Dilip Bethanabhotla

Optimal User-Cell Association for Massive MIMO Wireless Networks

Massive MIMO is one of the most promising approaches for coping with the predicted wireless data traffic explosion. Future deployment scenarios will involve dense heterogeneous networks, comprised of massive MIMO base stations with different powers, numbers of antennas and multiplexing gain capabilities, and possibly highly nonhomogeneous user density (hot-spots). In such dense irregularly deployed networks, it will be important to have mechanisms for associating users to base stations so that the available wireless infrastructure is efficiently used. In this paper, we consider the optimal user-cell association problem for massive MIMO heterogeneous networks and illustrate how massive MIMO can also provide nontrivial advantages at the system level. Unlike previous treatments that rely on integer program problem formulations and their convex relaxations, the user-cell association problem is formulated directly as a convex network utility maximization and solved efficiently by a centralized subgradient algorithm. As we show, the globally optimal solution is physically realizable, in that there exists a sequence of integer-valued associations approaching arbitrarily closely the optimal fractional association. We also consider simple decentralized user-centric association schemes, where each user individually and selfishly connects to the base station with the highest promised throughput. Such user-centric schemes where users make local association decisions in a probabilistic manner can be viewed as games and are known to converge to Nash equilibria. Surprisingly, as we show, under certain conditions, the globally optimal solution is close to these Nash equilibria. Such decentralized approaches are, therefore, attractive not only for their simplicity, but also because they operate near the system social optimum. Our theoretical results are confirmed by extensive simulations with realistic LTE-like network parameters.

User association and load balancing for cellular massive MIMO

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 near-optimal performance.

Caching Eliminates the Wireless Bottleneck in Video Aware Wireless Networks

Wireless video is the main driver for rapid growth in cellular data traffic. Traditional methods for network capacity increase are very costly and do not exploit the unique features of video, especially asynchronous content reuse. In this paper we give an overview of our work that proposed and detailed a new transmission paradigm exploiting content reuse and the widespread availability of low-cost storage. Our network structure uses caching in helper stations (femtocaching) and/or devices, combined with highly spectrally efficient short-range communications to deliver video files. For femtocaching, we develop optimum storage schemes and dynamic streaming policies that optimize video quality. For caching on devices, combined with device-to-device (D2D) communications, we show that communications within clusters of mobile stations should be used; the cluster size can be adjusted to optimize the tradeoff between frequency reuse and the probability that a device finds a desired file cached by another device in the same cluster. In many situations the network throughput increases linearly with the number of users, and the tradeoff between throughput and outage is better than in traditional base-station centric systems. Simulation results with realistic numbers of users and channel conditions show that network throughput can be increased by two orders of magnitude compared to conventional schemes.

A Control-Theoretic Approach to Adaptive Video Streaming in Dense Wireless Networks

Recently, the way people consume video content has been undergoing a dramatic change. Plain TV sets, that have been the center of home entertainment for a long time, are losing ground to hybrid TVs, PCs, game consoles, and, more recently, mobile devices such as tablets and smartphones. The new predominant paradigm is: watch what I want, when I want, and where I want. The challenges of this shift are manifold. On the one hand, broadcast technologies such as DVB-T/C/S need to be extended or replaced by mechanisms supporting asynchronous viewing, such as IPTV and video streaming over best-effort networks, while remaining scalable to millions of users. On the other hand, the dramatic increase of wireless data traffic begins to stretch the capabilities of the existing wireless infrastructure to its limits. Finally, there is a challenge to video streaming technologies to cope with a high heterogeneity of end-user devices and dynamically changing network conditions, in particular in wireless and mobile networks. In the present work, our goal is to design an efficient system that supports a high number of unicast streaming sessions in a dense wireless access network. We address this goal by jointly considering the two problems of wireless transmission scheduling and video quality adaptation, using techniques inspired by the robustness and simplicity of proportional-integral-derivative (PID) controllers. We show that the control-theoretic approach allows to efficiently utilize available wireless resources, providing high quality of experience (QoE) to a large number of users.

Utility optimal scheduling and admission control for adaptive video streaming in small cell networks

We consider the jointly optimal design of a transmission scheduling and admission control policy for adaptive video streaming over small cell networks. We formulate the problem as a dynamic network utility maximization and observe that it naturally decomposes into two subproblems: admission control and transmission scheduling. The resulting algorithms are simple and suitable for distributed implementation. The admission control decisions involve each user choosing the quality of the video chunk asked for download, based on the network congestion in its neighborhood. This form of admission control is compatible with the current video streaming technology based on the DASH protocol over TCP connections. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions for a small-cell network.

Adaptive video streaming for device-to-device mobile platforms

This demo abstract describes an initial design of a new adaptive video streaming protocol for device-to-device WiFi-based mobile platforms and its software implementation. For the demonstration, two mobile servers and two mobile users will be deployed verifying that our device-to-device adaptive video streaming implementation works with desirable user experience.

Joint transmission scheduling and congestion control for adaptive streaming in wireless device-to-device networks

We consider the jointly optimal design of a transmission scheduling and admission control policy for adaptive streaming over wireless device-to-device networks. We formulate the problem as a dynamic network utility maximization and observe that it naturally decomposes into two subproblems: admission control and transmission scheduling. The resulting algorithms are simple and suitable for distributed implementation. The admission control decisions involve each user choosing the quality of the video chunk asked for download, based on the network congestion in its neighborhood. This form of admission control is compatible with the current video streaming technology based on the DASH protocol over TCP connections. We also consider a mechanism for dropping bits from the transmission queues in order to obtain deterministic bounds on the queueing delays, which determine the number of video chunks that should be pre-fetched in order to guarantee smooth playback without interruptions.

Near-optimal user-cell association schemes for real-world networks

The growing demand for wireless bandwidth makes WiFi deployments denser and pushes cellular networks to adopt a denser, small-cell architecture. In such dense environments, users have multiple options when it comes to selecting an access point (AP) to associate with the network, and the user-cell association scheme that is used has a large impact on overall network performance. Industry is currently using simplistic, sub-optimal approaches to select an AP for each user, while academia has produced optimal schemes under unrealistic assumptions, which prevents them from ever being used in practice. In this paper we design high-performance user-cell association schemes which can be deployed in real-world networks. The performance of the proposed schemes is shown to be near-optimal via both formal performance bounds under realistic assumptions, and simulation results under real-world setups.

Performance of a Fast, Distributed Multiple Access Based Relay Selection Algorithm Under Imperfect Statistical Knowledge

Cooperative wireless systems can exploit spatial diversity by opportunistically selecting the best relay to forward data to a destination. However, determining the best relay is a challenging task and requires a selection algorithm because the relays are geographically separated and only have local channel knowledge. Selecting the best relay is equivalent to finding the relay with the largest metric, where each relay computes its metric using local channel knowledge. We analyze the performance of a fast, distributed, and scalable multiple access based selection algorithm when it assumes incorrect values for two fundamental parameters that it requires to operate efficiently - the number of available relays and the cumulative distribution function (CDF) of the metrics. Such imperfect knowledge will invariably arise in practice. We develop new expressions for the time required to select the best relay as a function of the assumed and actual parameters. We show that imperfect knowledge can significantly slow down the selection algorithm. Further, in a system that uses its observations to update its CDF estimate, we determine the minimum number of observations required to limit the performance degradation. We also develop a minimax formulation that makes the algorithm robust to uncertainties in the number of relays in the system.