John Tadrous

Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

This paper introduces the novel concept of proactive resource allocation for wireless networks, through which the predictability of user behavior is exploited to balance the wireless traffic over time, and significantly reduces the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which smart wireless devices are assumed to predict the arrival of new requests and submit them to the network time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases with the prediction duration . Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed light on multicasting with predictable demands and show that proactive multicast networks can achieve a significantly higher diversity gain that scales superlinearly with . Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive wireless resource framework.

Admission and Power Control for Spectrum Sharing Cognitive Radio Networks

We investigate the problem of admission and power control considering a scenario where licensed, or primary, users and cognitive radios, or secondary users, are transmitting concurrently over the same band. The primary users share a common receiver and the interference on this receiver from secondary users should be strictly limited to a certain level. Each secondary link is assumed to have a minimum quality of service (QoS) requirement that should be satisfied together with the interference limit constraint, otherwise the secondary link is not admitted. Under those constraints, admission and power control for secondary users are investigated for two main optimization objectives. First, we maximize the number of admitted secondary links. Second, we maximize the sum throughput of the admitted secondary links. The first problem is NP-hard, hence we provide a distributed close-to-optimal solution based on local measurements at each user and a limited amount of signaling. For the second problem, which is non-convex, we propose a suboptimal algorithm based on sequential geometric programming. The proposed algorithms are compared with previously related work to demonstrate their relative efficiency in terms of outage probability, complexity and achievable throughput.

On Optimal Proactive Caching for Mobile Networks With Demand Uncertainties

Mobile data users are known to possess predictable characteristics both in their interests and activity patterns. Yet, their service is predominantly performed, especially at the wireless edges, “reactively” at the time of request, typically when the network is under heavy traffic load. This strategy incurs excessive costs to the service providers to sustain on-time (or delay-intolerant) delivery of data content, while their resources are left underutilized during the light-loaded hours. This motivates us in this work to study the problem of optimal “proactive” caching whereby, future delay-intolerant data demands can be served within a given prediction window ahead of their actual time-of-arrival to minimize service costs. To that end, we first establish fundamental bounds on the minimum possible cost achievable by any proactive policy, as a function of the prediction uncertainties. These bounds provide interesting insights on the impact of uncertainty on the maximum achievable proactive gains. We then propose specific proactive caching strategies, both for uniform and fluctuating demand patterns, that are asymptotically-optimal in the limit as the prediction window size grows while the prediction uncertainties remain fixed. We further establish the exponential convergence rate characteristics of our proposed solutions to the optimal, revealing close-to-optimal performance characteristics of our designs even with small prediction windows. Also, proactive design is contrasted with its reactive and delay-tolerant counter-parts to obtain interesting results on the unavoidable costs of uncertainty and the potentially remarkable gains of proactive operation.

Proactive resource allocation: Turning predictable behavior into spectral gain

This paper introduces the novel concept of proactive resource allocation in which the predictability of user behavior is exploited to balance the wireless traffic over time, and hence, significantly reduce the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which the smart wireless devices are assumed to predict the arrival of new requests and submit them to the network T time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases linearly with the prediction duration T. This model is then generalized to incorporate the effect of prediction errors and the randomness in the prediction lookahead time T. Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by the primary users results in more spectral opportunities for the secondary users at a marginal, or no, cost in the primary network outage. Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive (non-causal) wireless networking framework.

On proactive caching with demand and channel uncertainties

Mobile data traffic has surpassed that of voice to become the main component of the system load of todays wireless networks. Recent studies indicate that the data demand patterns of mobile users are predictable. Moreover, the channel quality of mobile users along their navigation paths is predictable by exploiting their location information. This work aims at fusing the statistically predictable demand and channel patterns in devising proactive caching strategies that alleviate network congestion. Specifically, we establish a fundamental bound on the minimum possible cost achievable by any proactive scheduler under time-invariant demand and channel statistics as a function of their prediction uncertainties, and develop an asymptotically optimal proactive service policy that attains this bound as the prediction window grows. In addition, the established bound yields insights on how the demand and channel statistics affect proactive caching decisions. We reveal some of these insights through numerical investigations.

