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.Index Terms-predictable demand, proactive caching, resource allocation, scheduling, uncertainty. 1063-6692

In this work, we propose and study optimal proactive resource allocation and demand shaping for data networks. Motivated by the recent findings on the predictability of human behavior patterns in data networks, and the emergence of highly capable handheld devices, our design aims to smooth out the network traffic over time and minimize the data delivery costs.Our framework utilizes proactive data services as well as smart content recommendation schemes for shaping the demand. Proactive data services take place during the off-peak hours based on a statistical prediction of a demand profile for each user, whereas smart content recommendation assigns modified valuations to data items so as to render the users' demand less uncertain. Hence, our recommendation scheme aims to boost the performance of proactive services within the allowed flexibility of user requirements. We conduct theoretical performance analysis that quantifies the leveraged cost reduction through the proposed framework. We show that the cost reduction scales at the same rate as the cost function scales with the number of users. Further, we prove that demand shaping through smart recommendation strictly reduces the incurred cost even below that of proactive downloads without recommendation.

Multiple transmitting antennas can considerably increase the downlink spectral efficiency by beamforming to multiple users at the same time. However, multiuser beamforming requires channel state information (CSI) at the transmitter, which leads to training overhead and reduces overall achievable spectral efficiency. In this paper, we propose and analyze a sequential beamforming strategy that utilizes full-duplex base station to implement downlink data transmission concurrently with CSI acquisition via in-band closed or open loop training. Our results demonstrate that full-duplex capability can improve the spectral efficiency of uni-directional traffic, by leveraging it to reduce the control overhead of CSI estimation. In moderate SNR regimes, we analytically derive tight approximations for the optimal training duration and characterize the associated respective spectral efficiency. We further characterize the enhanced multiplexing gain performance in the high SNR regime. In both regimes, the performance of the proposed full-duplex strategy is compared to the half-duplex counterpart to quantify spectral efficiency improvement. With experimental data [1] and 3D channel model [2] from 3GPP, in a 1.4 MHz 8 × 8 system LTE system with the block length of 500 symbols, the proposed strategy attains a spectral efficiency improvement of 130% and 8% with closed and open loop training, respectively.

Abstract-This paper introduces the novel concept of proactive resource allocation through 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 with the prediction duration T . This model is then generalized to incorporate the effect of 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 improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed lights on multicasting with predictable demands and show that the proactive multicast networks can achieve a significantly higher diversity gain that scales super-linearly with T . Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive (non-causal) wireless networking framework.