Apurva N. Mody

Synchronization for MIMO OFDM systems

This paper proposes a time and frequency synchronization technique for a Q transmit and L receive (Q/spl times/L), MIMO OFDM system. The synchronization is achieved using training symbols which are simultaneously transmitted from Q transmit antennas. The training symbols are directly modulatable orthogonal polyphase sequences. The synchronization algorithm shows satisfactory performance even at a low SNR and in a frequency selective channel. The training sequence structure is specialized such that channel parameters in terms of channel coefficients and noise variance can be estimated once synchronization is achieved.

IEEE Standards Supporting Cognitive Radio and Networks, Dynamic Spectrum Access, and Coexistence

Cognitive radio techniques are being applied to many different communications systems. They hold promise for increasing utilization of radio frequencies that are underutilized today, allowing for improved commercial data services, and allowing for new emergency and military communications services. For example, these techniques are being considered by the U.S. FCC for communications services in unlicensed VHF and UHF TV bands. Although traditionally these techniques are closely associated with software-defined radios, many standards such as WiFi (IEEE 802.11), Zigbee (IEEE 802.15.4), and WiMAX (IEEE 802.16) already include some degree of CR technology today. Further advances are occurring rapidly. IEEE 802.22 will be the first cognitive radio-based international standard with tangible frequency bands for its operation. Standardization is at the core of the current and future success of cognitive radio. Industry stakeholders are participating in international standards activities governing the use of cognitive radio techniques for dynamic spectrum access and coexistence, next-generation radio and spectrum management, and interoperability in infrastructure-less wireless networks. This article provides a review of standardization activities for cognitive radio technologies and comments on prospects and issues for future standardization.

Receiver implementation for a MIMO OFDM system

We present preamble and receiver design for a Q transmit L receive multi-input multi-output OFDM system. Tasks performed by the receiver are synchronization in time and frequency domains, followed by channel estimation and noise variance estimation. An algorithm is developed such that only Q OFDM symbols of a generalized length are required to perform functions of the preamble. Coarse time synchronization is performed using inherent periodicity in the preamble. Frequency offset estimation is then performed in the time domain, followed by residual frequency offset estimation in the frequency domain followed by fine time synchronization. The channel estimation algorithm is developed using least squares principle. Simulations are carried out for a broadband fixed wireless access scenario using a preamble with and without zero-padding of tones in the frequency domain.

Recent advances in cognitive communications

This article describes recent advances in cognitive communications. We combine the concepts of signal processing, communications, pattern classification, and machine learning to make dynamic use of the spectrum, such that the emanated signals do not interfere with the existing ones. Unlike other programs such as neXt Generation communications of the Defense Advanced Research Projects Agency, where radio scene analysis is performed to find the spectrum holes or the white space, we make use of the white, as well as the gray space for non- interfering signal transmission. We examine the possibility of employing machine perception and autonomous machine learning technologies to the autonomous design and analysis of air interfaces. The underlying premise is that a learning module will facilitate adaptation in the standard classification process so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allows knowledge enhancement as more snapshots of data are processed, resulting in improved decisions. Some of the contributions of this project include technological advances in signal detection, feature identification, signal classification, sub-space tracking, adaptive waveform design, machine learning, and prediction.

LDPC Coded OFDM with Alamouti/SVD Diversity Technique

Two transmit two receive space-time processingwith LDPC coding is evaluated for OFDM transmission.The two methodsfor space-time processing are Alamoutis combining and the SVD technique.The channel estimates are calculated and provided tothe diversity combiner, the SVD filters and LDPC decoder.Noise variance estimates areprovided to the LDPC decoder. Using the proposed schemewe can obtain a BER of 10−5 at an SNR of 2.6 dB withspectral efficiency of0.4 bits/sec/Hz and 14.5 dB with a spectral efficiency of 4.2 bits/sec/Hz.

Parameter estimation for OFDM with transmit receive diversity

This paper proposes a channel parameter estimation technique, for Q transmit and L receive (Q /spl times/ L), space time diversity combining scheme. The algorithm is further specialised for the case when 2 transmit and L receive antennas are used along with Alamoutis (see IEEE JSAC, vol.16, no.8, 1998) space time diversity combining technique. OFDM modulation is used and channel parameters are estimated with the help of pilot symbols. The estimated parameters are the channel coefficients and the noise variance. The proposed algorithm provides results that are within 1.5 dB of the theoretical performance curve obtained with a perfect knowledge of the channel. Low computational complexity and good accuracy of the proposed algorithm deems it appropriate for various wireless applications.

