To make use of the sparsity property of broadband multipath wireless communication channels, we mathematically propose anlp-norm-constrained proportionate normalized least-mean-square (LP-PNLMS) sparse channel estimation algorithm. A generallp-norm is weighted by the gain matrix and is incorporated into the cost function of the proportionate normalized least-mean-square (PNLMS) algorithm. This integration is equivalent to adding a zero attractor to the iterations, by which the convergence speed and steady-state performance of the inactive taps are significantly improved. Our simulation results demonstrate that the proposed algorithm can effectively improve the estimation performance of the PNLMS-based algorithm for sparse channel estimation applications.

Enhancing Internet of Things (IoT) communications through Reconfigurable Intelligent Surfaces (RIS) necessitates novel approaches that go beyond the conventional deployment of passive elements. This paper introduces an efficient method to enhance IoT system performance by combining active and passive functionality in RISs: an active and passive integrated approach. One identified enhancement area is efficiently managing signal throughput and exchanges in IoT systems, which is essential for supporting numerous devices. For this challenge, we propose a thorough process model that describes channel gains, develops a nonlinear energy harvesting, and identifies device-to-device communication schemes without violating information causality. The innovative component of our work is the utilization and strategic application of RIS panels that benefit from the advantages of active and passive components synergism to solve thermal noise issues and optimize signal reflection and transmission. An advanced optimization mechanism is developed based on mixed-integer nonlinear programming: an enabling approach between performance efficiency and maximum service utilization. Our simulation analysis show that developed RIS panels optimize IoT system performance and surpass existing performance indicators in conventional RIS-less systems.

Unmanned aerial vehicle (UAV)-based communication system design has already attracted substantial interest due to UAV flexibility in deployment, cost effectiveness, and in-built line-of-sight air-to-ground channels. However, there is a persistent issue of security threats associated with the broadcast nature of UAV: physical layer security (PLS) can be introduced to enhance the secrecy performance in that regard. In the system proposed here, the combined effect of 3D fractional-order Liu chaotic system and 3D fractional-order Li chaotic system is introduced to enhance the PLS of the UAV-to-ground communication network. In addition, to reduce multi-user interference (MUI), efficient orthogonal variable spreading factor (OVSF) codes are integrated with a zero forcing (ZF) scheme. Moreover, to improve bit error rate (BER), a combined MUI signal discrimination and ZF signal detection scheme is introduced, strengthened by various channel coding techniques with multi-user beamforming weighting. The numerical outcomes establish the efficacy of the proposed system in terms of PLS and improvement of data rate with signal-to-interference-plus-noise ratio. This approach achieved an out-of-band emission reduction of 119 dB. Furthermore, an improved BER of $1 \times 10^{-4}$ is achieved in 16-QAM for a signal-to-noise ratio of 6 dB.

We propose a smooth approximationl0-norm constrained affine projection algorithm (SL0-APA) to improve the convergence speed and the steady-state error of affine projection algorithm (APA) for sparse channel estimation. The proposed algorithm ensures improved performance in terms of the convergence speed and the steady-state error via the combination of a smooth approximationl0-norm (SL0) penalty on the coefficients into the standard APA cost function, which gives rise to a zero attractor that promotes the sparsity of the channel taps in the channel estimation and hence accelerates the convergence speed and reduces the steady-state error when the channel is sparse. The simulation results demonstrate that our proposed SL0-APA is superior to the standard APA and its sparsity-aware algorithms in terms of both the convergence speed and the steady-state behavior in a designated sparse channel. Furthermore, SL0-APA is shown to have smaller steady-state error than the previously proposed sparsity-aware algorithms when the number of nonzero taps in the sparse channel increases.

To enhance redundancy and ensure continuous connectivity in conventional communication systems, a method utilizing an on-demand forwarding signal transmitter enabled by a reflective intelligent surface (RIS) serves as a flexible and adaptive solution, particularly for outdoor communications. However, managing and regulating these RISs in dynamic environments present significant challenges, particularly in large-scale network systems. Software-defined networking presents a compelling method for managing networks in contrast to traditional networking, delivering automated, centralized, and intelligent control. In this paper, we introduce a software-defined RIS network architecture that aims to address the expected demands of future wireless communication, focusing on high network availability, the improved convergence of connectivity, and improved performance. The results of this study indicate that the achievable data rate is increased by approximately 35%, energy efficiency is improved by about 15%, packet processing time is nearly 2.32% shorter, server utilization is improved by approximately 48%, pathloss is minimized by roughly 64%, spectral efficiency is improved by nearly 84.2%, service rate is improved by approximately 38.7%, and packet arrival rate is exacerbated by approximately 3.3%.