Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Millimeter Wave Cellular Networks: A MAC Layer Perspective

The millimeter wave (mmWave) frequency band is seen as a key enabler of multi-gigabit wireless access in future cellular networks. In order to overcome the propagation challenges, mmWave systems use a large number of antenna elements both at the base station and at the user equipment, which lead to high directivity gains, fully-directional communications, and possible noise-limited operations. The fundamental differences between mmWave networks and traditional ones challenge the classical design constraints, objectives, and available degrees of freedom. This paper addresses the implications that highly directional communication has on the design of an efficient medium access control (MAC) layer. The paper discusses key MAC layer issues, such as synchronization, random access, handover, channelization, interference management, scheduling, and association. The paper provides an integrated view on MAC layer issues for cellular networks, identifies new challenges and tradeoffs, and provides novel insights and solution approaches.Comment: 21 pages, 9 figures, 2 tables, to appear in IEEE Transactions on Communication

Programmable stochastic processors

As traditional approaches for reducing power in microprocessors are being exhausted, extreme power challenges call for unconventional approaches to power reduction. Recent research has shown substantial promise for application-specific stochastic computing, i.e., computing that exploits application error tolerance to enable careful relaxation of correctness guarantees provided by hardware in order to reduce power. This dissertation explores the feasibility, challenges, and potential benefits of stochastic computing in the context of programmable general purpose processors. Specifically, the dissertation describes design-level techniques that minimize the power of a processor for a non-zero error rate or allow a processor to fail gracefully when operated over a range of non-zero error rates. It presents microarchitectural design principles that allow a processor to trade off reliability and energy more efficiently to minimize energy when exploiting error resilience. It demonstrates the benefit of using compiler optimizations that optimize a binary to enable more energy savings when operating at a non-zero error rate. It also demonstrates significant benefits for a programmable stochastic processor prototype that improves energy efficiency by carefully relaxing correctness and exposing errors in applications running on a commodity processor. This dissertation on programmable stochastic processors conclusively shows that the architecture and design of processors and applications should be approached differently in scenarios where errors are allowed to be exposed from the hardware to higher levels of the compute stack. Significant energy benefits are demonstrated for design-, architecture-, compiler-, and application-level optimizations for general purpose programmable stochastic processors

Convex Optimization for Big Data

This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques like first-order methods and randomization for scalability, and survey the important role of parallel and distributed computation. The new Big Data algorithms are based on surprisingly simple principles and attain staggering accelerations even on classical problems.Comment: 23 pages, 4 figurs, 8 algorithm