A massive MIMO system entails a large number of base station antennas M serving a much smaller number of users. This leads to large gains in spectral and energy efficiency compared with other technologies. As the number of antennas M grows, the performance of such systems gets limited by pilot contamination interference. Large Scale Fading Precoding/Postcoding (LSFP) was proposed for mitigation of pilot contamination, and it was shown that in channels without spatial correlation (uncorrelated base station antennas) LSFP leads to large spectral-efficiency gains. It was recently proven that if a channel has spatial correlation, then one can use this correlation to drastically reduce the pilot contamination interference in the asymptotic regime as M tends to infinity. In this work, we analyze the performance of Uplink (UL) transmission of massive MIMO systems with finitely many antennas M for channels with spatial correlation. We extend the idea of LSFP to correlated channel models and derive SINR expressions that depend only on slow fading channel components for such systems with and without LSFP. These simple expressions lead us to simple algorithms for transmit power optimization. As a result, we obtain a multi-fold increase in data transmission rates.

A massive MIMO system entails a large number (tens or hundreds) of base station antennas serving a much smaller number of terminals. These systems demonstrate large gains in spectral and energy efficiency compared with the conventional MIMO technology. As the number of antennas grows, the performance of a massive MIMO system gets limited by the interference caused by pilot contamination. Ashikhmin and Marzetta proposed (under the name of Pilot Contamination Precoding) large scale fading precoding (LSFP) and large scale fading decoding (LSFD) based on limited cooperation between base stations. They showed that zero-forcing LSFP and LSFD eliminate pilot contamination entirely and lead to an infinite throughput as the number of antennas grows. In this paper, we focus on the uplink and show that even in the case of a finite number of base station antennas, LSFD yields a very large performance gain. In particular, one of our algorithms gives a more than 140 fold increase in the 5% outage data transmission rate! We show that the performance can be improved further by optimizing the transmission powers of the users. Finally, we present decentralized LSFD that requires limited cooperation only between neighboring cells.

Narrowband Internet of Things (NB-IoT) is a new radio access technology, recently standardized in 3GPP to enable support for IoT devices. NB-IoT offers a range of flexible deployment options and provides improved coverage and support for a massive number of devices within a cell. In this paper, we provide a detailed evaluation of the coverage performance of NBIoT and show that it achieves a coverage enhancement of up to 20 dB when compared with existing LTE technology.

In this paper, we study the downlink performance of a heterogeneous cellular network (HetNet) where both macro and small cells share the same spectrum and hence interfere with each other. We assume that the users are concentrated at certain areas in the cell, i.e., they form hotspots. While some of the hotspots are assumed to have a small cell in their vicinity, the others are directly served by the macrocell. Due to a relatively small area of each hotspot, the users lying in a particular hotspot appear to be almost co-located to the macrocells, which are typically deployed at some elevation. We assume a large number of antennas at the macrocell relative to the number of users simultaneously served. In this “massive MIMO” regime, the channel vectors become highly directional. We exploit this directionality in the channel vectors to obtain spatial blanking, i.e., concentrating transmission energy only in certain directions while creating transmission opportunities for the small cells lying in the other directions. In addition to this inherent interference suppression, we also develop three low-complexity interference coordination strategies: turn off small cells based on the amount of cross-tier interference they receive or cause to the scheduled macrocell hotspots; schedule hotspots such that treating interference as noise is approximately optimal for the resulting Gaussian interference channel; and offload some of the macrocell hotspots to nearby small cells to improve throughput fairness across all hotspots. For all these schemes, we study the relative merits and demerits of uniform deployment of small cells vs. deploying more small cells towards the cell center or the cell edge.

A Large Scale Antenna System (LSAS) entails a large number (tens or hundreds) of base station antennas serving a much smaller number of terminals, with large gains in spectral-efficiency and energy efficiency compared with conventional MIMO technology. As the number of antennas grows, the performance of an LSAS gets limited by pilot contamination, arising due to the use of same pilot sequences for channel estimation in neighboring cells. Recently A. Ashikhmin and T. Marzetta showed that using proper precoding/postcoding (PCP) and limited cooperation between cells, it is possible to eliminate pilot contamination entirely and get infinite throughput as M → ∞. In this paper, we focus on the uplink of an LSAS and show that even in the case of a finite number of base station antennas, PCP yields very significant performance gain in terms of data transmission rates. In particular, one of our algorithms gives a 140 fold increase in the 5% outage data transmission rates! We also show that the performance can be improved further by optimizing the transmission powers of the users, and present a simple decentralized algorithm in order to solve it.

