International Journal of Advanced Computer Science, Vol. 1, No. 4, Pp. 134-141, Oct. 2011. Manuscript Received: 29, Aug., 2011 Revised: 13, Sep.,2011 Accepted: 25, Sep.,2011 Published: 30, Oct.,2011 Keywords SDR, Cognitive Radio, Optimum data Rate, FHSS (Frequency Hopping Spread Spectrum, Matlab M-File Proramming, Advance Algorithms AbstractSoftware Defined Radio (SDR) is one of possibilities to realize the structure of device with a high mobility, flexibility and reconfigurability. This technology can provide the seamless shifting between existed air- interface standards. Extending the flexibility further, a system capable to sense the spectrum space available for communication and adapt to it is Cognitive Radio. Obviously SDR in Cognitive Radio should be configured not only to independent standards, protocols and services but also to the extensively dynamic nature of bandwidth allocation. Moreover this need of dynamic allocation of Spectrum space is a must to cater to its increased demand. Cognitive radio is envisioned as the ultimate system that can sense, adapt and learn from the environment in which it operates. Sensing the available bandwidth an SDR (Software defined radio) in a Cognitive System, tunes the circuits in the System for transferring data at optimum data rates, permissible by the space available. So it is a must for the SDR to accordingly add processing circuits to maintain the System performance at variable working frequencies. This paper discusses the critical issue of designing the SDR for the Cognitive Radio (CR) and also presents some useful results obtained to configure the SDR for higher bandwidth available in Cognitive Radio. Results of Frequency Hopping Spread Spectrum (FHSS) implementation, Codec Algorithm modifications, and decoder iterations variation and performance improvement using OQPSK are depicted in the paper. Further to meet the narrow bandwidth requirements of CR, advance source coding implementation results have also been depicted. This work was supported by the Research and Development department of Shri Ramdeobaba Kamla Nehru Engineering College, Nagpur, M.S, India. Dr. Rajeshree D. Raut is Asociatet Professor, Department of Electronics & Communication Engineering, SRKNEC, Nagpur, M.S., India. raut-rajeshree@gmail.com. Dr. Kishore D. Kulat is Professor, Electronics & Computer Science Department, VNIT, Nagpur, M. S., India. k.d.kulat@ece.vnit.ac.in 1. Introduction Most part of current research works in the wireless communication technologies focuses on providing many varied services and maintaining high bit rate [1]. The possibility to be reachable in any place and at anytime has also become much demanded. That is why the greatest companies try to find possible solutions to satisfy these requirements. To this end, there is a need to devise the structure which will be able to support the possibility to retune mobile terminals according to the signal reception [2]. Existing approaches assume that a user should purchase a detached device for each standard, because most of them have their own specification of frequencies range, types of modulation, coding scheme, and access to the environment. Therefore, the mobile operator has to provide support for all wireless systems separately. To resolve this problem, the Software Defined Radio (SDR) technology comes in handy [3]. A need to extend the above adaption has aroused over a last couple of years wherein the spectrum demand has increased many folds. Spectrum allocation policy has faced spectrum scarcity in particular spectrum bands. In contrast, a large portion of the assigned spectrum is used sporadically, leading to underutilization of significant amount of spectrum space [4]. Spectral occupancy measurements consistently show that some bands are under-utilized in some areas at some times. The key enabling technology of dynamic spectrum access techniques is Cognitive radio. Joseph Mitola III and Gerald Q. Maguire who first officially presented the idea of Cognitive Radio [5], define it as 8Cognitive radio is an intelligent wireless communication system that is aware of its Radio Frequency (RF) environment, and uses the methodology of understanding- by- building to learn from the environment and adapt its internal states to statistical variation in the environment by making changes to adjustable parameters, namely transmit power, carrier frequency and modulation strategy, all in real Time, [Mitola 1999]. This paper introduces the SDR and Cognitive Radio Technology in Section 2 and 3 respectively. Section 4 explains how to design the SDR for Cognitive radio. Adapting of Advance Source Coding Algorithms to be able to send data when the CR has to tune to narrow bandwidth is proposed. Implementations for the same have been explained in detail. The results of these implementation work carried out using Matlab are depicted in Section 5. The paper concludes SDR Design with Advanced Algorithms for Cognitive Radio Rajeshree D. Raut & Kishore D. Kulat