical results and evaluate the effects of the numbers of total transmit antennas and users on system performance. It is shown that, from the perspective of the cell, the D-MIMO system with D-based scheme outperforms the C-MIMO system and achieves almost alike performance compared with the SINR-based solution while requiring less complexity.Peer reviewe

Group Sparse Precoding for Cloud-RAN with Multiple User Antennas

Cloud radio access network (C-RAN) has become a promising network architecture to support the massive data traffic in the next generation cellular networks. In a C-RAN, a massive number of low-cost remote antenna ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed low-latency fronthaul links, which enables efficient resource allocation and interference management. As the RAPs are geographically distributed, the group sparse beamforming schemes attracts extensive studies, where a subset of RAPs is assigned to be active and a high spectral efficiency can be achieved. However, most studies assumes that each user is equipped with a single antenna. How to design the group sparse precoder for the multiple antenna users remains little understood, as it requires the joint optimization of the mutual coupling transmit and receive beamformers. This paper formulates an optimal joint RAP selection and precoding design problem in a C-RAN with multiple antennas at each user. Specifically, we assume a fixed transmit power constraint for each RAP, and investigate the optimal tradeoff between the sum rate and the number of active RAPs. Motivated by the compressive sensing theory, this paper formulates the group sparse precoding problem by inducing the $\ell_0$-norm as a penalty and then uses the reweighted $\ell_1$ heuristic to find a solution. By adopting the idea of block diagonalization precoding, the problem can be formulated as a convex optimization, and an efficient algorithm is proposed based on its Lagrangian dual. Simulation results verify that our proposed algorithm can achieve almost the same sum rate as that obtained from exhaustive search