Apurva N. Mody

Bio: Apurva N. Mody is an academic researcher from BAE Systems. The author has contributed to research in topics: Cognitive radio & Orthogonal frequency-division multiplexing. The author has an hindex of 20, co-authored 38 publications receiving 1687 citations. Previous affiliations of Apurva N. Mody include Georgia Institute of Technology & Georgia Tech Research Institute.

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

Abstract: 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.

Time and frequency synchronization in Multi-Input, Multi-Output (MIMO) systems

Abstract: In a communication system, and in particular a wireless Orthogonal Frequency Division Multiplexing (OFDM) communication system, the present invention provides systems for synchronizing data transmitted across a channel. The present invention may be used in a Multi-Input, Multi-Output (MIMO) system in which the data is transmitted from any number of transmitting antennas and received by any number of receiving antennas. The number of transmitting and receiving antennas does not necessarily have to be the same. Circuitry is provided for synchronizing the data in both the time domain and frequency domain. Time synchronization involves coarse time synchronization and fine time synchronization. Frequency synchronization involves coarse frequency offset estimation, fine frequency offset estimation, and frequency offset correction.

Synchronization for MIMO OFDM systems

Abstract: 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.

Estimating channel parameters in multi-input, multi-output (MIMO) systems

Abstract: In a communication system, and in particular a wireless Orthogonal Frequency Division Multiplexing (OFDM) communication system, the present invention provides systems for estimating parameters of a channel across which a signal is transmitted. The present invention may be used in a Multi-Input, Multi-Output (MIMO) system in which the data is transmitted from any number of transmitting antennas and received by any number of receiving antennas. The number of transmitting and receiving antennas does not necessarily have to be the same. Circuitry is provided for calculating parameters indicative of the characteristics of the communication channel. Estimates of channel parameters may include channel estimates and noise variance estimates.

Preamble structures for single-input, single-output (SISO) and multi-input, multi-output (MIMO) communication systems

Abstract: A communication system is provided herein for transmitting frames across a channel. The frames may be transmitted in single-input, single-output (SISO) and/or multi-input, multi-output (MIMO) communication systems. One such frame includes at least one training symbol, each having a cyclic prefix and a training block. The time length N I of the training block is equal to an integer fraction I of the time length of a data block, i.e., N I =N/I. Furthermore, the time length G of the cyclic prefix is an integer fraction of the time length N I . For example, G may be equal to N I /4 or 25% of N I . The training symbols provide coarse and fine time synchronization, coarse and fine frequency synchronization, channel estimation, and noise variance estimation.

Broadband MIMO-OFDM wireless communications

Abstract: Orthogonal frequency division multiplexing (OFDM) is a popular method for high data rate wireless transmission. OFDM may be combined with antenna arrays at the transmitter and receiver to increase the diversity gain and/or to enhance the system capacity on time-varying and frequency-selective channels, resulting in a multiple-input multiple-output (MIMO) configuration. The paper explores various physical layer research challenges in MIMO-OFDM system design, including physical channel measurements and modeling, analog beam forming techniques using adaptive antenna arrays, space-time techniques for MIMO-OFDM, error control coding techniques, OFDM preamble and packet design, and signal processing algorithms used to perform time and frequency synchronization, channel estimation, and channel tracking in MIMO-OFDM systems. Finally, the paper considers a software radio implementation of MIMO-OFDM.

Cooperative Communications for Cognitive Radio Networks

Abstract: Cognitive radio is an exciting emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum. Such revolutionary and transforming technology represents a paradigm shift in the design of wireless systems, as it will allow the agile and efficient utilization of the radio spectrum by offering distributed terminals or radio cells the ability of radio sensing, self-adaptation, and dynamic spectrum sharing. Cooperative communications and networking is another new communication technology paradigm that allows distributed terminals in a wireless network to collaborate through some distributed transmission or signal processing so as to realize a new form of space diversity to combat the detrimental effects of fading channels. In this paper, we consider the application of these technologies to spectrum sensing and spectrum sharing. One of the most important challenges for cognitive radio systems is to identify the presence of primary (licensed) users over a wide range of spectrum at a particular time and specific geographic location. We consider the use of cooperative spectrum sensing in cognitive radio systems to enhance the reliability of detecting primary users. We shall describe spectrum sensing for cognitive radios and propose robust cooperative spectrum sensing techniques for a practical framework employing cognitive radios. We also investigate cooperative communications for spectrum sharing in a cognitive wireless relay network. To exploit the maximum spectrum opportunities, we present a cognitive space-time-frequency coding technique that can opportunistically adjust its coding structure by adapting itself to the dynamic spectrum environment.

Over-the-Air Deep Learning Based Radio Signal Classification

Abstract: We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification, and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequency offset, symbol rate, and multipath fading in simulation, and conduct over-the-air measurement of radio classification performance in the lab using software radios, and we compare performance and training strategies for both. Finally, we conclude with a discussion of remaining problems, and design considerations for using such techniques.

MIMO WLAN system

Abstract: A multiple-access MIMO WLAN system that employs MIMO, OFDM, and TDD. The system (1) uses a channel structure with a number of configurable transport channels, (2) supports multiple rates and transmission modes, which are configurable based on channel conditions and user terminal capabilities, (3) employs a pilot structure with several types of pilot (e.g., beacon, MIMO, steered reference, and carrier pilots) for different functions, (4) implements rate, timing, and power control loops for proper system operation, and (5) employs random access for system access by the user terminals, fast acknowledgment, and quick resource assignments. Calibration may be performed to account for differences in the frequency responses of transmit/receive chains at the access point and user terminals. The spatial processing may then be simplified by taking advantage of the reciprocal nature of the downlink and uplink and the calibration.

Channel estimation for wireless ofdm systems

Abstract: Techniques are described for efficiently estimating and compensating for the effects of a communication channel in a multi-carrier wireless communication system. The techniques exploit the fact that the transmitted symbols are drawn from a finite-alphabet to efficiently estimate the propagation channel for multi-carrier communication systems, such systems using OFDM modulation. A transmitter transmits data through a communication channel according to the modulation format. A receiver includes a demodulator to demodulate the data and an estimator to estimate the channel based on the demodulated data. The channel estimator applies a power-law operation to the demodulated data to identify the channel. The techniques can be used in both blind and semi-blind modes of channel estimation.