Modern wireless sensor networks (WSNs) for Internet of Things (IoT) applications require low-complexity algorithms for positioning, due to the large number of nodes with low power consumption. Thus, simple received signal strength indicator (RSSI) based ranging techniques represent an attractive option for low power systems such as LoRa ones. However, interactions of the RSSI with a real-world environment are difficult to predict and often lead to significant errors in the localization process. Based on this, a novel algorithm is proposed to improve the RSSI ranging output by a Wiener-based method. According to the free-space path loss model, the distance is expressed as an exponential function of the collected RSSI measures, considering, during the training and calibration phase, the channel model information, i.e., the received power at 1 m and the loss exponent. The presented algorithm minimizes the distance logarithm error, instead of interpolating the free-space path loss model as in common solutions, resulting in a more precise ranging and positioning. Moreover, we also study the possibility to suitably combine the diversity information coming from the LoRa physical layer modes, corresponding to different data rates and bandwidths. The performance of the proposed algorithm is evaluated by using both simulated and real experimental data sets, proving the effectiveness of the presented solution compared to existing RSSI-based methods.

Applications of clustering and neural network techniques to channel equalization have revealed the classification nature of this problem. This paper illustrates an implementation of a global system for mobile communications (GSM) receiver in which channel equalization and demodulation are realized by means of the nearest neighbor (NN) classifier algorithm. The most important advantage in using such techniques is the significant reduction in terms of the computational complexity compared with the maximum likelihood sequence estimation (MLSE) equalizer. The proposed approach involves symbol-by-symbol interpretation and the knowledge of the channel is embedded in the mapping process of the received symbols over the symbols of the training sequence. This means that no explicit channel estimation need be carried out, either with correlative blocks or using neural networks thus speeding up the entire process. The performance of the proposed receiver, evaluated through a channel simulator for mobile radio communications, is compared with the results obtained by means of a 16-state Viterbi algorithm and other suboptimal receivers. It is shown that the presented algorithm increases the bit error rate (BER) compared with the MLSE demodulator, but the performance degradation, despite the simplicity of the receiver, is kept within the limits imposed by the GSM specifications

Intra-body communication (IBC) will foster personalized medicine by enabling interconnection of implanted devices. Communication takes place through energy-efficient technologies such as capacitive coupling (CC) and galvanic coupling (GC); however, their modeling is still incomplete. This paper tackles characterization of the human body channel using impulse response, including a first-ever comparison of CC and GC in both wearable and implantable configurations. Experimental data are leveraged to evaluate the measured impulse response in ex-vivo chicken tissue and in-vivo human tissue in a frequency range up to 100 kHz. Pseudorandom noise (PN) sequences are transmitted in baseband and a correlative channel sounding system is implemented. Experimental results demonstrate that the channel is relatively flat in the frequency range of interest, thus offering the opportunity to simplify the design of an IBC transceiver. The relationship between the channel responses and the transmitter-to-receiver distance is also examined using linear correlation, and two regression models are developed. The results show that CC channels are not affected by distance within the range of investigation, while a negative relationship is found for GC channels. Finally, experiments reveal that implantable CC with isolated ground -not deeply investigated yet- is a very promising solution for IBC.

Intra-body communication (IBC) is a novel key research area that will foster personalized medicine by allowing in situ and real time monitoring in daily life. In this work, the energy efficient galvanic coupling (GC) technology is used to send data through intra-body links. A novel sound card-based GC testbed is designed and implemented, whose main features are: (i) low equipment requirements since it only employs two ordinary PCs with sound card support and Matlab software, (ii) high flexibility since all the parameters setting may be easily modified through the PC control panel and Matlab programs, (iii) real time physiological data transmissions, and (iv) almost error free communication by developing specific physical (PHY) layer techniques, which are implemented and tested with a real chicken tissue in the experimental evaluation. A signal to noise ratio (SNR) calculation is also proposed with the twofold purpose to be used for frequency offset compensation and as metric to evaluate the proposed architecture. The developed GC testbed may be easily replicated by the interested research community to carry out simulation-based experiments, thus fostering new research in this field. Moreover, the Matlab source code of the proposed GC transceiver is freely available online on Code Ocean.