RSS-Based Positioning in Distributed Massive MIMO under Unknown Transmit Power and Pathloss Exponent

We consider positioning multiple users si- multaneously in a distributed massive multiple-input multiple-output (MIMO) system from the uplink re- ceived signal strength (RSS) information. The trans- mission power of the users, although available from the telecommunication protocol specifications, is con- sidered unknown due to the uncertainty arising from hardware component losses and/or battery voltage fluctuations. The pathloss exponent is also assumed to be unknown due to environmental fluctuations and/or infrequent calibration campaigns. Taking these con- straints into account, we propose a positioning algo- rithm which uses the concept of differential RSS to handle the transmit power uncertainty and derives a linear least squares solution from the ratio-of-distance estimates, which are in turn obtained by assuming that the pathloss exponent is a uniformly distributed random variable. Numerical studies reveal that the proposed positioning algorithm achieves meter-level accuracy even though the transmit power and the pathloss exponent are unknown. The best localization performance is achieved when the differential RSS is calculated using the remote radio head (RRH) with the lowest RSS as the reference. Also, the observed localization performance is consistent across different pathloss exponent values.