Joint pricing and proactive caching for data services: Global and user-centric approaches

In this work, we investigate the profit maximization problem of a network service provider through smart pricing and proactive data services. The demand characteristics of each user are dependent on the price and willingness-to-pay values of each service. By learning these characteristics, the service provider can further improve its profit performance through a proactive service of the predictable demand so as to smooth-out its load dynamics over time, and reduce the incurred cost. We formulate the joint price and proactive download allocation problem and study its impact on the expected user payments and the service provider profit. In particular, we show that proactive downloads can only enhance the expected profit of service provider and at the same time reduce the expected payments by the user, when compared with the no-proactive-service regime. The problem is studied from two perspectives: global optimization, and game theory. From the global optimization perspective, the problem is shown to be non-convex, yet an algorithm that yields a local optimal solution with better profit than the no-proactive-download scenario is developed. From the game theoretical perspective, the problem is posed as a coordination game with the user and the service provider are players. Best response dynamics are shown to converge to a Nash Equilibrium (NE) of the game, which is the local optimal solution achieved by the developed non-convex optimization algorithm.

Proactive scheduling for content pre-fetching in mobile networks

The global adoption of smart phones has raised major concerns about a potential surge in the wireless traffic due to the excessive demand on multimedia services. This ever increasing demand is projected to cause significant congestions and degrade the quality of service for network users. In this paper, we develop a proactive caching framework that utilizes the predictability of the mobile user behavior to offload predictable traffic through the WiFi networks ahead of time. First, we formulate the proactive scheduling problem with the objective of maximizing the user-content hit ratio subject to constrains stemming from the user behavioral models. Second, we propose a quadratic-complexity (in the number of slots per day) greedy, yet, high performance heuristic algorithm that pinpoints the best download slot for each content item to attain maximal hit ratio. We confirm the merits of the proposed scheme based on the traces of a real dataset leveraging a large number of smart phone users who consistently utilized our framework for two months.

MIMO broadcast channel with continuous feedback using full-duplex radios

In this paper, we study the use of full-duplex radios for continuous feedback of channel state information in MIMO broadcast channels. The simultaneous transmission of feedback on the same frequency band as downlink transmissions causes inter-node interference (INI) at the receiver. We quantify the impact of INI and associated tradeoff in the design of the feedback channel. We show that a continuously adaptive beamforming strategy can achieve higher multiplexing gain with same feedback resources compared to its half-duplex counterpart.

Proactive Content Distribution for dynamic content

We study the bounds and means of optimal caching in overlay Content Distribution Networks (CDN) that serve data with dynamic content to end-users who send random requests for the most up-to-date version of such content. Applications with such dynamic content are numerous, including daily news, weather conditions, stock market prices, social networking messages, etc. The service for such a dynamically changing content necessitates a fundamentally different approach than traditional pull-based (also called non-proactive) schemes. In particular, proactive caching is required to optimize the type and amount of content to be updated in the local servers of a CDN hence minimize the transmission and caching costs, subject to storage constraints. We study the metric of cost reduction achieved by proactive caching over non-proactive caching strategies. We introduce the notion of popularity to establish fundamental upper and lower bounds on cost reduction under different degrees of storage space constraints. We prove the lower bounds to achieve the optimal rate of increase achieved by the upper bounds as the database of items increases. In particular, for a general form of convex, superlinear and monotonically increasing cost functions, our results reveal that the optimal cost reduction scales as the cost function itself, or at least as its first derivative, depending on the number of popular data items, as well as the cache storage capacity.

Proactive multicasting with predictable demands

In a recent work, we have introduced the notion of proactive resource allocation in wireless networks whereby the predictability of user demands are leveraged to significantly enhance the spectral efficiency of the network in outage limited regimes. In this paper, we expand the horizon to the important scenario of multicast traffic. Our analysis reveals two additional types of gains that can be leveraged in this proactive multicast scenario. The first can be attributed to the basic nature of multicast traffic in which each request would represent a data source rather than a user, as it would in the unicast case. The second is the demand alignment phenomenon whereby the predictive network would wait to gather as much requests as possible and serve them altogether using the same resources. We analytically derive the impact of these advantages on the system diversity gain, which quantifies the exponential decay rate of the outage probability, and further illustrate the resulting gains via numerical results.