Cognitive/Software Radio

Cognitive radio (CR) is one of the new long term developments taking place and radio receiver and radio communications technology. After the Software Defined Radio (SDR) which is slowly becoming more of a reality, cognitive radio (CR) and cognitive radio technology will be the next major step forward enabling more effective radio communications systems to be developed.

The idea for cognitive radio has come out of the need to utilize the radio spectrum more efficiently, and to be able to maintain the most efficient form of communication for the prevailing conditions. By using the levels of processing that are available today, it is possible to develop a radio that is able to look at the spectrum, detect which frequencies are clear, and then implement the best form of communication for the required conditions. In this way cognitive radio technology is able to select the frequency band, the type of modulation, and power levels most suited to the requirements, prevailing conditions and the geographic regulatory requirements.

Major Functional Blocks of CR

Cognitive radio architecture

In addition to the level of processing required for cognitive radio, the RF sections will need to be particularly flexible. Not only may they need to swap frequency bands, possibly moving between portions of the radio communications spectrum that are widely different in frequency, but they may also need to change between transmission modes that could occupy different bandwidths.

To achieve the required level of performance will need a very flexible front end. Traditional front end technology cannot handle these requirements because they are generally band limited, both for the form of modulation used and the frequency band in which they operate. Even so called wide band receivers have limitations and generally operate by switching front ends as required. Accordingly, the required level of performance can only be achieved by converting to and from the signal as close to the antenna as possible. In this way no analogue signal processing will be needed, all the processing being handled by the digital signal processing.

The conversion to and from the digital format is handled by digital to analogue converters (DACs) and analogue to digital converters (ADCs). To achieve the performance required for a cognitive radio, not only must the DACs and ADCs have an enormous dynamic range, and be able to operate over a very wide range, extending up to many GHz, but in the case of the transmitter they must be able to handle significant levels of power.

With the required DAC and ADC technology,Cognitive Radio will become a commonplace thing.

Research Challenges

• Decision Making - As Cognitive radio system CRS is driven by a decision making, the first relevant research challenge is where and how the decision (e.g., the decision on spectrum availability, strategy for selecting channel for sensing or access, or how to optimize radio performance) should be taken. The first issue is directly related to whether the cognitive process should be implemented in a centralized or distributed fashion. This aspect is more critical not only for cognitive networks, where intelligence is more likely to be distributed, but also for cognitive radios, as decision making could be influenced by collaboration between them and also with other devices. The second issue is the choice of the decision algorithms (e.g., neural networks, genetic algorithms, ant-colony optimization, etc.) which should be customized to fulfill the CRS requirements.

• Learning Process - Research in machine learning has grown dramatically recently, with significant amount of progress. One of the important aspects of the learning mechanisms is whether the learning performed is supervised or unsupervised. In the context of a CRS, either technique may be applied. The first challenge of learning is to avoid wrong choices before a feasible decision, especially in autonomous or unsupervised learning process. The second issue is to concretely define learning process in the context of CRS, its objectives and contributions. In terms of implementation and algorithm design, the cognitive functionalities, which are related to enabling devices or networks to learn from past decisions to improve their behavior, are too much complex. The design of the learning algorithm represents by itself a challenge, and measurements which should be employed by learning open new issues related to which measurements to use and how to perform them.

• Cross-Layers - While the aspect of inter-protocol interaction is included in the concept of cognitive network as means to support user and applications requirement, no relevant and comprehensive analysis is available to address the performance and, in general, the behavior of applications and networks based on CRS technology. The design of cognitive or self-organized network is itself a challenging task, in particular, the outer and inner loops coordination, the networking middle-ware for knowledge exchange, and inter-system networking for sharing and cooperation. Challenge is also in the design of high layers including MAC sublayer and network layer, spectrum management functions integrated at the different layers of the network protocol stack, cognitive radio resource management and coordination, various protocols and routing. Many technologies will be using multiple frequency bands. As a result, challenges in interoperability, including coexistence, cooperation and collaboration for devices, and networks signaling with cross-layer interfaces and inter-layer signaling are to be solved.

