A Joint Tensor Completion and Prediction Scheme for Multi-Dimensional Spectrum Map Construction

Spectrum data, which are usually characterized by many dimensions, such as location, frequency, time, and signal strength, present formidable challenges in terms of acquisition, processing, and visualization. In practice, a portion of spectrum data entries may be unavailable due to the interference during the acquisition process or compression during the sensing process. Nevertheless, the completion work in multi-dimensional spectrum data has drawn few attention to the researchers working in the eld. In this paper, we rst put forward the concept of spectrum tensor to depict the multi-dimensional spectrum data. Then, we develop a joint tensor completion and prediction scheme, which combines an improved tensor completion algorithm with prediction models to retrieve the incomplete measurements. Moreover, we build an experimental platform using Universal Software Radio Peripheral to collect real-world spectrum tensor data. Experimental results demonstrate that the effectiveness of the proposed joint tensor processing scheme is superior than relying on the completion or prediction scheme only

RSS-based indoor localization system with single base station

The paper proposes an Indoor Localization System (ILS) which uses only one fixed Base Station (BS) with simple non-reconfigurable antennas. The proposed algorithm measures Received Signal Strength (RSS) and maps it to the location in the room by estimating signal strength of a direct line of sight (LOS) signal and signal of the first order reflection from the wall. The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office, sampling 21 different locations in the room. It is demonstrated the system can identify user’s real-time location with a maximum estimation error below 0.7 m for 80% confidence Cumulative Distribution Function (CDF) user level, demonstrating the ability to accurately estimate the receiver’s location within the room. The system is intended as a cost-efficient indoor localization technique, offering simplicity and easy integration with existing wireless communication systems. Unlike comparable single base station localization techniques, the proposed system does not require beam scanning, offering stable communication capacity while performing the localization process