This paper provides a theoretical performance evaluation of the downlink of asynchronous orthogonal frequency division multiplexing (OFDM) and filter bank based multicarrier (FBMC) cellular radio communication systems. An accurate derivation, for the interference caused by the timing synchronization errors in the neighboring cells, is developed. The multipath effects on the interfering and desired signal are also considered. Based on computing the moment generating functions of the interference power, exact expressions are derived for average error rates of OFDM and FBMC systems considering the frequency correlation fading in the case of block subcarrier assignment scheme.

Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.

In this paper, we consider an IoT dedicated network corresponding to a non licensed LoRa Low Power Wide Area Network. The LoRa network operates in the unlicensed 868 MHz band within a total bandwidth of 1 MHz divided into 8 orthogonal channels of 125 kHz each. Despite the high level of interference, this network offers long range communications in the order of 2 to 5 km in urban areas and 10 to 30 km in rural areas. To efficiently mitigate this high level of interference, LoRa network essentially relies on a Chirp Spread Spectrum (CSS) modulation and on repetition diversity mechanisms. The LoRa CSS modulation spreads the signal within a band of 125 kHz using 6 possible spreading factors (from 7 to 12) to target data rates (starting from 5 kbps for the closest node to 300 bps for the furthest ones). The repetition diversity mechanisms enable the data recovery when the transmission is subject to bad channel conditions or/and high interference levels. Although the CSS modulation protects edge-cell’s devices from the high level of interference induced by nodes in the proximity of the gateway, it fails to protect nodes at the edge of a given SF region and several trials are required to recover the packet. In this paper, we propose an adaptive multi-channels allocation policy that attributes multiple adjacent channels of 125 kHz for nodes situated at the edge of SF zones. We study the impact of this adaptive sub-band allocation on the gateways’ intensities, the rate distribution and the power consumption. Our results are based on a statistical characterization of the interference in the network as well as the outage probability in a typical cell.

In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented and compared in terms of localization accuracy and complexity. Simulation results demonstrate that Adam outperforms all other algorithms in terms of localization accuracy and computational complexity.

We investigate the blind identification and separation of underdetermined linear instantaneous mixtures with a single sensor and an arbitrary known number of sources with finite known support and uniform distribution. We propose channel estimators based on the high-order statistics of the received signal and on the rotational symmetries of the source constellations. Explicit expressions for distinct and equal rotation orders are derived. The proposed estimators are used as initializers for the iterative least squares with enumeration algorithm to enhance its convergence properties. Simulation results on several high interference two-user wireless communication scenarios show that our initialization strategy achieves a substantial performance gain compared to other traditional initializers.