Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

An Efficient Bandit Algorithm for Realtime Multivariate Optimization

Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Such optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions. Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21% conversion increase compared to the median layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.Comment: KDD'17 Audience Appreciation Awar

Law Firm Selection and the Value of Transactional Lawyering

Following the contraction in demand for law firms’ services during the Great Recession, “Big Law” was widely diagnosed as suffering from several maladies that would spell its ultimate demise, including excessive fees, excessive size, increased competition from in-house counsel, the commoditization of legal work, and the decline in demand for “relationship firms.” While each of these market pressures is only too real for certain segments of the law-firm population, their threat to the most elite U.S. law firms has been largely misunderstood. Even as many firms reduce their fees and contract in size, we should expect certain firms to continue to charge more and grow bigger. The current prescriptions for fixing Big Law fail to recognize that the top-tier firms within the group serve a unique market function. Focusing on a particular type of legal work – major corporate transactions – this Article proposes a novel theory of the value created by elite law firms: their private information about “market” deal terms, acquired through repeated exposure to the same types of transactions, provides clients with a significant bargaining advantage in deal negotiations. By aggregating expertise in the ever-changing and ever-increasing set of deal terms for certain transactions, law firms help their clients price such terms more accurately and thereby maximize their surplus from the deal. This pricing function – traditionally thought to be limited to investment banks – is one that cannot be replicated or subsumed by in-house counsel, other service providers, or commoditized contracts