Learning for Cross-layer Resource Allocation in the Framework of Cognitive Wireless Networks

The framework of cognitive wireless networks is expected to endow wireless devices with a cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In this dissertation, we focus on the problem of developing cognitive network control mechanisms without knowing in advance an accurate network model. We study a series of cross-layer resource allocation problems in cognitive wireless networks. Based on model-free learning, optimization and game theory, we propose a framework of self-organized, adaptive strategy learning for wireless devices to (implicitly) build the understanding of the network dynamics through trial-and-error. The work of this dissertation is divided into three parts. In the first part, we investigate a distributed, single-agent decision-making problem for real-time video streaming over a time-varying wireless channel between a single pair of transmitter and receiver. By modeling the joint source-channel resource allocation process for video streaming as a constrained Markov decision process, we propose a reinforcement learning scheme to search for the optimal transmission policy without the need to know in advance the details of network dynamics. In the second part of this work, we extend our study from the single-agent to a multi-agent decision-making scenario, and study the energy-efficient power allocation problems in a two-tier, underlay heterogeneous network and in a self-sustainable green network. For the heterogeneous network, we propose a stochastic learning algorithm based on repeated games to allow individual macro- or femto-users to find a Stackelberg equilibrium without flooding the network with local action information. For the self-sustainable green network, we propose a combinatorial auction mechanism that allows mobile stations to adaptively choose the optimal base station and sub-carrier group for transmission only from local payoff and transmission strategy information. In the third part of this work, we study a cross-layer routing problem in an interweaved Cognitive Radio Network (CRN), where an accurate network model is not available and the secondary users that are distributed within the CRN only have access to local action/utility information. In order to develop a spectrum-aware routing mechanism that is robust against potential insider attackers, we model the uncoordinated interaction between CRN nodes in the dynamic wireless environment as a stochastic game. Through decomposition of the stochastic routing game, we propose two stochastic learning algorithm based on a group of repeated stage games for the secondary users to learn the best-response strategies without the need of information flooding

D6.2 - Prototype description and field trial results

Deliverable D6.2 del projecte FARAMIRPostprint (published version

4G 네트워킹 디바이스로 구성된 이름 주소 기반 네트워크를 위한 테스트베드

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 권태경.In recent years, mobile traffic (especially video traffic) explosion has become serious concern for mobile network operators. While video streaming services become crucial for mobile users, their traffic may often exceed the bandwidth capacity of cellular networks. To address the video traffic problem, we consider a future Internet architecture: Named-Data Networking (NDN). NDN is an innovative network architecture that is being considered as a successor to the Internet. In this thesis, we design and implement framework of adaptive mobile video streaming and sharing in the NDN architecture (AMVS-NDN) with multiple wireless interfaces (e.g., 4G LTE and Wi-Fi). To demonstrate the benefit of NDN, AMVS-NDN has two key functionalities: (1) in the base situation, a mobile station (MS) tries to use either 4G LTE or Wi-Fi links opportunistically, further using Multi-Interface technology, 4G LTE and Wi-Fi links can be used simultaneously, and (2) MSs can share content directly by exploiting local Wi-Fi direct connectivity. We implement AMVS-NDN over NDN and Multi-Interface, the tests are performed in a real testbed consisting of a WiMAX base station, a LTE Femtocell and Android phones. Testing with time-varying link conditions in mobile environments reveals that AMVS-NDN achieves the higher video quality and less cellular traffic than other solutions, with using Multi-Interface, AMVS-NDN can gain the highest video quality.Contents I. Introduction 6 II. Related Work 12 2.1 Named Data Networking 12 2.2 Adaptive Video Streaming 13 2.3 MS-to-MS Content Sharing 13 2.4 Multi Interface 14 III. AMVS-NDN Framework 15 3.1 AMVS-NDN illustration 15 3.2 Video Segmentation and Naming 16 3.3 Adaptive Streaming Strategy in AMVS-NDN 17 3.4 Dealing with Delays 20 IV. Video Sharing in AMVS-NDN 22 V. Details of Multi Interface 24 5.1 NDN-Femtocell for Edge Caching 24 5.2 Multi-Interface in Linux 24 5.3 Multi-interface in Android 26 VI. Implementation and Evaluation 27 6.1 Testbed Environment 29 6.2 AMVS-NDN Evaluation 30 6.3 AMVS-NDN Streaming and Sharing 33 6.4 Comparison with Pure-NDN and DASH-NDN 34 6.5 Multi Interface in Linux with LTE access to the Femtocell 36 6.6 Multi Interface in Android with LTE access 38 6.7 Live broadcasting with CCNx and Wi-Fi Direct 41 VII. Conclusion 43 VIII. References 45Maste