Power allocation in multi-hop OFDM transmission systems with amplify-and-forward relaying: A unified approach

In this paper, a unified approach for power allocation (PA) in multi-hop orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) relaying systems is presented. In the proposed approach, we consider short and long term individual and total power constraints at the source and relays, and devise low complexity PA algorithms when wireless links are subject to channel path-loss and small-scale Rayleigh fading. To manage the complexity, in the proposed formulations, we adopt a two-stage iterative approach consisting of a power distribution phase among distinct subcarriers, and a power allocation phase among different relays. In particular, aiming at improving the instantaneous rate of multi-hop transmission systems with AF relaying, we develop (i) a near-optimal iterative PA algorithm based on the exact analysis of the received SNR at the destination; (ii) a low complexity suboptimal iterative PA algorithm based on an approximate expression of the received SNR at high-SNR regime; and (iii) a low complexity non-iterative PA scheme with limited performance loss. Simulation results show the superior performance of the proposed power allocation algorithms

Practical Resource Allocation Algorithms for QoS in OFDMA-based Wireless Systems

In this work we propose an efficient resource allocation algorithm for OFDMA based wireless systems supporting heterogeneous traffic. The proposed algorithm provides proportionally fairness to data users and short term rate guarantees to real-time users. Based on the QoS requirements, buffer occupancy and channel conditions, we propose a scheme for rate requirement determination for delay constrained sessions. Then we formulate and solve the proportional fair rate allocation problem subject to those rate requirements and power/bandwidth constraints. Simulations results show that the proposed algorithm provides significant improvement with respect to the benchmark algorithm.Comment: To be presented at 2nd IEEE International Broadband Wireless Access Workshop. Las Vegas, Nevada USA Jan 12 200

Green Communication via Power-optimized HARQ Protocols

Recently, efficient use of energy has become an essential research topic for green communication. This paper studies the effect of optimal power controllers on the performance of delay-sensitive communication setups utilizing hybrid automatic repeat request (HARQ). The results are obtained for repetition time diversity (RTD) and incremental redundancy (INR) HARQ protocols. In all cases, the optimal power allocation, minimizing the outage-limited average transmission power, is obtained under both continuous and bursting communication models. Also, we investigate the system throughput in different conditions. The results indicate that the power efficiency is increased substantially, if adaptive power allocation is utilized. For instance, assume Rayleigh-fading channel, a maximum of two (re)transmission rounds with rates $\{1,\frac{1}{2}\}$ nats-per-channel-use and an outage probability constraint ${10}^{-3}$. Then, compared to uniform power allocation, optimal power allocation in RTD reduces the average power by 9 and 11 dB in the bursting and continuous communication models, respectively. In INR, these values are obtained to be 8 and 9 dB, respectively.Comment: Accepted for publication on IEEE Transactions on Vehicular Technolog

Optimal Power Control for Analog Bidirectional Relaying with Long-Term Relay Power Constraint

Wireless systems that carry delay-sensitive information (such as speech and/or video signals) typically transmit with fixed data rates, but may occasionally suffer from transmission outages caused by the random nature of the fading channels. If the transmitter has instantaneous channel state information (CSI) available, it can compensate for a significant portion of these outages by utilizing power allocation. In a conventional dual-hop bidirectional amplify-and-forward (AF) relaying system, the relay already has instantaneous CSI of both links available, as this is required for relay gain adjustment. We therefore develop an optimal power allocation strategy for the relay, which adjusts its instantaneous output power to the minimum level required to avoid outages, but only if the required output power is below some cutoff level; otherwise, the relay is silent in order to conserve power and prolong its lifetime. The proposed scheme is proven to minimize the system outage probability, subject to an average power constraint at the relay and fixed output powers at the end nodes.Comment: conference IEEE Globecom 2013, Atlanta, Georgia, U

Convergence Analysis of Mixed Timescale Cross-Layer Stochastic Optimization

This paper considers a cross-layer optimization problem driven by multi-timescale stochastic exogenous processes in wireless communication networks. Due to the hierarchical information structure in a wireless network, a mixed timescale stochastic iterative algorithm is proposed to track the time-varying optimal solution of the cross-layer optimization problem, where the variables are partitioned into short-term controls updated in a faster timescale, and long-term controls updated in a slower timescale. We focus on establishing a convergence analysis framework for such multi-timescale algorithms, which is difficult due to the timescale separation of the algorithm and the time-varying nature of the exogenous processes. To cope with this challenge, we model the algorithm dynamics using stochastic differential equations (SDEs) and show that the study of the algorithm convergence is equivalent to the study of the stochastic stability of a virtual stochastic dynamic system (VSDS). Leveraging the techniques of Lyapunov stability, we derive a sufficient condition for the algorithm stability and a tracking error bound in terms of the parameters of the multi-timescale exogenous processes. Based on these results, an adaptive compensation algorithm is proposed to enhance the tracking performance. Finally, we illustrate the framework by an application example in wireless heterogeneous network

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network