Some experimental results of the concept development and practical implementation of an Orthogonal Frequency Division Multiplexing (OFDM) based secondary cognitive link are presented in this paper. The secondary link is realized using Universal Software Radio Peripheral (USRP) N210 platforms. For communication with USRP, we use MATLAB toolbox. Several algorithms are used to overcome transmission problems. Time-synchronization is achieved using a method based on auto-correlation of two sliding windows. Frequency offset estimation is performed using a phase offset between samples in a signal header, comprised of a sinusoid. A channel is estimated using predefined symbols inserted at the beginning of every frame, which enables channel equalization. Also, the cognitive feature of spectrum sensing and changing transmission parameters is implemented. A least-mean-square adaptive filter is introduced to offer time-synchronization error estimation as well as an alternative option for channel equalization.

Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware.

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