Advanced wireless communications using large numbers of transmit antennas and receive nodes

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. First, we propose practical open-loop and closed-loop training frameworks to reduce the overhead of the downlink training phase. We then discuss efficient CSI quantization techniques using a trellis search. The proposed CSI quantization techniques can be implemented with a complexity that only grows linearly with the number of transmit antennas while the performance is close to the optimal case. We also analyze distributed reception using a large number of geographically separated nodes, a scenario that may become popular with the emergence of the Internet of Things. For distributed reception, we first propose coded distributed diversity to minimize the symbol error probability at the fusion center when the transmitter is equipped with a single antenna. Then we develop efficient receivers at the fusion center using minimal processing overhead at the receive nodes when the transmitter with multiple transmit antennas sends multiple symbols simultaneously using spatial multiplexing

Full-Rate, Full-Diversity, Finite Feedback Space-Time Schemes with Minimum Feedback and Transmission Duration

In this paper a MIMO quasi static block fading channel with finite N-ary delay-free, noise-free feedback is considered. The transmitter uses a set of N Space-Time Block Codes (STBCs), one corresponding to each of the N possible feedback values, to encode and transmit information. The feedback function used at the receiver and the N component STBCs used at the transmitter together constitute a Finite Feedback Scheme (FFS). Although a number of FFSs are available in the literature that provably achieve full-diversity, there is no known universal criterion to determine whether a given arbitrary FFS achieves full-diversity or not. Further, all known full-diversity FFSs for T<N_t where N_t is the number of transmit antennas, have rate at the most 1. In this paper a universal necessary condition for any FFS to achieve full-diversity is given, using which the notion of Feedback-Transmission duration optimal (FT-Optimal) FFSs - schemes that use minimum amount of feedback N given the transmission duration T, and minimum transmission duration given the amount of feedback to achieve full-diversity - is introduced. When there is no feedback (N=1) an FT-optimal scheme consists of a single STBC with T=N_t, and the universal necessary condition reduces to the well known necessary and sufficient condition for an STBC to achieve full-diversity: every non-zero codeword difference matrix of the STBC must be of rank N_t. Also, a sufficient condition for full-diversity is given for the FFSs in which the component STBC with the largest minimum Euclidean distance is chosen. Using this sufficient condition full-rate (rate N_t) full-diversity FT-Optimal schemes are constructed for all (N_t,T,N) with NT=N_t. These are the first full-rate full-diversity FFSs reported in the literature for T<N_t. Simulation results show that the new schemes have the best error performance among all known FFSs.Comment: 12 pages, 5 figures, 1 tabl

Hybrid beamforming in mm-wave MIMO systems having a finite input alphabet

Recently, there has been significant research effort toward achieving high data rates in the millimeter wave bands by employing large antenna systems. These systems are considered to have only a fraction of the RF chains compared with the total number of antennas and employ analog phase shifters to steer the transmit and receive beams in addition to the conventional beamforming (BF)/combining invoked in the baseband domain. This scheme, which is popularly known as hybrid BF, has been extensively studied in the literature. To the best of our knowledge, all the existing schemes focus on obtaining the BF/combining matrices that maximize the system capacity computed using a Gaussian input alphabet. However, this choice of matrices may be suboptimal for practical systems, since they employ a finite input alphabet, such as quadrature amplitude modulation/phase-shift keying constellations. Hence, in this paper, we consider a hybrid BF/combining system operating with a finite input alphabet and optimize the analog as well as digital BF/combining matrices by maximizing the mutual information (MI). This is achieved by an iterative gradient ascent algorithm that exploits the relationship between the minimum mean-squared error and the MI. Furthermore, an iterative algorithm is proposed for designing a codebook for the analog and digital BF/combining matrices based on a vector quantization approach. Our simulation results demonstrate that the proposed gradient ascent algorithm achieves an ergodic rate improvement of up to 0.4 bits per channel use (bpcu) compared with the Gaussian input scenario. Furthermore, the gain in the ergodic rate achieved by employing the vector quantization-based codebook is about 0.5 bpcu compared with the Gaussian input scenari