Fundamental Limits in Correlated Fading MIMO Broadcast Channels: Benefits of Transmit Correlation Diversity

We investigate asymptotic capacity limits of the Gaussian MIMO broadcast channel (BC) with spatially correlated fading to understand when and how much transmit correlation helps the capacity. By imposing a structure on channel covariances (equivalently, transmit correlations at the transmitter side) of users, also referred to as \emph{transmit correlation diversity}, the impact of transmit correlation on the power gain of MIMO BCs is characterized in several regimes of system parameters, with a particular interest in the large-scale array (or massive MIMO) regime. Taking the cost for downlink training into account, we provide asymptotic capacity bounds of multiuser MIMO downlink systems to see how transmit correlation diversity affects the system multiplexing gain. We make use of the notion of joint spatial division and multiplexing (JSDM) to derive the capacity bounds. It is advocated in this paper that transmit correlation diversity may be of use to significantly increase multiplexing gain as well as power gain in multiuser MIMO systems. In particular, the new type of diversity in wireless communications is shown to improve the system multiplexing gain up to by a factor of the number of degrees of such diversity. Finally, performance limits of conventional large-scale MIMO systems not exploiting transmit correlation are also characterized.Comment: 29 pages, 8 figure

General Rank Multiuser Downlink Beamforming With Shaping Constraints Using Real-valued OSTBC

In this paper we consider optimal multiuser downlink beamforming in the presence of a massive number of arbitrary quadratic shaping constraints. We combine beamforming with full-rate high dimensional real-valued orthogonal space time block coding (OSTBC) to increase the number of beamforming weight vectors and associated degrees of freedom in the beamformer design. The original multi-constraint beamforming problem is converted into a convex optimization problem using semidefinite relaxation (SDR) which can be solved efficiently. In contrast to conventional (rank-one) beamforming approaches in which an optimal beamforming solution can be obtained only when the SDR solution (after rank reduction) exhibits the rank-one property, in our approach optimality is guaranteed when a rank of eight is not exceeded. We show that our approach can incorporate up to 79 additional shaping constraints for which an optimal beamforming solution is guaranteed as compared to a maximum of two additional constraints that bound the conventional rank-one downlink beamforming designs. Simulation results demonstrate the flexibility of our proposed beamformer design

Efficient implementation of channel estimation algorithm for beamforming

Abstract. The future 5G mobile network technology is expected to offer significantly better performance than its predecessors. Improved data rates in conjunction with low latency is believed to enable technological revolutions such as self-driving cars. To achieve faster data rates, MIMO systems can be utilized. These systems enable the use of spatial filtering technique known as beamforming. Beamforming that is based on the preacquired channel matrix is computationally very demanding causing challenges in achieving low latency. By acquiring the channel matrix as efficiently as possible, we can facilitate this challenge. In this thesis we examined the implementation of channel estimation algorithm for beamforming with a digital signal processor specialized in vector computation. We present implementations for different antenna configurations based on three different approaches. The results show that the best performance is achieved by applying the algorithm according to the limitations given by the system and the processor architecture. Although the exploitation of the parallel architecture was proved to be challenging, the implementation of the algorithm would have benefitted from the greater amount of parallelism. The current parallel resources will be a challenge especially in the future as the size of antenna configurations is expected to grow.Keilanmuodostuksen tarvitseman kanavaestimointialgoritmin tehokas toteutus. Tiivistelmä. Tulevan viidennen sukupolven mobiiliverkkoteknologian odotetaan tarjoavan merkittävästi edeltäjäänsä parempaa suorituskykyä. Tämän suorituskyvyn tarjoamat suuret datanopeudet yhdistettynä pieneen latenssiin uskotaan mahdollistavan esimerkiksi itsestään ajavat autot. Suurempien datanopeuksien saavuttamiseksi voidaan hyödyntää monitiekanavassa käytettävää MIMO-systeemiä, joka mahdollistaa keilanmuodostuksena tunnetun spatiaalisen suodatusmenetelmän käytön. Etukäteen hankittuun kanavatilatietoon perustuva keilanmuodostus on laskennallisesti erittäin kallista. Tämä aiheuttaa haasteita verkon pienen latenssivaatimuksen saavuttamisessa. Tässä työssä tutkittiin keilanmuodostukselle tarkoitetun kanavaestimointialgoritmin tehokasta toteutusta hyödyntäen vektorilaskentaan erikoistunutta prosessoriarkkitehtuuria. Työssä esitellään kolmea eri lähestymistapaa hyödyntävät toteutukset eri kokoisille antennikonfiguraatioille. Tuloksista nähdään, että paras suorituskyky saavutetaan sovittamalla algoritmi järjestelmän ja arkkitehtuurin asettamien rajoitusten mukaisesti. Vaikka rinnakkaisarkkitehtuurin hyödyntäminen asetti omat haasteensa, olisi algoritmin toteutus hyötynyt suuremmasta rinnakkaisuuden määrästä. Nykyinen rinnakkaisuuden määrä tulee olemaan haaste erityisesti tulevaisuudessa, sillä antennikonfiguraatioiden koon odotetaan kasvavan