Debjani Mitra

Improved power loading scheme for orthogonal frequency division multiplexing based cognitive radio

A subcarrier power allocation scheme for orthogonal frequency division multiplexing (OFDM) based cognitive users has been proposed for spectrum sharing with primary users (PU) allowing secondary users (SU) to coexist in same as well as adjacent frequency bands by underlay and interweave approaches respectively. The SU sum capacity of reliable communication has been maximised under the constraints of total power, each sub-channel power and PU aggregate interference limits. Based on the quality of channel gains and spectral distance from PU bands, the powers of subcarriers adjacent to PU bands are suitably controlled till the interference constraint of PUs are satisfied. Controlled subcarrier deactivation achieved by augmenting this ‘n-adjacent’ power redistribution step to traditional OFDM water-filling gives a powerful tool for efficiently maintaining high SU sum capacity without exceeding PU interference tolerance limit for a wide range of total power budget. Different water levels get assigned for the adjacent and non-adjacent subcarrier groups. In terms of SU capacity and PU interference, the proposed algorithm outperforms some of the existing schemes having almost same complexity as the proposed one. The improvement is significant when the adjacent subcarriers channel gains are relatively ‘good’ or ‘bad’ compared with the non-adjacent ones.

Development of a Sensor Fusion Strategy for Robotic Application Based on Geometric Optimization

Fusion of multi-sensor information is an important technology, which is growing exponentially due to its tremendous application potential in many areas. Effective fusion of data from sensors is very critical in increasing an intelligent systems capability to accomplish complex tasks. Appropriate fusion technologies are needed to be developed specially when a system requires redundant sensors to be used. More the redundancy in sensors, more is the computational complexity for controlling the system and more is its intelligence level. This research presents a strategy developed for multiple sensor fusion, based on geometric optimization. Each sensors uncertainty has been modeled using classical Lagrangian optimization techniques. However, the uniqueness and effectiveness of the present technique lies on the fact that starting from the optimized value as initial estimate the accuracy of the sensory information has further been improved up to any pre defined bounded range, by developing two architectures – FFA (fission–fusion architecture) and FDD (fusion in differential domain). Sufficient evidences and analyses have been provided in the paper to show its effectiveness in various applications.

Gradient-Based Real-Time Spectrum Sensing at Low SNR

One of the main steps of enabling dynamic spectrum allocation for cognitive radios is spectrum sensing. Two critical problems in sensing a wide bandwidth are variations in noise power and degraded performance at a low SNR. The common FCME approach may lead to missed detection in sensing a wideband. The improved gradient-based energy detection method proposed here satisfactorily addresses the problem of choosing an appropriate threshold in such a scenario. The sensing bandwidth is divided into smaller subbands, and the gradient of the mean of subbands (GMSB) is computed and summed over past data samples in each subband. Applying a histogram-based threshold on the GMSB enables the accurate distinction of signal and noise without perfect knowledge of the noise floor. The theory of the proposed algorithm has been validated with numerical simulations over a wide range of low SNR values at different signal bandwidths. The detection performance of the approach is more effective than the FCME. The technique is tested over real-time wideband sensing in the GSM and CDMA bands.

FIS based cognitive radio scheduling

Cognitive radio is the latest technology paradigm enabling dynamic spectrum access for exploiting unused spectrum resources, cross-layer optimization and achieving optimal system performance using learning and reasoning techniques. This paper derives a generalized decision surface for opportunistic spectrum scheduling by cognitive users in licensed spectrum using Fuzzy Inference System. The proposed FIS has incorporated the PU-SU distance and signal strength inputs in a logical reasoning to balance the tradeoff between spectrum sensing accuracy and interference to PU. The influence of the shape of the MF on the dynamics of the variation in decision making has been evaluated for a spectrum sharing model using different secondary user parameters as input: signal power, spectrum utilization efficiency, degree of mobility and its distance to the primary user. The capabilities of two different rule base frameworks have been compared to develop an optimized FIS structure in terms of the type of membership function for predicting the best scheduled set of secondary users. Simulation results show the feasibility of evaluating CR scheduling with the evaluated possibility factors from built-in fuzzy inference decision surfaces. The superiority of the trapezoidal MF and the distance-factor trade-off inclusive 81 rule base in this regard is also clearly established.

A Compact Dual-Band Reconfigurable Open-End Slot Antenna for Cognitive Radio Front End System

A compact dual-band frequency reconfigurable microstrip-line fed open-end omnidirectional slot antenna is proposed in this paper suited for cognitive radio front end system. The antenna is capable of frequency switching at different frequency bands by changing the resonance length using two PIN diodes. The resonance frequency is tuned by a single varactor diode, placed at a certain location along the slot. The proposed antenna has a wide tuning range in the dual bands: 1.57 to 3.1 GHz and 3.8 to 5 GHz. The designed antenna has compact size of 40 × 40 mm 2 . An approximate transmission line model of the proposed antenna is derived to calculate the proper positioning of the diodes, and the design has been verified through numerical simulations and measured results.