• Security - The challenges of employing CRS include that of ensuring secure devices operations. Security in this context includes enforcement of rules. Enforcement for static systems is already a challenge due to the amount of resources necessary to authorize equipment, the requirement of obtaining proof that violations have occurred, and the determination of the violator’ identities. As the systems become more dynamic, there is an increase in the number of potential interaction that can lead to a violation. Additionally, this leads to a decrease of the time and special scales of these interactions. Both of these changes will amplify the enforcement challenges. 

• Sensing - Following challenge is about spectrum sensing, especially on the accuracy on spectrum occupancy decision, sensing time, and malicious adversary, taking into account the fundamental limits of spectrum sensing algorithms due to noise uncertainty multi path fading and shadowing. In order to solve hidden PU problem and mitigate the impact of these issues, cooperative spectrum sensing has been shown to be an effective method to improve the detection performance by exploiting spatial diversity in the observations of spatially located CRs. Challenges of cooperative sensing include reducing cooperation overhead, developing efficient information sharing algorithms. The coordination algorithm for cooperation should be robust to changes and failures in the network, and introduce a minimum amount of delay.

• Geolocation -  Geolocation is an important CR enabling technology due to the wide range of applications that may results from a radio being aware of its current location and possibility being aware of its planned path and destination. When CRS uses the geolocation technology for location determination combined with a database look-up, each access point (AP) may be connected to one, or multiple databases which provide information on the unused TV White Spaces (TVWS) channels that are available at the location of AP and they provide also information on maximum transmit power levels usable in each channel. Furthermore, the use of master-slave technology is encouraged so that the necessary functionalities for database lookup and channel selection need to be implemented only in APs. It keeps the complexity and cost of end-user devices to a minimum. However, the challenges in this area are who and how to implement the data base, how to feed it. Providing incumbent (or PUs) databases requires knowledge of the locations of CR devices whose precision should be specified. If global positioning service (GPS) is equipped and CR devices are outdoor, obtaining their geolocations may still be less a technical challenge. If no GPS is available or if CR devices are indoor, then obtaining geolocations becomes a challenging task.

It is a very interesting topic and a lot of research is being done at present. I tried to incorporate most of the recent developments but due to the rapid evolving nature of technology some of the points made might no longer be true. Please verify once before deducing anything conclusively from this page. For further reading, refer the following fantastic papers. I have incorporated a brief description on most of them.

A very good place to build some preliminary idea of the topic as a whole is this paper Spectrum Access Technologies:

The Past, the Present, and the Future by By Jeffrey H. Reed, Jennifer T. Bernhard and Jung-Min (Jerry) Park of Virginia Tech.

1. A. Goldsmith, S. A. Jafar, I. Maric, and S. Srinivasa, “Breaking spectrum gridlock with cognitive radios: An information theoretic perspective,”Proceedings of the IEEE, vol. 97, no. 5, pp. 894-914, May 2009.               (Survey on the information-theoretic capacity results, related bounds, and the degrees of freedom for different cognitive radio network design paradigms (e.g., underlay, overlay, and interweave paradigms))

2. N. Devroye, P. Mitran, and V. Tarokh, “Achievable rates in cognitive radio channels,”IEEE Transactions on Information Theory, vol. 52, no. 5, pp. 1813-1827, May 2006.                                                                             (information-theoretic analysis of achievable rate region for a two-sender and two-receiver cognitive radio interference channel).

3. A. Jovicic and P.Viswanath, “Cognitive radio: An information-theoretic perspective,” IEEE Transactions on Information Theory, vol. 55, no. 9, pp. 3945-3958, September 2009.                                                                 (Rate capacity of cognitive radio-based communications under coexistence conditions where a cognitive radio causes to rate degradation for the primary user communication with single-user decoder.) 

4. M. Gastpar, “On capacity under receive and spatial spectrum-sharing constraints,”IEEE Transactions on Information Theory, vol. 53, no. 2, pp. 471-487, February 2007.                                                                                (Capacity analysis for wireless multiple access and relay networks under received power constraints and geometric spectrum-sharing constraints. The capacity analysis considers cooperation, feedback and dependent sources.)

