The Generalized Memory Polynomial (GMP) model is one of the most commonly used in Digital Predistortion (DPD) to compensate for the nonlinearities and memory effects of Power Amplifiers (PAs). A very difficult, yet crucial, aspect of behavioral modeling is model dimensioning, i.e., determining nonlinear orders and memory depths. In this paper, we propose an algorithm based on Hill-Climbing (HC) heuristic to search for a GMP model structure which provides the best trade-off between modeling accuracy and complexity with different search criteria. Algorithm performance and convergence are evaluated for two different search criteria. Some optimization methods of the algorithms are then proposed to reduce the execution time by controlling its search path. The algorithm has been evaluated using real measurements from two different Doherty PAs. The solutions of the algorithm are proved robust in linearization of PA on the test bench. The results show that the proposed algorithm allows to decrease the searching time by a factor typically greater than $10^{4}$ compared with exhaustive search.

Wireless underground sensor networks (WUSNs) is an emerging area of research. They are used in many applications from intelligent irrigation to security and assisted navigation. WUSNs mainly use narrowband systems working in the frequency range below 1 GHz and, therefore, suffer from a poor localization capability and large-sized antennas. The use of impulse radio ultrawideband (UWB) technique allows alleviating these drawbacks. In this letter, the impact of soil on UWB underground-to-aboveground communication link is studied experimentally. More specifically, the effects of the orientation of the buried antenna and burial depth and soil moisture are investigated. The experimental results show that, at 7 GHz, additional attenuations of 10, 34, and 40 dB are noted for depointing the underground antenna by 90°, increasing the depth from 0 to 30 cm and raising the soil moisture from 0% to 20%, respectively. Burial depth lower than 30 cm or soil moisture lower than 20% allow getting acceptable received signal strength (superior to -100 dBm) for communication and localization, particularly in the UWB low band (3.1-5 GHz). The obtained results bring out the feasibility of UWB WUSN.

This letter unfolds a digital predistortion (DPD) technique that improves the linearity of limited range mobile front haul links for the contemporary long-term evolution (LTE) and future (5G) networks. In particular, the proposed technique is applied to radio-over-fiber links based on single-mode (SM) vertical cavity surface emitting lasers emitting at 850 nm and standard SM fibers. Both the memory and generalized memory polynomial models are exploited for the predistorter and identified by utilizing indirect learning architecture. The impact of the DPD technique is observed by the link performance improvement in terms of normalized mean square error and adjacent channel power ratio, referring to complete LTE frames of 10 ms occupying 5-MHz bandwidth and having 64-quadrature amplitude modulation format. Furthermore, the effectiveness of the DPD approach, when varying input power levels, is investigated. The experimental results demonstrate the capability of the proposed DPD technique to achieve promising linearization performance.

In the present letter, a new adaptive threshold comparison approach for time-of-arrival (TOA) estimation in ultra wideband (UWB) signals is proposed. This approach can be used with non-coherent energy detector receivers in UWB systems. It exploits the idea of cell averaging constant false alarm rate (CA-CFAR) used in radar systems, where the threshold changes every energy block. The performance of several approaches are compared via Monte Carlo simulations using the CM1 channel model of the standard IEEE 802.15.4a. Both simulation results and comparisons are provided highlighting the effectiveness of the proposed approach.

Existing localization technologies face several challenges indoors because of their sensitivity to the environment. In this paper, we present an indoor localization system based on Low Frequency (LF) Radio-Frequency Identification (RFID) as a reliable and low-cost solution, which is less affected by challenging indoor conditions. The presented system makes use of LF (125 kHz) magnetic fields for reliable localization in multipath and Non-Line-of-Sight (NLOS) environments. The objective of this paper is to analyze two-dimensional (2D) positioning estimation performance of LF-RFID based localization system in comparison with the localization systems based on Ultra-High-Frequency (UHF) and Ultra-Wideband (UWB) technologies. We present results for 2D localization tests for these three systems in a challenging indoor environment of area 315 square meters. The presented system, which is implemented using off-the-shelf components, achieves a mean positioning error of 1.53 m with a standard deviation of 0.91 m for 352 position estimations while keeping the positioning error below 2.82 m for 90% of the cases.