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.

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.

Abstract-Massive MIMO is expected to play a key role in coping with the predicted mobile-data traffic explosion. Indeed, in combination with small cells and TDD operation, it promises large throughputs per unit area with low latency. In this paper we focus on the problem of balancing the load across networks with massive MIMO base-stations (BSs). The need for load balancing arises from variations in the user population density and is more pronounced in small cells due to the large variability in coverage area. We consider methods for load balancing over networks with small and large massive MIMO BSs. As we show, the distinct operation and properties of massive MIMO enable practical resource-efficient load-balancing methods with nearoptimal performance.

Abstract-Dense large-scale antenna deployments are one of the most promising technologies for delivering very large throughputs per unit area in the downlink (DL) of cellular networks. We consider such a dense deployment involving a distributed system formed by multi-antenna remote radio head (RRH) units connected to the same fronthaul serving a geographical area. Knowledge of the DL channel between each active user and its nearby RRH antennas is most efficiently obtained at the RRHs via reciprocity based training, that is, by estimating a user's channel using uplink (UL) pilots transmitted by the user, and exploiting the UL/DL channel reciprocity.We consider aggressive pilot reuse across an RRH system, whereby a single pilot dimension is simultaneously assigned to multiple active users. We introduce a novel coded pilot approach, which allows each RRH unit to detect pilot collisions, i.e., when more than a single user in its proximity uses the same pilot dimensions. Thanks to the proposed coded pilot approach, pilot contamination can be substantially avoided. As shown, such strategy can yield densification benefits in the form of increased multiplexing gain per UL pilot dimension with respect to conventional reuse schemes and some recent approaches assigning pseudorandom pilot vectors to the active users.

No abstract