Samir Medina Perlaza

Power Allocation Strategies in Energy Harvesting Wireless Cooperative Networks

In this paper, a wireless cooperative network is considered, in which multiple source-destination pairs communicate with each other via an energy harvesting relay. The focus of this paper is on the relays strategies to distribute the harvested energy among the multiple users and their impact on the system performance. Specifically, a non-cooperative strategy that uses the energy harvested from the i-th source as the relay transmission power to the i-th destination is considered first, and asymptotic results show that its outage performance decays as log SNR/SNR. A faster decay rate, 1/SNR, can be achieved by two centralized strategies proposed next, of which a water filling based one can achieve optimal performance with respect to several criteria, at the price of high complexity. An auction based power allocation scheme is also proposed to achieve a better tradeoff between system performance and complexity. Simulation results are provided to confirm the accuracy of the developed analytical results.

Self-Organization in Small Cell Networks: A Reinforcement Learning Approach

In this paper, a decentralized and self-organizing mechanism for small cell networks (such as micro-, femto- and picocells) is proposed. In particular, an application to the case in which small cell networks aim to mitigate the interference caused to the macrocell network, while maximizing their own spectral efficiencies, is presented. The proposed mechanism is based on new notions of reinforcement learning (RL) through which small cells jointly estimate their time-average performance and optimize their probability distributions with which they judiciously choose their transmit configurations. Here, a minimum signal to interference plus noise ratio (SINR) is guaranteed at the macrocell user equipment (UE), while the small cells maximize their individual performances. The proposed RL procedure is fully distributed as every small cell base station requires only an observation of its instantaneous performance which can be obtained from its UE. Furthermore, it is shown that the proposed mechanism always converges to an epsilon Nash equilibrium when all small cells share the same interest. In addition, this mechanism is shown to possess better convergence properties and incur less overhead than existing techniques such as best response dynamics, fictitious play or classical RL. Finally, numerical results are given to validate the theoretical findings, highlighting the inherent tradeoffs facing small cells, namely exploration/exploitation, myopic/foresighted behavior and complete/incomplete information.

Opportunistic interference alignment in MIMO interference channels

We present two interference alignment techniques such that an opportunistic point-to-point multiple input multiple output (MIMO) link can reuse, without generating any additional interference, the same frequency band of a similar pre-existing primary link. In this scenario, we exploit the fact that under power constraints, although each radio maximizes independently its rate by water-filling on their channel transfer matrix singular values, frequently, not all of them are used. Therefore, by aligning the interference of the opportunistic radio it is possible to transmit at a significant rate while insuring zero-interference on the pre-existing link. We propose a linear pre-coder for a perfect interference alignment and a power allocation scheme which maximizes the individual data rate of the secondary link. Our numerical results show that significant data rates are achieved even for a reduced number of antennas.

Learning equilibria with partial information in decentralized wireless networks

In this article, a survey of several important equilibrium concepts for decentralized networks is presented. The term decentralized is used here to refer to scenarios where decisions (e.g., choosing a power allocation policy) are taken autonomously by devices interacting with each other (e.g., through mutual interference). The iterative long-term interaction is characterized by stable points of the wireless network called equilibria. The interest in these equilibria stems from the relevance of network stability and the fact that they can be achieved by letting radio devices to repeatedly interact over time. To achieve these equilibria, several learning techniques - the best response dynamics, fictitious play, smoothed fictitious play, reinforcement learning algorithms, and regret matching - are discussed in terms of information requirements and convergence properties. Most of the notions introduced here, for both equilibria and learning schemes, are illustrated by a simple case study, an interference channel with two transmitter-receiver pairs.

Electrical Vehicles in the Smart Grid: A Mean Field Game Analysis

In this article, we investigate the competitive interaction between electrical vehicles or hybrid oil-electricity vehicles in a Cournot market consisting of electricity transactions to or from an underlying electricity distribution network. We provide a mean field game formulation for this competition, and introduce the set of fundamental differential equations ruling the behavior of the vehicles at the feedback Nash equilibrium, referred here to as the mean field equilibrium. This framework allows for a consistent analysis of the evolution of the price of electricity as well as of the instantaneous electricity demand in the power grid. Simulations precisely quantify those parameters and suggest that significant reduction of the daily electricity peak demand can be achieved by appropriate electricity pricing.

