Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio

Non-iterative downlink training sequence design based on sum rate maximization in FDD massive MIMO systems

This paper considers the problem of downlink (DL) training sequence design with limited coherence time for frequency division duplex (FDD) massive MIMO systems in a general scenario of single-stage precoding and distinct spatial correlations between users. To this end, a computationally feasible solution for designing the DL training sequences is proposed using the principle of linear superposition of sequences constructed from the users' channel covariance matrices. Based on the non-iterative superposition training structure and the P-degrees of freedom (P-DoF) channel model, a novel closed-form solution for the optimum training sequence length that maximizes the DL achievable sum rate is provided for the eigenbeamforming (BF) precoder. Additionally, a simplified analysis that characterizes the sum rate performance of the BF and regularized zero forcing (RZF) precoders in closed-form is developed based on the method of random matrix theory and the P-DoF channel model. The results show that the superposition training sequences achieve almost the same rate performances as state-of-the-art training sequence designs. The analysis of the complexity results demonstrates that more than four orders-of-magnitude reduction in the computational complexity is achieved using the superposition training design, which signifies the feasibility of this approach for practical implementations compared with state-of-the-art iterative algorithms for DL training designs. Importantly, the results indicate that the analytical solution for the optimum training sequence length with the P-DoF channel model can be effectively used with high accuracy to predict the sum rate performance in the more realistic one ring (OR) channel model, and thus, near optimal solutions can be readily obtained without resorting to computationally intensive optimization techniques

Pilot Contamination and Mitigation Techniques in Massive MIMO Systems

A multi-antenna base station (BS) can spatially multiplex a few terminals over the same bandwidth, a technique known as multi-user, multiple-input multiple-output (MU-MIMO). A new idea in cellular MU-MIMO is the use of a large excess of BS antennas to serve several single-antenna terminals simultaneously. This so-called "massive MIMO" promises attractive gains in spectral efficiency with time-division duplex operation. Within a cell, the BS estimates the channel from mutually orthogonal reverse-link pilot sequences to formulate a receiver for the reverse link and (assuming reciprocity) a precoder for the forward link. The channel coherence is typically constrained in time as well as frequency, leading to a trade-off between the resources spent on pilots and those available for data symbols. This pilot overhead can be reduced by reusing pilot sequences in nearby cells, however this potentially introduces interference in the channel estimation phase, the so-called "pilot contamination" effect. In this thesis, we study the impact of pilot contamination in realistic environments and investigate schemes to mitigate it. We evaluate the mean squared error (MSE) of channel estimates in case of a plain-vanilla least-squares (LS) estimator and a minimum MSE (MMSE) estimator that exploits prior knowledge of second-order channel statistics. Next, we introduce a pilot open-loop power control (pilot OLPC) scheme to improve the SINR-fairness of received pilot signals at the BS. We evaluate the effect of relaxing the pilot reuse factor and also implement a soft pilot reuse (SPR) scheme to distribute pilot sequences efficiently. To study the trade-off between pilot and data symbols, we evaluate the achievable rate in forward link with maximum-ratio and zero-forcing precoding at the BS. We evaluate an inter-cell coordination scheme that exploits prior knowledge of all cross-channel covariance matrices to reuse pilots among spatially well-separated terminals. We simulate a 21-cell MU-MIMO setup with up to 100-antenna BSs and up to 24 single-antenna terminals per cell in an outdoor urban macro environment. We find that pilot reuse 1 causes severe impairment of the channel estimates, which can be improved with pilot OLPC. Pilot reuse 1/3 effectively mitigates pilot contamination, and can improve the achievable rate in the forward link. SPR also mitigates contamination but with a smaller increase in pilot overhead. Inter-cell coordinated pilot allocation, implemented using a greedy approach, provides gains over random allocation only for the initial few pilots. In general, maximum ratio precoding is more robust against pilot contamination than zero-forcing.A multi-antenna base station (BS) can be used to improve cellular communication performance. The signal at each antenna can be designed in a way that it increases received energy at the desired terminals, and attenuates it at other locations (reducing interference). This technique can be used to serve several terminals over the same time and frequency using independent data streams, known as multi-user MIMO (MU-MIMO). In this thesis, we investigate MU-MIMO approach for very large BS antenna arrays, also called massive MIMO. The performance of such systems depends critically on the quality of channel estimates the BS. We simulate realistic channel conditions in a multi-cell setup, which gives rise to interference during channel estimation. We evaluate the system performance in terms of quality of BS channel estimates and the achievable data rate within a cell. We evaluate different techniques for channel estimation, and for generating data streams from the BS to the terminals. Next, we evaluate schemes to improve the channel estimation. We conclude by noting the trade-offs involved in the various schemes and the conditions under which certain schemes might provide performance improvements

Channel Estimation in Multi-user Massive MIMO Systems by Expectation Propagation based Algorithms

Massive multiple input multiple output (MIMO) technology uses large antenna arrays with tens or hundreds of antennas at the base station (BS) to achieve high spectral efficiency, high diversity, and high capacity. These benefits, however, rely on obtaining accurate channel state information (CSI) at the receiver for both uplink and downlink channels. Traditionally, pilot sequences are transmitted and used at the receiver to estimate the CSI. Since the length of the pilot sequences scale with the number of transmit antennas, for massive MIMO systems downlink channel estimation requires long pilot sequences resulting in reduced spectral efficiency and the so-called pilot contamination due to sharing of the pilots in adjacent cells. In this dissertation we first review the problem of channel estimation in massive MIMO systems. Next, we study the problem of semi-blind channel estimation in the uplink in the case of spatially correlated time-varying channels. The proposed method uses the transmitted data symbols as virtual pilots to enhance channel estimation. An expectation propagation (EP) algorithm is developed to iteratively approximate the joint a posterior distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. The distribution is then used for direct estimation of the channel matrix and detection of the data symbols. A modified version of Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize our algorithm. Simulation results demonstrate that channel estimation error and the symbol error rate of the proposed algorithm improve with the increase in the number of BS antennas or the number of data symbols in the transmitted frame. Moreover, the proposed algorithms can mitigate the effects of pilot contamination as well as time-variations of the channel. Next, we study the problem of downlink channel estimation in multi-user massive MIMO systems. Our approach is based on Bayesian compressive sensing in which the clustered sparse structure of the channel in the angular domain is exploited to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An EP algorithm is developed to approximate the intractable joint distribution on the sparse vector and its support with a distribution from an exponential family. This distribution is then used for direct estimation of the channel. The EP algorithm requires the model parameters which are unknown. We estimate these parameters using the expectation maximization (EM) algorithm. Simulation results show that the proposed combination of EM and EP referred to as EM-EP algorithm outperforms several recently-proposed algorithms in the literature