Performance Comparison of Hmm Discrete Channel Modeling in CDMA Links

The performance comparison of two Markov models namely the Baum Welch Algorithm based HMM and Semi Hidden Markov Model has been evaluated for a DS-CDMA link in this work. The simulation includes the effects of AWGN, Multipath and Multiple Access Interference. Validation includes a comparison of the runlength statistic for the original and regenerated error sequence from estimated models. The SHMM approach is seen to be capable of developing a more accurate model as compared to the BWA. The length of number of symbols processed at a time does not affect the accuracy in both the methods.

Band-Reconfigurable Monopole Antenna for Cognitive Radio Applications

ABSTRACT A single-port compact planar integrated ultra-wideband (UWB)/frequency-reconfigurable narrowband monopole antenna is proposed. Reconfigurability is achieved by inserting a varactor-diode tuned bandpass filter into the feed line of the UWB antenna. The antenna operation is changed from wideband to narrowband by activating PIN diode switches in the structure. Good tunability obtained within a wide sensing band of 2.2–6 GHz is significant for cognitive radio applications.

Order estimation of HMM Discrete Channel Model for OFDM systems

OFDM systems popularly being used in current high data rate wireless access networks and international standards are practically implemented using FEC and interleaving schemes whose performance evaluation and analysis can greatly benefit from Discrete Channel Models developed for the underlying channel. In this paper the order i.e., minimum number of states required for the Hidden Markov discrete channel modelling of a typical OFDM system has been estimated by extensive simulation. The input sample period (Ts) of the signal on which the channel acts, is seen to play a vital role in the HMMs for the OFDM systems. The models which have been completely derived for 4 different values of input sample period strongly indicate the need for specific evaluation of HMM channel models for different wireless standards. The Baum-Welch Algorithms (BWA) is used to train the HMM and to generate the HMM based error patterns which are compared with the original in terms of Autocorrelation functions and distribution probabilities of error intervals.

Multi-target trackers using cubature Kalman filter for Doppler radar tracking in clutter

A major feature of the Gaussian mixture probability hypothesis density (GM-PHD) filter is that it does not require any measurement-to-track association to complete its update step. This, according to the authors, should constitute significant advantage over conventional data-association based methods, especially in presence of high false-alarm rate, frequent miss-detections and targets in close proximity. To test this hypothesis, a multi-target tracking (MTT) problem using Doppler radar is considered, where the performance of GM-PHD algorithm is compared against six data-association based MTT filters in aforementioned adverse tracking conditions. To handle the non-linearity due to Doppler, cubature Kalman filter (CKF) is used in the framework of all MTT algorithms. Detailed mathematical framework of a new non-linear variant of GM-PHD using CKF has been derived using fundamental principles of non-linear Bayesian filtering. It is named as CK-GM-PHD. CK-GM-PHD is formulated using approximated Gaussian mixture assumption and follows track-oriented approach. Cubature integration method is used to numerically compute mean and covariance of components in the Gaussian mixture. Simulation results support the hypothesis by revealing substantial performance improvement of CK-GM-PHD algorithm over conventional data-association based approaches while tested in moderate to heavy clutter rate with lower detection probability and closely spaced target scenarios.

Subspace-based spectrum guarding

Cognitive radio is regarded as the most promising technology for driving the future development of next-generation wireless networks, increasing spectrum efficiency by dynamic spectrum access. Allowing white spaces to be used in a deregulated manner exposes the radio spectrum to anomalous usage, which in turn may intentionally or unintentionally cause interference to primary users (PUs) as well as secondary users (SUs). In this paper we propose a subspace-based spectrum guarding detector (SSGD) to address the security of unauthorized spectrum usage. In particular, we consider the scenario where ongoing authorized transmissions by PUs have to be preserved from concurrent unauthorized emissions in the same frequency bands. The detector operates in two phases: a training phase where SSGD collects samples of the authorized transmissions, and a detection (on-line) phase where the spectrum is guarded against anomalous emissions. The performance of SSGD is analyzed in terms of detection and false alarm probabilities highlighting the effects of the propagation scenario, the PU characteristics, the signal-to-noise ratio (SNR) and the duration of both training and on-line phases. Numerical results show that in practical scenarios SSGD provides a remarkable SNR improvement of several dB over the conventional energy detector (ED).