About

Randall Berry is a Professor and Chair of Electrical and Computer Engineering at Northwestern University. His research focuses on resource allocation problems in networked systems, including communication networks and social networks. He employs mathematical models to gain insights into these systems, utilizing tools from stochastic modeling, optimization, economics, and algorithms. His specific areas of interest include developing distributed resource allocation techniques for wireless networks, dynamic spectrum sharing, wireless spectrum policy, understanding the role of incentives in network security, and modeling learning and adoption in social networks. Dr. Berry's work contributes to advancing the understanding and development of network systems through rigorous analytical approaches.

Research topics

• Computer Science
• Business
• Artificial Intelligence
• Computer Security
• Physics
• Telecommunications
• Industrial organization

Selected publications

• Market Impacts of Pooling Intermittent Spectrum

  2024 · 5 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Business

  Temporal sharing of spectrum as in the CBRS system provides wireless service providers (SPs) with spectrum that is intermittently available. This intermittency can decrease the value of the spectrum to a SP. In this paper we consider a setting where a SP can pool multiple intermittent bands of spectrum with independent availability. We find that pooling can achieve a higher spectrum efficiency in terms of the congestion incurred by users compared to using a single intermittent band (with the same total bandwidth). We show that this efficiency gain can be achieved with a relatively small pool of bands and it quickly converges to the optimal case as the number of bands increases. We also observe that pooled intermittency has a lesser impact on bids if spectrum is auctioned.

  DOI

• Market Impacts of Relaxed Incumbent Protection in Spectrum Sharing

  2022 IEEE International Conference on Communications Workshops (ICC Workshops) · 2024 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Security
  • Business

  Spectrum sharing approaches such as that adopted in the Citizens Broadband Radio Service (CBRS) provide strong protection to incumbent users of the spectrum. This can lower the value of such spectrum for commercial service providers (SPs). This paper considers the impact of relaxing this protection on the market for secondary spectrum services. We assume that SPs must reduce their traffic when the incumbent is present. We find that consumers always benefit from relaxing incumbent protection but SPs' revenue and social welfare exhibit subtle behavior. Depending on the extent of relaxation, the market might respond negatively implying that regulators need to carefully choose such policies.

  DOI

• Faithful Edge Federated Learning: Scalability and Privacy

  IEEE Journal on Selected Areas in Communications · 2021 · 58 citations

  • Computer Science
  • Computer Science
  • Computer Security

  Federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents’ incentives to voluntarily participate and obediently follow traditional federated learning algorithms. To be more specific, our analysis reveals that agents with less typical data distributions and relatively more samples are more likely to opt out of or tamper with federated learning algorithms. To this end, we formulate the first faithful implementation problem of federated learning and design two faithful federated learning mechanisms which satisfy economic properties, scalability, and privacy. First, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Faithful Federated Learning (FFL) mechanism</i> which approximates the Vickrey–Clarke–Groves (VCG) payments via an incremental computation. We show that it achieves (probably approximate) optimality, faithful implementation, voluntary participation, and some other economic properties (such as budget balance). Further, the time complexity in the number of agents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(\log (K))$ </tex-math></inline-formula> . Second, by partitioning agents into several clusters, we present a scalable VCG mechanism approximation. We further design a scalable and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Differentially Private FFL (DP-FFL) mechanism</i> , the first differentially private faithful mechanism, that maintains the economic properties. Our DP-FFL mechanism enables one to make three-way performance tradeoffs among privacy, the iterations needed, and payment accuracy loss.

  DOI

• When to Be Agile: Ratings and Version Updates in Mobile Apps

  Management Science · 2021 · 32 citations

  • Computer Science
  • Computer Science
  • Marketing

  Lean and agile models of product development organize the flexible capacity to rapidly update individual products in response to customer feedback. Although agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, and the apps’ customer ratings. We develop a structural model estimating the dependence of product versioning on (a) market feedback in the form of customer ratings against (b) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when they are able to exploit scale economies from coordinating development over a portfolio of apps. Agile operations increase firm payoffs by margins of 20% to 80%, and interestingly, partial agility is often sufficient to capture the bulk of these returns. Finally, turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories’ average user satisfaction rates by as much as 22%). This paper was accepted by Jayashankar Swaminathan, operations management.

