Time-Division Duplexing (TDD) allows to estimate the downlink channels for an arbitrarily large number of base station antennas from a finite number of orthogonal pilot signals in the uplink, by exploiting channel reciprocity. Therefore, while the number of users per cell served in any time-frequency channel coherence block is necessarily limited by the number of pilot sequence dimensions available, the number of base station antennas can be made as large as desired. Based on this observation, a recently proposed very simple "Massive MIMO" scheme was shown to achieve unprecedented spectral efficiency in realistic conditions of user spatial distribution, distance-dependent pathloss and channel coherence time and bandwidth.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 timefrequency 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).

The use of a very large number of antennas at the base station sites (referred to as Massive MIMO) is one of the most promising approaches to cope with the predicted wireless data traffic explosion.Following the current wireless technology trend of moving to higher frequency bands and denser cell deployments, a large number of antennas can be implemented within a small form factor even in smallcell base stations. Envisioned scenarios involve heterogeneous networks (comprised of base stations with different powers, numbers of antennas and multiplexing gain capabilities) serving user traffic with often highly non-homogeneous user density. A key system optimization problem in such networks consists of associating users to base stations such that congestion is avoided and the available wireless infrastructure is efficiently used.In this paper, we consider the user-cell association problem for a massive MIMO heterogeneous network. We formulate the problem as a network utility maximization, where the network utility is a function of the users' long-term average rates (per-user throughputs). Under a massive-MIMO specific system model, we show that optimizing the activity fractions between user-BS pairs problem is a convex problem that can be solved efficiently by centralized sub-gradient algorithms. Furthermore, we show that such a solution is physically realizable, in the sense that there exists a scheduling sequence approaching arbitrarily closely the optimal activity fractions.We also consider a decentralized user-centric scheme, where each user has a positive probability to switch cell association if the utility expected from a different base station is higher than the utility achieved from the currently associated one. We formulate a non-cooperative association game and show that its pure-strategy Nash equilibria must be close to the global optimum of the centralized problem.We also show that, under certain technical conditions that we refer to as heavy-loaded network, if the centralized global optimum consists of a unique association (i.e., no user has positive activity fraction to more than one base station), then this association is a pure-strategy Nash equilibrium of the corresponding user-centric association game. Based on previously known results, we also have that the proposed usercentric decentralized probabilistic scheme converges to a pure-strategy Nash equilibrium with probability 1, for the practically relevant cases of proportional fairness and max-min fairness utility functions. Hence, our user-centric algorithm is attractive not only for its simplicity and fully decentralized implementation, but also because it operates near the system social optimum.

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.

Large-scale distributed Multiuser MIMO (MU-MIMO) is a promising wireless network architecture that combines the advantages of "massive MIMO" and "small cells." It consists of several Access Points (APs) connected to a central server via a wired backhaul network and acting as a large distributed antenna system. We focus on the downlink, which is both more demanding in terms of traffic and more challenging in terms of implementation than the uplink. In order to enable multiuser joint precoding of the downlink signals, channel state information at the transmitter side is required. We consider Time Division Duplex (TDD), where the downlink channels can be learned from the user uplink pilot signals, thanks to channel reciprocity. Furthermore, coherent multiuser joint precoding is possible only if the APs maintain a sufficiently accurate relative timing and phase synchronization.AP synchronization and TDD reciprocity calibration are two key problems to be solved in order to enable distributed MU-MIMO downlink. In this paper, we propose novel over-the-air synchronization and calibration protocols that scale well with the network size. The proposed schemes can be applied to networks formed by a large number of APs, each of which is driven by an inexpensive 802.11grade clock and has a standard RF front-end, not explicitly designed to be reciprocal. Our protocols can incorporate, as a building block, any suitable timing and frequency estimator. Here we revisit the problem of joint ML timing and frequency estimation and use the corresponding Cramer-Rao bound to evaluate the performance of the synchronization protocol. Overall, the proposed synchronization and calibration schemes are shown to achieve sufficient accuracy for satisfactory distributed MU-MIMO performance.

Signal estimation from a sequential encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an information-bearing signal from low-bandwidth digitized information received from remote sensors, and may or may not broadcast feedback information to the sensors. We demonstrate that the use of an appropriately designed and often easily implemented additive control input before signal quantization at the sensor can significantly enhance overall system performance. In particular, we develop efficient estimators in conjunction with optimized random, deterministic, and feedback-based control inputs, resulting in a hierarchy of systems that trade performance for complexity.

We compare the downlink throughput of various cellular architectures with multi-antenna base stations and multiple single-antenna users per cell, by considering a number of inherent physical layer issues such as path-loss and time and frequency selective fading. In particular, we focus on Multiuser MIMO (MU-MIMO) downlink techniques that require channel state information at the transmitter (CSIT). Our analysis takes explicit account of the cost of CSIT estimation and illuminates the tradeoffs between CSIT, estimation error, and system resource dedicated to training. This tradeoff shows that the number of antennas that can be jointly coordinated (either on the same base station or across multiple base stations) is intrinsically limited not just by "external factors," such as complexity and rate of the backbone wired network, but by the inherent time and frequency variability of the fading channels. Our analysis, in agreement with a number of recent simulation results, shows that conventional MU-MIMO cellular architectures may outperform schemes based on coordinated transmission from base stations (referred to as Network MIMO schemes, NW-MIMO), at the negligible cost of a few extra antennas per station. In light of these results, it appears that the inherent bottleneck of NW-MIMO systems is not the backbone network (which here is assumed ideal with infinite capacity) but the intrinsic dimensional limitation of estimating the channels.

The transmitter optimization (i.e., steering vectors and power allocation) for a MISO Broadcast Channel (MISO-BC) subject to general linear constraints is considered. Such constraints include, as special cases, the sum power, the per-antenna or per-group-of-antennas power, and "forbidden interference direction" constraints. We consider both the optimal dirty-paper coding and the simple suboptimal linear zero-forcing beamforming strategies, and provide numerically efficient algorithms that solve the problem in its most general form. As an application, we consider a multi-cell scenario with partial cell cooperation, where each cell optimizes its precoder by taking into account interference constraints on specific users in adjacent cells. The effectiveness of the proposed methods is evaluated in a simple system scenario including two adjacent cells, under different fairness criteria that emphasize the bottleneck role of users near the cell "boundary". Our results show that "active" Inter-Cell Interference (ICI) mitigation outperforms the conventional "static" ICI mitigation based on fractional frequency reuse. Index TermsMISO broadcast channel, convex optimization, dirty paper coding, zero forcing beamforming, multicell scheduling, inter-cell interference mitigation.

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