Multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) receivers with an electronically steerable passive array radiator (ESPAR) antenna can improve bit error rate (BER) performance and obtain additional diversity gain without increasing the number of radio-frequency (RF) front-end circuits. In this scheme, a minimum-mean-square-error (MMSE) channel estimator is employed for obtaining the channel state information. However, its drawbacks include insufficient estimation accuracy and high computational complexity due to the inverse matrix calculation. In this paper, we propose using a low-complexity compressed-sensing-based channel estimation for the MIMO-OFDM receiver with an ESPAR antenna. We also propose a low-complexity detection scheme to further reduce the computational cost required for the detection. The simulation results show that with using the proposed channel estimation method, the BER performance is improved, while its complexity is reduced. The simulations also show that the proposed detection scheme successfully reduces the required computational cost while minimizing the degradation in the BER.

The Third Generation Partnership Project (3GPP) has recently announced its Release 16, which introduces advanced functionalities to support the cellular vehicle to everything (C-V2X) technology. C-V2X allows direct communication between vehicles through the sidelink operation. This appears as an option to DSRC technologies; both are key wireless technologies that play a vital role in implementing and deploying advanced driving applications. This paper presents a thorough review of the available open-source frameworks intended for the performance evaluation of C-V2X protocols and V2X applications. For this, we consider validated OMNeT++-based simulation libraries and frameworks: SimuLTE, OpenCV2X, Artery-C, 5G-Sim-V2I/N, and Simu5G. We focus on the different frameworks' support regarding advanced V2X communications on 5G mobile networks.

Ensuring reliable communication in remote or disaster-affected areas is a technical challenge due to unplanned deployment and mobilization, meaning placement difficulties and high operation costs of conventional telecommunications infrastructures. To address this problem, unmanned aerial vehicles (UAVs) have emerged as an excellent alternative to provide quick connectivity in remote or disaster-affected regions at a reasonable cost. However, the limited battery autonomy of UAVs restricts their flight service time. This paper proposes a communication system based on a tethered UAV (T-UAV) capable of continuous operation through a wired power network connected to a ground station. The communications system is based on low-cost devices, such as Raspberry Pi platforms, and offers wireless IP telephony services, providing high-quality and reliable communication. Experimental tests assessed power consumption, UAV stability, and data transmission performance. Our results prove that the T-UAV, based on a quadcopter drone, operates stably at 16 V and 20 A, ensuring consistent VoIP communications at a height of 10 m with low latency. These experimental findings underscore the potential of T-UAVs as cost-effective alternatives for extending or providing communication networks in remote regions, emergency scenarios, or underserved areas.

Network simulators are used for the research and development of several types of networks. However, one of the limitations of these simulators is the usage of simplified theoretical models of the Packet Error Rate (PER) at the Physical Layer (PHY) of the IEEE 802.11 family of wireless standards. Although the simplified PHY model can significantly reduce the simulation time, the resulting PER can differ considerably from other more realistic results. In this work, we first study several PER theoretical models. Then, we propose a curve fitting algorithm, which is able to obtain a fast and accurate approximation of other PER models. The curve fitting algorithm uses simulated data as input and outputs the coefficients of a simple model that offers a very accurate approximation of the original PER. Finally, we implemented this approximation in the ns-3 network simulator, thus obtaining high realism since now we can select several theoretical PER models or even a more realistic scenario with the effect of the high Peak-to-Average Power Ratio (PAPR) in the signal. The ns-3 results show how the selection of the PER model at the PHY can significantly impact the Packet Loss Rate (PLR) of a scenario composed of a linear chain of several nodes, one of the simplest multi-hop scenarios.