  PublisherDOI

• Optimal and Quantized Mechanism Design for Fresh Data Acquisition

  IEEE Journal on Selected Areas in Communications · 2021 · 24 citations

  • Computer Science
  • Computer Science
  • Mathematical optimization

  The proliferation of real-time applications has spurred much interest in data freshness, captured by the age-of-information (AoI) metric. When strategic data sources have private market information, a fundamental economic challenge is how to incentivize them to acquire fresh data and optimize the age-related performance. In this work, we consider an information update system in which a destination acquires, and pays for, fresh data updates from multiple sources. The destination incurs an age-related cost, modeled as a general increasing function of the AoI. Each source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. The destination decides on the price of updates, when to get them, and who should generate them, based on the sources' reported sampling costs. We show that a benchmark that naively trusts the sources' reports can lead to an arbitrarily bad outcome compared to the case where sources truthfully report. To tackle this issue, we design an optimal (economic) mechanism for timely information acquisition following Myerson's seminal work. To this end, our proposed optimal mechanism minimizes the sum of the destination's age-related cost and its payment to the sources, while ensuring that the sources truthfully report their private information and will voluntarily participate in the mechanism. However, finding the optimal mechanisms may suffer from prohibitively expensive computational overheads as it involves solving a nonlinear infinite-dimensional optimization problem. We further propose a quantized version of the optimal mechanism that achieves asymptotic optimality, maintains the other economic properties, and enables one to tradeoff between optimality and computational overheads. Our analytical and numerical studies show that (i) both the optimal and quantized mechanisms can lead to an unbounded benefit under some distributions of the source costs compared against a benchmark; (ii) the optimal and quantized mechanisms are most beneficial when there are few sources with heterogeneous sampling costs.

  DOI

• The Impact of Unlicensed Access on Small-Cell Resource Allocation

  IEEE Journal on Selected Areas in Communications · 2020 · 13 citations

  • Computer Science
  • Computer Science
  • Computer network

  Small-cells in licensed spectrum and unlicensed access via Wi-Fi are two commonly used options to reduce the demand for conventional macro-cellular networks and to provide expanded wireless services to low mobility users. The mix of these technologies depends on both the decisions made by wireless service providers (SPs) that seek to maximize revenue, and the allocation of licensed and unlicensed spectrum by regulators. In this paper, we study these interactions and consider heterogeneous cellular networks together with unlicensed access. Both a single monopoly SP and multiple competing SPs are investigated. The SPs split any available licensed spectrum into two separate bands for macro- and small-cells, which are then used to serve two types of users: mobile and fixed. Mobile users must be served by macro-cells only, whereas fixed users can be served by either macro- or small-cells, or alternatively by unlicensed access service. While the providers charge a (different) price per unit rate for licensed access services (macro- or small-cell), unlicensed access is free. We formulate a sequential game in which the users choose a service that yields the highest payoff, and the providers allocate bandwidth across macro-/small-cells. In general, the competition from unlicensed access results in inefficient (albeit unique) market equilibria, and in many cases all or some SPs allocate no resources to small-cell deployment. We conclude by showing how our framework can also be used to optimize the fraction of unlicensed spectrum when new bandwidth becomes available.

  PublisherDOI

• Pricing, Bandwidth Allocation, and Service Competition in Heterogeneous Wireless Networks

  IEEE/ACM Transactions on Networking · 2020 · 15 citations

  • Computer Science
  • Computer Science
  • Computer network

  Small-cells deployed in licensed spectrum can expand wireless service to low mobility users, which potentially reduces the demand for macro-cellular networks with wide-area coverage. Introducing such heterogeneity also makes network resource allocation more complicated. To understand these challenges and tradeoffs we present a two-tier heterogeneous wireless network model with two types of users: mobile users that can only connect to macro-cells; and fixed users that can associate with either macro-cells or small-cells. We study pricing strategies and bandwidth allocation across macro- and small-cells, assuming both monopoly and competitive Service Providers (SPs). For a monopoly SP, we characterize the revenue-maximizing prices and bandwidth allocations. We then consider a competitive scenario, and we show the existence of a unique Nash equilibrium. The possible Nash equilibria for different system parameters are sorted into four categories corresponding to whether or not different SPs assign bandwidth to the macro- and/or small-cells. We also study the allocations that maximize social welfare. For the competitive scenario, we characterize the conditions under which the optimal social welfare is obtained in equilibria as the number of SPs tends to infinity. Case study examples and numerical results illustrate the corresponding pricing and bandwidth allocations.

  PublisherDOI

Frequent coauthors

• Kangle Mu

  2 shared

• Emanuele Viterbo

  1 shared

• Paul H. Siegel

  University of California, San Diego

  1 shared

• Michelle Effros

  California Institute of Technology

  1 shared

• Ian F. B Lake

  1 shared

• Max H. M. Costa

  Chinese University of Hong Kong

  1 shared

• Erchin Serpedin

  1 shared

• Second Vice President

  1 shared

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