Quality-Of-Service Provisioning in Decentralized Networks: A Satisfaction Equilibrium Approach

This paper introduces a particular game formulation and its corresponding notion of equilibrium, namely the satisfaction form (SF) and the satisfaction equilibrium (SE). A game in SF models the case where players are uniquely interested in the satisfaction of some individual performance constraints, instead of individual performance optimization. Under this formulation, the notion of equilibrium corresponds to the situation where all players can simultaneously satisfy their individual constraints. The notion of SE, models the problem of QoS provisioning in decentralized self-configuring networks. Here, radio devices are satisfied if they are able to provide the requested QoS. Within this framework, the concept of SE is formalized for both pure and mixed strategies considering finite sets of players and actions. In both cases, sufficient conditions for the existence and uniqueness of the SE are presented. When multiple SE exist, we introduce the idea of effort or cost of satisfaction and we propose a refinement of the SE, namely the efficient SE (ESE). At the ESE, all players adopt the action which requires the lowest effort for satisfaction. A learning method that allows radio devices to achieve a SE in pure strategies in finite time and requiring only one-bit feedback is also presented. Finally, a power control game in the interference channel is used to highlight the advantages of modeling QoS problems following the notion of SE rather than other equilibrium concepts, e.g., generalized Nash equilibrium.

Decentralized Cross-Tier Interference Mitigation in Cognitive Femtocell Networks

In this paper, recent results in game theory and stochastic approximation are brought together to mitigate the problem of femto-to-macrocell cross-tier interference. The main result of this paper is an algorithm which reduces the impact of interference of femtocells over the existing macrocells. Such algorithm relies on the observations of the signal to interference plus noise ratio (SINR) of all active communications in both macro and femtocells when they are fed back to the corresponding base stations. Based on such observations, femto base stations learn the probability distributions over the feasible transmit configurations (frequency band and power levels) such that a minimum time-average SINR can be guaranteed in the macrocells, at the equilibrium. In this paper, we introduce the concept of logit equilibrium (LE) and present its interpretation in terms of the trade-off faced by femtocells when experimenting several actions to discover the network, and taking the action to maximize their instantaneous performance. Finally, numerical results are given to validate our theoretical findings.

On the Resilience of Wireless Multiuser Networks to Passive and Active Eavesdroppers

Physical layer security can provide alternative means for securing the exchange of confidential messages in wireless applications. In this paper, the resilience of wireless multiuser networks to passive (interception of the broadcast channel) and active (interception of the broadcast channel and false feedback) eavesdroppers is investigated under Rayleigh fading conditions. Stochastic characterizations of the secrecy capacity (SC) are obtained in scenarios involving a base station and several destinations. The expected values and variances of the SC along with the probabilities of secrecy outages are evaluated in the following cases: (i) in the presence of passive eavesdroppers without any side information; (ii) in the presence of passive eavesdroppers with side information about the number of eavesdroppers; and (iii) in the presence of a single active eavesdropper with side information about the behavior of the eavesdropper. This investigation demonstrates that substantial secrecy rates are attainable on average in the presence of passive eavesdroppers as long as minimal side information is available. On the other hand, it is further found that active eavesdroppers can potentially compromise such networks unless statistical inference is employed to restrict their ability to attack. Interestingly, in the high signal to noise ratio regime, multiuser networks become insensitive to the activeness or passiveness of the attack.

On the base station selection and base station sharing in self-configuring networks

We model the interaction of several radio devices aiming to obtain wireless connectivity by using a set of base stations (BS) as a non-cooperative game. Each radio device aims to maximize its own spectral efficiency (SE) in two different scenarios: First, we let each player to use a unique BS (BS selection) and second, we let them to simultaneously use several BSs (BS Sharing). In both cases, we show that the resulting game is an exact potential game. We found that the BS selection game posses multiple Nash equilibria (NE) while the BS sharing game posses a unique one. We provide fully decentralized algorithms which always converge to a NE in both games. We analyze the price of anarchy and the price of stability for the case of BS selection. Finally, we observed that depending on the number of transmitters, the BS selection technique might provide a better global performance (network spectral efficiency) than BS sharing, which suggests the existence of a Braess type paradox.

Learning coarse correlated equilibria in two-tier wireless networks

In this paper, we study the strategic coexistence between macro and femto cell tiers from a game theoretic learning perspective. A novel regret-based learning algorithm is proposed whereby cognitive femtocells mitigate their interference toward the macrocell tier, on the downlink. The proposed algorithm is fully decentralized relying only on the signal-to-interference-plus-noise ratio (SINR) feedback to the corresponding femtocell base stations. Based on these local observations, femto base stations learn the probability distribution of their transmission strategies (power levels and frequency band) by minimizing their regrets for using certain strategies, while adhering to the cross-tier interference constraint. The decentralized regret based learning algorithm is shown to converge to an ϵ-coarse correlated equilibrium (ϵ-CCE) which is a generalization of the classical Nash Equilibrium (NE). Finally, numerical results are shown to corroborate our findings where, quite remarkably, our learning algorithm achieves the same performance as the classical regret matching, but with substantially much less overhead.