Combined Learning for Resource Allocation in Autonomous Heterogeneous Cellular Networks Autocorrelation Function for Cyclostationary Process Compressed Sensing - Archive ouverte HAL Access content directly
Conference Papers Year : 2013

Combined Learning for Resource Allocation in Autonomous Heterogeneous Cellular Networks Autocorrelation Function for Cyclostationary Process Compressed Sensing

Abstract

The cross- and co-tier interference creates the challenges to facilitate the concept of heterogeneous cellular networks (HCNs) in practice. In this paper, we establish a combined learning framework to autonomously mitigate the destructive interference. The macrocell is modeled as the leader and protects itself through pricing the interference from small-cells, which are the followers in the stochastic learning process. During each epoch (an epoch consists of T time slots), the leader commits to a pricing policy by knowing the resource allocation policies of all followers, while the followers compete against each other in each time slot only with the leader's price information. In general, for any two consecutive epochs, the HCN states are highly correlated. The previous policy information can thus be leveraged to improve the learning performance. Numerical results support that the proposed study substantially protects the macrocell and at the same time, optimizes the energy efficiency in small-cells.
Not file

Dates and versions

hal-00868243 , version 1 (01-10-2013)

Identifiers

  • HAL Id : hal-00868243 , version 1

Cite

Xianfu Chen, Hongguang Zhang, Tao Chen, Jacques Palicot. Combined Learning for Resource Allocation in Autonomous Heterogeneous Cellular Networks Autocorrelation Function for Cyclostationary Process Compressed Sensing. IEEE PIMRC 2013, Sep 2013, London, United Kingdom. ⟨hal-00868243⟩
126 View
0 Download

Share

Gmail Facebook Twitter LinkedIn More