5.  T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp.116-130, 2009.                                                               (Introduces the concept of multi-dimensional spectrum sensing – explains the various forms of cooperative sensing –discusses models for prediction of primary user behavior - a one stop reference for spectrum sensing.)

6. Y.-C. Liang, Y. Zeng, E.C.Y. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 4, pp. 1326-1337, April 2008.           (Studies the problem of optimizing the sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected.  The sensing-throughput tradeoff is studied for energy detection and cooperative sensing is also considered.)

7. R. Tandra and A. Sahai, “SNR walls for signal detection,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 4-17, February 2008.                                                                                                         (Models the effects of uncertainty in noise and channel fading on signal detection in the context of cognitive radios – coins the term “SNR wall” - the minimum SNR of the signal below which a detector is not able to detect it reliably no matter how large the sensing duration is. The paper analyzes the tradeoff between the performance loss to the primary system and the robustness of signal detection/spectrum sensing.)

8. Z. Quan, S. Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28-40, February 2008.   (Cooperative spectrum sensing - optimal weightings for linearly combining the energies measured at the cognitive radio users such that the probability of detection is maximized with a constraint on the probability of false alarm.)

9. G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio, part I: Two user networks,” and “part II: Multiuser networks,"IEEE Transactions on Wireless Communications, vol. 6, no. 6, pp. 2204-2222, June 2007.   (These two papers introduce the novel concept of cooperation through relaying for spectrum sensing and thus exploit spatial diversity for performance gain in spectrum sensing.)

10. K. B. Letaief and W. Zhang, “Cooperative communications for cognitive radio networks,” Proceedings of the IEEE, vol. 97, no. 5, pp. 878-893, May 2009.                                                                                                           (presents several robust cooperative spectrum sensing techniques based on the concepts of cooperative diversity and multiuser diversity.)

11. A. Ghasemi and E. S. Sousa, “Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs,” IEEE Communications Magazine, vol. 46, no. 4, pp. 32-39, April 2008.                               (overview of the regulatory requirements (e.g., sensing periodicity and detection sensitivity), the major challenges associated with spectrum sensing (e.g., due to uncertainty in radio environment), the commonly used spectrum sensing methods in cognitive radio networks, and the performance trade-off issues in spectrum sensing.)

12. Y. Zeng and Y.-C. Liang, “Eigenvalue based spectrum sensing algorithms for cognitive radio,” IEEE Transactions Communications, vol. 57, no. 6, pp. 1784-1793, 2009.                                                                         (A pioneering work which uses random matrix theory to obtain the probability distributions of the test statistics and find the closed-form expressions for the probability of detection and the probability of false alarm – also proposes spectrum sensing methods which, overcome the noise uncertainty problem, and can be used without requiring the knowledge of signal, channel and noise power.)

Here are some other articles that make for a good introductory read on CR.

1. J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, 1999. 

2. S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005.

3. R. Tandra, S. M. Mishra, and A. Sahai, “What is a spectrum hole and what does it take to recognize one?” Proceedings of the IEEE, vol. 97, no. 5, pp. 824–848, 2009.

4. J. Wang, M. Ghosh, and K. Challapali, “Emerging cognitive radio applications: a survey,” IEEE Communications Magazine, vol. 49, no. 3, pp. 74–81, 2011.

5. Notice of Proposed Rule Making and Order, FCC 03-322, in the Matter of Facilitating Opportunities for Flexible, Efficient, and Reliable Spectrum Use Employing Cognitive Radio Technologies," U.S. Federal Communications Commission, FCC-03-322A1, December 2003.

6. H. Darabi, “A blocker filtering technique for wireless receivers,” in Proceedings of the 54th IEEE International Solid-State Circuits Conference (ISSCC '07), pp. 77–588, February 2007.

I have compiled this list with the help of my Supervisor. For any query, please leave a message.