Survey of IEEE standards supporting cognitive radio and dynamic spectrum access

Standardization is key to the success of many technologies. Cognitive radio (CR) is no exception. CR techniques are being applied in many different communications systems. They promise to improve the utilization of radio frequencies making room for new and additional commercial data, emergency, and military communications services [1, 2]. In the United States (US) these techniques are being considered by the Federal Communications Commission (FCC) for communications services in unlicensed VHF and UHF TV bands. Similar consideration is being given elsewhere in the world such as with Office of Communications (Ofcom) in the United Kingdom. Standardization is at the core of cognitive radiopsilas current and future successes. Industry stake-holders are participating in international standards activities governing the use of cognitive radio techniques. This article provides a survey of cognitive radio standardization activities, their past and present, and discusses prospects and issues for future standardization.

Machine Learning based Cognitive Communications in White as Well as the Gray Space

This paper describes new ideas and results on machine learning based cognitive communications in White as well as the Gray space. We combine the concepts of signal processing, communications, pattern classification and machine learning to make a dynamic use of the spectrum such that the emanated signals do not interfere with the existing ones. Unlike other programs such as the neXt Generation (XG) communications program of the Defense Advanced Research Projects Agency (DARPA), where radio scene analysis is carried out to find the spectrum holes also known as the White space, we make use of the White as well as the Gray space for non-interfering signal transmission. Our assumption is that a learning module will facilitate adaptation in the signal classification process, so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allow knowledge enhancement as more snap-shots of data are processed, resulting in improved decisions. Use of non-competitive policy set results in zero interference with the already existing signals with a modest increase in the White and Gray space utilization. On the other hand competitive policy set utilizes machine learning to predict the future behavior of the signals which results in more than 90% utilization of spectrum at an expense of some interference due to errors in prediction.

Security in cognitive radio networks: An example using the commercial IEEE 802.22 standard

Security in wireless networks is challenging. Security in cognitive radio networks (CRN) is even more challenging. This is because a CRN consists of cognitive radios (CR) which have many more functions and processes to account for, such as sensing, geolocation, spectrum management, access to the policy database etc. Each of these functions and processes need to be assessed for potential vulnerabilities and security mechanisms need to be provided for protection of not just the secondary users of the spectrum but also the primary users or the incumbents. This paper discusses the potential security vulnerabilities and the remediations for the same in a CRN with an example using a commercial IEEE 802.22 standard. Due to the unique characteristics of the CRs in a CRN, enhanced security mechanisms are required. The security mechanisms in CRN are divided into several security sub-layers which protect non-cognitive as well as cognitive functions of the system and the interactions between the two. This paper describes these security features as incorporated into the IEEE 802.22 standard. It is possible to apply similar security mechanisms for a military CRN.

From Spectrum Agility to Network Agility: Proactive and Adaptive Reconfiguration for Reliable Communication in Tactical Networks

In tactical networks reliable communication is a vital military issue that needs to be resolved. A large number of research efforts are currently focused on providing radios with spectrum agility. However, in many cases, spectrum agility is not enough to achieve reliable communications in tactical networks. Instead, mission success often requires network agility -- cognition across the protocol layers for environmental awareness and autonomous reconfiguration -- which is still an unsolved challenge to our best knowledge, and needs to be addressed. Toward this, we have developed a proactive and adaptive cross-layer reconfiguration (PACR) framework for autonomous network adaptation through network monitoring, proactive prediction, network performance characterization, root-cause analysis, and cross-layer negotiation. Although PACR is generically applicable to any radio network, for validation it has been prototyped and demonstrated on two specific networks -- a commercial off-the-shelf (COTS) hardware testbed using IEEE 802.11 WiFi devices, and a military radio testbed using Warfighter Information Network-Tactical (WIN-T) Local Area Waveform (LAW) radios. Through tests and demonstrations, it has been shown that our solution provides cross-layer adaptation demonstrating spectrum as well as network agility, resulting in mission success through cognitive networking.