No abstract

Spatial modulation (SM) is one of the probable candidates to be utilised in the fifth generation of wireless networks due to its power and spectral efficiencies. Since only one active transmit antenna exists in spatial modulation, inter‐channel interferences are avoided and the number of radio frequency (RF) chains is reduced. However, channel estimation is a major challenge in spatial modulation communication systems. In this study, a novel blind signal and channel estimation method for spatial modulation‐based multi‐input multi‐output communications, based on the well‐known Expectation‐Maximisation algorithm, is presented. This blind detector requires only a few pilot symbols at the beginning of the transmission to resolve the inherent phase and permutation ambiguities. Simulation results demonstrate that the proposed detector performs very similar to the optimum detector with full channel state information and outperforms existing blind detectors.

Massive spatial modulation (SM) multi‐input multi‐output (MIMO) system is a promising technique in uplink communications for future mobile communication, due to its power and spectral efficiencies. However, these systems, like other MIMO communications, face the challenge of channel estimation. Pilot‐based channel estimation methods result in data rate reduction as well as imposing additional complexity at the receiver side, which is intensified in time‐varying channels. Therefore, blind channel estimation is an alternative way to avoid pilot transmission. Considering a time‐varying channel and taking the advantages of machine learning techniques, blind channel estimation and data detection for SM uplink multi‐user massive MIMO communications is presented in this article. In this regard, a blind multi‐user detection based on the expectation‐maximization (EM) algorithm, called BMU‐EM, is presented first; however, this detector suffers from high computational complexity. In order to mitigate the complexity problem, a blind multi‐user detection based on sparse Bayesian learning and expectation‐maximization, called BMU‐SBEM, is proposed. Simulation results show that the BMU‐SBEM detector performs almost close to the optimum detector where the perfect channel information is available. Furthermore, the computational complexity of the BMU‐SBEM detector increases linearly with the number of users, making it suitable for massive communications in time‐varying channels.

UWB is one of the main technologies for localization in IoT applications. For range-based localization, it is crucial to secure UWB ranging by a suitable mechanism. Thereby, trustworthiness measures appear to be specifically attractive for constraints posed by IoT applications. In this work, a measure for data trustworthiness of the double-sided two-way-ranging estimate is proposed. The measure relies on features obtained from the channel impulse response and applies two machine learning techniques, namely a modified k nearest neighbour and a modified random forest, to infer an error correction term together with a trust value. To increase the number of trusted measurements, a more accurate stepwise labeling of the training data is used, and an optimum combination scheme of the resulting stepwise trust values is proposed. The results on experimental data show an improvement of 34% RMSE on the test set with 61% of the measurements considered trustworthy.