COGNITIVE RADIO NETWORKING AND RENDEZVOUS Presented by Vinay chekuri

Contents 1. Introduction 2.Wireless networking challenges 3. Cognitive network 4.Autocratic method of cognitive radio network development 5. Waveform distribution and Rendezvous 6.Distributed AI 7.Cognitive radio network Test beds 8.Conclusion UHCL

INTRODUCTION Wireless technology is rapidly proliferating into all aspects of computing and communication. Radio technology will be at the very heart of the future computing world. Cognitive radios offer the promise of being just this disruptive technology innovation that will enable the future wireless world. Cognitive radios are fully programmable wireless devices that can sense their environment and dynamically adapt their transmission waveform, channel access method, spectrum use and networking protocols as needed for good network and application performance UHCL

Wireless Networking Challenges Why is wireless networking hard? Resources are constrained – Spectrum “scarcity” → bandwidth & delay issues Environment changes – Mobility → different surroundings (indoor, urban, rural) Varying physical properties – Wireless communication path changes over time UHCL

Cognitive network Basic functionality of the cognitive radio system is the ability to transfer the information and solutions among the nodes operating on the network. Cognitive network is more than a network of cognitive radios. Cognitive network exhibits distributed intelligence by configuring individual nodes to meet dynamic set of network goals. UHCL

Cognitive networking vision UHCL

Autocratic method of cognitive radio network development In this method one radio develops a waveform and pushes it out to the other nodes for them to use. This method falls short of realizing the full potential of cognitive radio network. It is because one radios optimized waveform may not be the same as another. UHCL

Waveform distribution and Rendezvous Simplest approach to enabling communication among cognitive radio nodes is through a static control channel. This model uses two scenarios 1. in band signaling 2. out-of-band signaling Rendezvous The method by which a radio hails and enters a network. UHCL

Problems associated with static control channel and methods to overcome Static control channels which are easily implemented are problematic because they are easily jammed and rendered useless. In order to overcome this few proposals have been mad e which include 1. Using dynamic control channels. 2. Remove control channel and use physical layer descriptors. 3. Use of embedded cyclostationary signatures in OFDM based systems. 4. Transmitting a beaconing signal UHCL

Cognitive radio networks Cognitive networks uses objective functions that optimize with respect to network performance. They use game theory approach to optimize an ad hoc network with respect to power and channel control. Game theory has been widely studied for wireless network optimization to look for optimal states for all zones UHCL

Distributed AI Distributed AI offers significant potential to improve the global solutions and reduces the time and power required by any individual node. Benefit from looking at the whole network instead of single node adaption is the advantage of available processing power capabilities of each node. Genetic algorithms have shown themselves to be easily separable for processing portions on different processors. Goldberg cites many methods that take advantage of the population of a GA in a distributed sense. UHCL

Popular technique is to create islands of population. These are then optimized. Parallel GA’s have some form of migration or sharing of population. Implementation of the migration should be designed to consider the required network overhead. UHCL

Cognitive radio wireless network testbeds Controlled testbeds that can be used for relatively early testing of prototypes of partially or fully integrated networks. Key requirements are flexibility high degree of control isolation, andrepeatability and safety (i.e., errors in UHCL

Open testbeds that can support larger scale experiments in fully realistic environments. The key difference with controlled testbeds is that being immersed in the real world (“open”), the signal propagation environment will include the effects of real world objects, mobile objects and people, and possibly interference from a variety of RF sources. Key requirements include heterogeneity and programmability at all levels of the system. UHCL

Cognitive radio test bed deployment plan UHCL

Conclusion A network of cognitive radio must include methods by which to transfer waveforms among all nodes. Take into consideration the needs of all other nodes when designing a new waveform. Consideration should be given to the overhead required on the network to transfer the information related to the cognitive radio performance and network maintenance. UHCL

References (1) C. Cordeiro and k.challapali cognitive protocal for multichannel wireless networks. (2) J.zhao,h.zheng “distributed coordination in dynamic spectrum allocation networks”. (3) J.neel, “Analysis and design of cognitive radio networks and distributed radio resource management algorithms”. (3) Genetic algorithms in search,optimization and machine learning by D.E.Goldberg. (4) “A survey of parallel distributed genetic algorithms” by E.Alba UHCL