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The performance of existing coded caching schemes is sensitive to the worst channel quality, a problem which is exacerbated when communicating over fading channels. In this paper, we address this limitation in the following manner: in short-term, we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness among users. Our online scheme combines (i) the classical decentralized coded caching scheme with (ii) joint scheduling and power control for the fading broadcast channel, as well as (iii) congestion control for ensuring the optimal long-term average performance. We prove that our online delivery scheme maximizes the alpha-fair utility among all schemes restricted to decentralized placement. By tuning the value of alpha, the proposed scheme can achieve different operating points on the average delivery rate region and tune performance according to an operator’s choice. We demonstrate via simulations that our scheme outperforms two baseline schemes: (a) standard coded caching with multicast transmission, limited by the worst channel user yet exploiting the global caching gain; (b) opportunistic scheduling with unicast transmissions exploiting the fading diversity but limited to local caching gain. [ABSTRACT FROM AUTHOR]

We study the problem of wireless edge caching when file popularity is unknown and possibly non-stationary. A bank of $J$ caches receives file requests and a utility is accrued for each request depending on the serving cache. The network decides dynamically which files to store at each cache and how to route them, in order to maximize total utility. The request sequence is assumed to be drawn from an arbitrary distribution, capturing time-variance, temporal and spatial locality of requests. For this challenging setting, we propose the Bipartite Supergradient Caching Algorithm (BSCA) which provably exhibits no regret ($R_{T}/T \to 0$). That is, as the time horizon $T$ increases, BSCA achieves (at least) the same utility with the cache configuration that we would have chosen knowing all future requests. The learning rate of the algorithm is characterized by its regret expression $R_{T}\!=\!O(\sqrt {JT})$ , which is independent of the file library size. For the single-cache case, we prove that this is the lowest attainable bound. BSCA requires at each step $J$ projections on intersections of boxes and simplices, for which we propose a tailored algorithm. Our model is the first that draws a connection between the network caching problem and Online Convex Optimization, and we demonstrate its generality by discussing various practical extensions and presenting a trace-driven comparison with state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Software-defined network (SDN) controllers include mechanisms to globally reconfigure the network in order to respond to a changing environment. As demands arrive or leave the system, the globally optimum flow configuration changes over time. Although the optimum configuration can be computed with standard iterative methods, convergence may be slower than system variations, and hence it may be preferable to interrupt the solver and restart. In this paper, we focus on the class of iterative solvers with an exponential decrease over time in the optimality gap. Assuming dynamic arrivals and departures of demands, the computed optimality gap at each iteration $Q(t)$ is described by an auto-regressive stochastic process. At each time slot, the controller may choose to: 1) stop the iterative solver and apply the best found configuration to the network or 2) allow the solver to continue the iterations keeping the network in its suboptimal form. Choice 1) reduces the optimality gap leading to smaller routing costs but requires flow reconfiguration which hurts QoS and system stability. To limit the negative impact of reconfigurations, we propose two control policies that minimize the time-average routing cost while respecting a network reconfiguration budget. We experiment with realistic network settings using standard linear programming tools from SDN industry. In the experiments conducted over the GEANT networks and fat tree networks, our policies provide a practical means of keeping the routing cost small within a given reconfiguration constraint. [ABSTRACT FROM AUTHOR]

We investigate a pilot allocation problem in wireless networks over Markovian fading channels. In wireless systems, the channel state information (CSI) is collected at the base station, in particular, this paper considers a pilot-aided channel estimation method (TDD mode). Typically, there are less available pilots than users, hence at each slot the scheduler needs to decide an allocation of pilots to users with the goal of maximizing the long-term average throughput. There is an inherent tradeoff in how the limited pilots are used: assign a pilot to a user with up-to-date CSI and good channel condition for exploitation, or assign a pilot to a user with outdated CSI for exploration. As we show, the arising pilot allocation problem is a restless bandit problem and thus its optimal solution is out of reach. In this paper, we propose an approximation based on the Lagrangian relaxation method, which provides a low-complexity Whittle index policy. We prove this policy to be asymptotically optimal in the many users regime (when the number of users in the system and the available pilots for channel sensing grow large). We evaluate the performance of Whittle’s index policy in various scenarios and illustrate its remarkably good performance for small number of users, where it is not guaranteed to be optimal. [ABSTRACT FROM AUTHOR]

In this paper, the problem of feedback and active user selection in multiple-input single-output (MISO) wireless systems such that the system’s stability region is as big as possible is examined. The focus is on a system in a Rayleigh fading environment where zero forcing precoding is used to serve all active users in every slot. Acquisition of the channel states is done via uplink training in time division duplexing mode by the active users. Clearly, only a subset of users can perform uplink training and the selection of this subset is a challenging and interesting problem especially in MISO systems. The stability regions of a baseline centralized scheme and two novel decentralized policies are examined analytically. In the decentralized schemes, the transmitter broadcasts periodically the queue state information and the users contend for the channel in a carrier sense multiple access-based manner with parameters based on the outdated queue state information and real-time channel state information. We show that, using infrequent signaling between the base station and the users, the decentralized policies outperform the centralized policy. In addition, a threshold-based user selection and training scheme for discrete-time contention is proposed. The results of this paper imply that, as far as stability is concerned, the users must be involved in the active user selection and feedback/training decision. This should be leveraged in future communication systems. [ABSTRACT FROM AUTHOR]

In this work, we address the problem of power allocation for interfering transmitter-receiver pairs so that the probability that each queue length exceeds a specified threshold is fixed at a desired value. One application is satisfying QoS requirements in a dense cellular network. We deal with this problem using heavy traffic approximation techniques which lead to an asymptotic model of a (controlled) stochastic differential equation. The proposed power control strategy consists of allocating most of the power according to the states of the channel and a smaller fraction according to the queue lengths, for which we find a closed-form expression. We first consider a scenario where all channel realizations and queue lengths are known instantaneously to every transmitter. Then, the algorithm is extended to the case where only local SINR feedback is available and when queue length information is shared with delays among the transmitters. These models and results are also extended to the case where the transmitters are equipped with multiple antennas. Finally, the applicability in practical system settings are discussed and simulation results are provided to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Broadband satellite communication networks, operating at Ka band and above, play a significant role to worldwide telecommunication infrastructure, providing backhaul and direct-to-user satellite services. Rain attenuation at these frequencies is the most dominant impairment factor that degrades the performance of the system. The dynamic reconfiguration of multi-beam satellite antennas has recently been proposed as a fading mitigation technique. In this paper, the problem of finding the power allocation among the beams of such an antenna, aiming that the number of subscribers not receiving the desired quality of service is minimized is addressed for a satellite communications system with a GEO satellite serving fixed earth terminals. An algorithm for the dynamic power allocation using a physical-mathematical model for rain attenuation prediction is proposed. Extended simulation results highlight the improvement of the dynamic scheme over the static allocation based on long term rain attenuation characteristics. [ABSTRACT FROM PUBLISHER]