Achieving "Massive MIMO" Spectral Efficiency with a Not-so-Large Number of Antennas

The main focus and contribution of this paper is a novel network-MIMO TDD architecture that achieves spectral efficiencies comparable with "Massive MIMO", with one order of magnitude fewer antennas per active user per cell. The proposed architecture is based on a family of network-MIMO schemes defined by small clusters of cooperating base stations, zero-forcing multiuser MIMO precoding with suitable inter-cluster interference constraints, uplink pilot signals reuse across cells, and frequency reuse. The key idea consists of partitioning the users population into geographically determined "bins", such that all users in the same bin are statistically equivalent, and use the optimal network-MIMO architecture in the family for each bin. A scheduler takes care of serving the different bins on the time-frequency slots, in order to maximize a desired network utility function that captures some desired notion of fairness. This results in a mixed-mode network-MIMO architecture, where different schemes, each of which is optimized for the served user bin, are multiplexed in time-frequency. In order to carry out the performance analysis and the optimization of the proposed architecture in a clean and computationally efficient way, we consider the large-system regime where the number of users, the number of antennas, and the channel coherence block length go to infinity with fixed ratios. The performance predicted by the large-system asymptotic analysis matches very well the finite-dimensional simulations. Overall, the system spectral efficiency obtained by the proposed architecture is similar to that achieved by "Massive MIMO", with a 10-fold reduction in the number of antennas at the base stations (roughly, from 500 to 50 antennas).Comment: Full version with appendice (proofs of theorems). A shortened version without appendice was submitted to IEEE Trans. on Wireless Commun. Appendix B was revised after submissio

A Dynamic Clustering and Resource Allocation Algorithm for Downlink CoMP Systems with Multiple Antenna UEs

Coordinated multi-point (CoMP) schemes have been widely studied in the recent years to tackle the inter-cell interference. In practice, latency and throughput constraints on the backhaul allow the organization of only small clusters of base stations (BSs) where joint processing (JP) can be implemented. In this work we focus on downlink CoMP-JP with multiple antenna user equipments (UEs) and propose a novel dynamic clustering algorithm. The additional degrees of freedom at the UE can be used to suppress the residual interference by using an interference rejection combiner (IRC) and allow a multistream transmission. In our proposal we first define a set of candidate clusters depending on long-term channel conditions. Then, in each time block, we develop a resource allocation scheme by jointly optimizing transmitter and receiver where: a) within each candidate cluster a weighted sum rate is estimated and then b) a set of clusters is scheduled in order to maximize the system weighted sum rate. Numerical results show that much higher rates are achieved when UEs are equipped with multiple antennas. Moreover, as this performance improvement is mainly due to the IRC, the gain achieved by the proposed approach with respect to the non-cooperative scheme decreases by increasing the number of UE antennas.Comment: 27 pages, 8 figure