Massive MIMO systems are well-suited for mm-Wave communications, as large arrays can be built with reasonable form factors, and the high array gains enable reasonable coverage even for outdoor communications. One of the main obstacles for using such systems in frequency-division duplex mode, namely, the high overhead for the feedback of channel state information (CSI) to the transmitter, can be mitigated by the recently proposed joint spatial division and multiplexing (JSDM) algorithm. In this paper, we analyze the performance of this algorithm in somerealisticpropagation channels that take into account the partial overlap of the angular spectra from different users, as well as the sparsity of mm-Wave channels. We formulate the problem of user grouping for two different objectives, namely, maximizing spatial multiplexing and maximizing total received power in a graph-theoretic framework. As the resulting problems are numerically difficult, we proposed (sub optimum) greedy algorithms as efficient solution methods. Numerical examples show that the different algorithms may be superior in different settings. We furthermore develop a new, “degenerate” version of JSDM that only requires average CSI at the transmitter and thus greatly reduces the computational burden. Evaluations in propagation channels obtained from ray tracing results, as well as inmeasuredoutdoor channels, show that this low-complexity version performs surprisingly well in mm-Wave channels.

We propose joint spatial division and multiplexing (JSDM), an approach to multiuser MIMO downlink that exploits the structure of the correlation of the channel vectors in order to allow for a large number of antennas at the base station while requiring reduced-dimensional channel state information at the transmitter (CSIT). JSDM achieves significant savings both in the downlink training and in the CSIT uplink feedback, thus making the use of large antenna arrays at the base station potentially suitable also for frequency division duplexing (FDD) systems, for which uplink/downlink channel reciprocity cannot be exploited. In the proposed scheme, the multiuser MIMO downlink precoder is obtained by concatenating a prebeamforming matrix, which depends only on the channel second-order statistics, with a classical multiuser precoder, based on the instantaneous knowledge of the resulting reduced dimensional “effective” channel matrix. We prove a simple condition under which JSDM incurs no loss of optimality with respect to the full CSIT case. For linear uniformly spaced arrays, we show that such condition is approached in the large number of antennas limit. For this case, we use Szego's asymptotic theory of Toeplitz matrices to show that a DFT-based prebeamforming matrix is near-optimal, requiring only coarse information about the users angles of arrival and angular spread. Finally, we extend these ideas to the case of a 2-D base station antenna array, with 3-D beamforming, including multiple beams in the elevation angle direction. We provide guidelines for the prebeamforming optimization and calculate the system spectral efficiency under proportional fairness and max-min fairness criteria, showing extremely attractive performance. Our numerical results are obtained via asymptotic random matrix theory, avoiding lengthy Monte Carlo simulations and providing accurate results for realistic (finite) number of antennas and users.

We study a two-tier macrocell/femtocell system where the macrocell base station is equipped with multiple antennas and makes use of multiuser MIMO (spatial multiplexing), and the femtocells are “cognitive”. In particular, we assume that the femtocells are aware of the locations of scheduled macrocell users on every time-frequency slot, so that they can make decisions on their transmission opportunities accordingly. Femtocell base stations are also equipped with multiple antennas. We propose a scheme where the macrocell downlink (macro-DL) is aligned with the femtocells uplink (femto-UL) and, Vice Versa, the macrocell uplink (macro-UL) is aligned with the femtocells downlink (femto-DL). Using a simple “interference temperature” power control in the macro-DL/femto-UL direction, and exploiting uplink/downlink duality and the Yates, Foschini and Miljanic distributed power control algorithm in the macro-UL/femto-DL direction, we can achieve an extremely attractive macro/femto throughput tradeoff region in both directions. We investigate the impact of multiuser MIMO spatial multiplexing in the macrocell under the proposed scheme, and find that large gains are achievable by letting the macrocell schedule groups of co-located users, such that the number of femtocells affected by the interference temperature power constraint is small.