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This work revises existing solutions for a problem of target localization in wireless sensor networks (WSNs), utilizing integrated measurements, namely received signal strength (RSS) and angle of arrival (AoA). The problem of RSS/AoA-based target localization became very popular in the research community recently, owing to its great applicability potential and relatively low implementation cost. Therefore, here, a comprehensive study of the state-of-the-art (SoA) solutions and their detailed analysis is presented. The beginning of this work starts by considering the SoA approaches based on convex relaxation techniques (more computationally complex in general), and it goes through other (less computationally complex) approaches, as well, such as the ones based on the generalized trust region sub-problems framework and linear least squares. Furthermore, a detailed analysis of the computational complexity of each solution is reviewed. Furthermore, an extensive set of simulation results is presented. Finally, the main conclusions are summarized, and a set of future aspects and trends that might be interesting for future research in this area is identified.

To circumvent the prohibitive complexity of linear minimum mean square error detection in a massive multiple‐input multiple‐output system, several iterative methods have been proposed. However, they can still be too complex and/or lead to non‐negligible performance degradation. In this letter, a Chebyshev acceleration technique is proposed to overcome the limitations of iterative methods, accelerating the convergence rates and enhancing the performance. The Chebyshev acceleration method employs a new vector combination, which combines the spectral radius of the iteration matrix with the receiver signal, and also the optimal parameters of Chebyshev acceleration have also been defined. A detector based on iterative algorithms requires pre‐processing and initialisation, which enhance the convergence, performance, and complexity. To influence the initialisation, the stair matrix has been proposed as the first start of iterative methods. The performance results show that the proposed technique outperforms state‐of‐the‐art methods in terms of error rate performance, while significantly reducing the computational complexity.

Digitalization in education sphere is the new paradigm of high technologies development. The article proves that digital technologies are relevant and widely used in various areas of society: management, economic relations, science and education. However, digitalization of the educational process is of particular importance, because the several factors such an advance of the quality and relevant experience in this sphere. Today, important transformation processes are taking place in the field of education: electronic textbooks, Internet portals, databases of information needs are spreading, systems of online courses and distance learning are actively developing.

In this paper, we propose a new approach based on convex optimization to address the received signal strength (RSS)-based cooperative localization problem in wireless sensor networks (WSNs). By using iterative procedures and measurements between two adjacent nodes in the network exclusively, each target node determines its own position locally. The localization problem is formulated using the maximum likelihood (ML) criterion, since ML-based solutions have the property of being asymptotically efficient. To overcome the non-convexity of the ML optimization problem, we employ the appropriate convex relaxation technique leading to second-order cone programming (SOCP). Additionally, a simple heuristic approach for improving the convergence of the proposed scheme for the case when the transmit power is known is introduced. Furthermore, we provide details about the computational complexity and energy consumption of the considered approaches. Our simulation results show that the proposed approach outperforms the existing ones in terms of the estimation accuracy for more than 1:5 m. Moreover, the new approach requires a lower number of iterations to converge, and consequently, it is likely to preserve energy in all presented scenarios, in comparison to the state-of-the-art approaches.

In the last years, the evolution of digital communications has been harnessed by medical applications. In that context, wireless communications are preferable over wired communications, as they facilitate the work of health technicians by reducing cabling on the stretchers. However, the use of wireless communications is challenging, especially when high data rates and low latencies are required. In those scenarios, multiple-input multiple-output (MIMO) techniques might have an important role, thanks to the high capacity gains that they can exhibit, which ideally increase with the MIMO size. In this work, we study the propagation scenario of a typical medical laboratory through ray-tracing techniques. By taking into account the derived channel model, we study the potential of MIMO techniques in an IEEE 802.11ax environment. Through a set of performance results regarding the system capacity, we show that the MIMO gains might not be as high as supposed in the medical laboratory, being far from the ideal scenario. Therefore, the large data rates required by the modern medical imaging applications might only be achieved with a combination of MIMO systems and large bandwidths.

Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.

Traditional estimation methods face challenges in adverse conditions in systems such as Multiple Input Multiple Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM). To overcome those challenges, Deep Learning (DL) approaches have been proposed as an interesting alternative, thanks to their ability to capture channel features without much complexity. This paper presents a hybrid approach that combines DL with traditional estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE), which we designate as DL-Enhanced. Our main innovation is a phase-preserving mechanism that maintains critical phase information frequently degraded in purely data-driven approaches. We evaluate the proposed technique considering MIMO-OFDM systems considering 3GPP Clustered Delay Line Model C (CDL-C) channels. Simulation results demonstrate that our method outperforms conventional techniques at high-SNR levels, thanks to neural network-based feature extraction and adaptive processing.

The large intelligent surface (LIS) concept represents an architectural advance for enhancing the performance of 6G wireless communication systems. In this work, we address the problem of jointly selecting active panels and associating terminals to outputs of such active panels in a panel-based LIS framework to maximise the minimum signal-to-interference-and-noise ratio (SINR) across all terminals. Due to the nature of the mixed-integer linear programming (MILP) formulation, we propose an alternative approach based on a genetic algorithm (GA) that efficiently explores the solution space through tailored crossover via column swapping and adaptive mutation. We compare the GA’s performance against the CPLEX solver under various configurations and time constraints. The performance results show that the GA provides competitive solutions with reduced computational complexity, showcasing its potential for scalable LIS implementations with complex resource allocation.

This work investigates the performance of distributed beamforming in Wireless Sensor Networks (WSNs), focusing on the impact of node position errors. A comprehensive simulation testbed was developed to assess how varying network topologies and position uncertainties impact system performance. Our results reveal that distributed beamforming in the near-field is highly sensitive to position errors, resulting in a noticeable degradation in performance, particularly in terms of Bit Error Rate (BER). Cramer–Rao Lower Bound (CRB) was used to analyse the theoretical limitations of position estimation accuracy and how these limitations affect beamforming performance. These findings underscore the critical importance of accurate localisation techniques and robust beamforming algorithms to fully realise the potential of distributed beamforming in practical WSN applications.

Pre-processing associated with massive multiple input-multiple output (MIMO) systems can lead to signals with high envelope fluctuations, which are very prone to nonlinear effects, especially when massive MIMO schemes are combined with orthogonal transform multiplexing (OFDM) modulations. If the nonlinear characteristics that affect a given system are known, we can design appropriate receivers that take into account the nonlinear effects introduced by the transmitter. Cubic systems are particularly important, not only because they can approximate many nonlinear effects (e.g., due to the power amplifier or clipping effects), but also because many more complex nonlinear characteristics in communication schemes can be replaced by equivalent lower-order nonlinear characteristics in general, and cubic characteristics in particular. To compensate the effects at the receiver side (e.g., by using the so-called Bussgang receivers), we need to estimate the nonlinear operation that was introduced at the transmitter, and this should be done blindly, without the need of training symbols. The paper contains a description of a mathematical approach for modeling and identification of nonlinear kernels in cubic systems. Based on theoretical tools of HOC in cubic systems, we build a new formula which relates the second- and fifth-order cumulants. Our performance results indicate that the proposed approach allows an accurate identification, yielding the desired kernels via fifth-order cumulants, and ensures a very good convergence, outperforming existing adaptive methods. This is achieved blindly, by exploiting the maximum information of the output system, making it suitable for many practical nonlinear effects.