Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

— A promising massive multiple input multiple output (M-MIMO) system is required to meet the growing need for highly traffic data, highly-resolution of streaming video, and intelligent communication on the fifth-generation wireless networks (5G). M-MIMO systems are essential for the optimization of the trade between energy efficiency (EE), throughput (R), and spectral _efficiency (SE) in wireless 5G networks. M-MIMO system architecture is proposed in this paper to enhance the trade-off between energy efficiency and uplink and downlink throughput at the optimum EE. Furthermore, using linear precoding techniques such as M MMSE, RZF, ZF, and MR, the EE-SE trade-off is optimized for uplink and downlink (M-MIMO) systems. The analysis of simulation results proved that throughput (R) is enhanced by increasing the number of antennas at optimum EE. After that, the proposed trading scheme is optimized and improved using M_MMSE compared to RZF, ZF. Finally, the results prove that M_MMSE gives the optimum trade-off between EE and R at the proved optimum ratio between the number of active antennas and the number of active users UE

Noise Effects on a Proposed Algorithm for Signal Reconstruction and Bandwidth Optimization

The development of wireless technology in recent years has increased the demand for channel resources within a limited spectrum. The system\u27s performance can be improved through bandwidth optimization, as the spectrum is a scarce resource. To reconstruct the signal, given incomplete knowledge about the original signal, signal reconstruction algorithms are needed. In this paper, we propose a new scheme for reducing the effect of adding additive white Gaussian noise (AWGN) using a noise reject filter (NRF) on a previously discussed algorithm for baseband signal transmission and reconstruction that can reconstruct most of the signal’s energy without any need to send most of the signal’s concentrated power like the conventional methods, thus achieving bandwidth optimization. The proposed scheme for noise reduction was tested for a pulse signal and stream of pulses with different rates (2, 4, 6, and 8 Mbps) and showed good reconstruction performance in terms of the normalized mean squared error (NMSE) and achieved an average enhancement of around 48%. The proposed schemes for signal reconstruction and noise reduction can be applied to different applications, such as ultra-wideband (UWB) communications, radio frequency identification (RFID) systems, mobile communication networks, and radar systems

Hiding data in images using steganography techniques with compression algorithms

Steganography is the science and art of secret communication between two sides that attempt to hide the content of the message. It is the science of embedding information into the cover image without causing a loss in the cover image after embedding.Steganography is the art and technology of writing hidden messages in such a manner that no person, apart from the sender and supposed recipient, suspects the lifestyles of the message. It is gaining huge attention these days as it does now not attract attention to its information's existence. In this paper, a comparison of two different techniques is given. The first technique used Least Significant Bit (LSB) with no encryption and no compression. In the second technique, the secret message is encrypted first then LSB technique is applied. Moreover, Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain. The LSB algorithm is implemented in spatial domain in which the payload bits are inserted into the least significant bits of cover image to develop the stego-image while DCT algorithm is implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are inserted into the frequency components of the cover image.The performance of these two techniques is evaluated on the basis of the parameters MSE and PSNR

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

A complexity efficient PAPR reduction scheme for FBMC-based VLC systems

Visible light communication based on a filter bank multicarrier holds enormous promise for optical wireless communication systems, due to its high-speed and unlicensed spectrum. Moreover, visible light communication techniques greatly impact communication links for small satellites like cube satellites, and pico/nano satellites, in addition to inter-satellite communications between different satellite types in different orbits. However, the transmitted visible signal via the filter bank multicarrier has a high amount of peak-to-average power ratio, which results in severe distortion for a light emitting diode output. In this work, a scheme for enhancing the peak-to-average power ratio reduction amount is proposed. First, an algorithm based on generating two candidates signals with different peak-to- average power ratio is suggested. The signal with the lowest ratio is selected and transmitted. Second, an alternate direct current-biased approach, which is referred to as the addition reversed method, is put forth to transform transmitted signal bipolar values into actual unipolar ones. The performance is assessed through a cumulative distribution function of peak-to-average power ratio, bit error rate, power spectral density, and computational complexity. The simulation results show that, compared to other schemes in literature, the proposed scheme attains a great peak-to-average power ratio reduction and improves the bit the error rate performance with minimum complexity overhead. The proposed approach achieved about 5 dB reduction amount compared to companding technique, 5.5 dB compared to discrete cosine transform precoding, and 8 dB compared to conventional direct current bias of an optical filter bank multicarrier. Thus, the proposed scheme reduces the complexity overhead by 15.7% and 55.55% over discrete cosine transform and companding techniques, respectively

Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication Networks

The intrusion detection system (IDS) is considered an essential sector in maintaining communication network security and has been desirably adopted by all network administrators. Several existing methods have been proposed for early intrusion detection systems. However, they experience drawbacks that make them subsequently inefficient against new/distinct attacks. To overcome these drawbacks, this paper proposes the enhanced long-short term memory (ELSTM) technique with recurrent neural network (RNN) (ELSTM-RNN) to enhance security in IDS. Intrusion detection technology has been associated with various problems, such as gradient vanishing, generalization, and overfitting issues. The proposed system solves the gradient-clipping issue using the likely point particle swarm optimization (LPPSO) and enhanced LSTM classification. The proposed method was evaluated using the NSL-KDD dataset (KDD TEST PLUS and KDD TEST21) for validation and testing. Many efficient features were selected using an enhanced technique, namely, the particle swarm optimization. The selected features serve for effective classification using an enhanced LSTM framework, where it is used to efficiently classify and detect the attack data from the normal data. The proposed system has been applied to the UNSW-NB15, CICIDS2017, CSE-CIC-IDS2018, and BOT _DATASET datasets for further verification. Results show that the training time of the proposed system is much less than that of other methods for different classes. Finally, the performance of the proposed ELSTM-RNN framework is analyzed using various metrics, such as accuracy, precision, recall, and error rate. Our proposed method outperformed LPBoost and DNNs methods

Design and implementation of a low-cost circuit for medium-speed flash analog to digital conversions

Despite the considerable advancements in analog-to-digital conversion (ADC) circuits, many papers neglect several crucial considerations: Firstly, it does not ensure that ADCs work well in the software or hardware. Secondly, it is not certain that ADCs have a wide range of amplitude responses for the input voltages to be convenient in many applications, especially in electronics, communications, computer vision, CubeSat circuits, and subsystems. Finally, many of these ADCs need to look at the suitability of the proposed circuit to the most extensive range of frequencies. In this paper, a design of a low-cost circuit is proposed for medium-speed flash ADCs. The proposed circuit is simulated based on a set of electronic components with specific values to achieve high stability operation for a wide range of frequencies and voltages, whether in software or hardware. This circuit is practically implemented and experimentally tested. The proposed design aims to achieve high efficiency in the sampling process over a range of amplitudes from 10 mV to 10 V. The proposed circuit operates at a bandwidth of frequencies from 0 Hz to greater than 10 kHz in the simulation and hardware implementation

A proposed preamble channel estimation scheme for flip FBMC-based indoor VLC systems

Filter bank multicarrier waveform is investigated as a potential waveform for visible light communication broadcasting systems. Imaginary inter-carrier and/or inter-symbol interference are causing substantial performance degradation in the filter bank multicarrier system. Direct current-biased optical filter bank multicarrier modulation overcomes all the problems of direct current-biased optical-orthogonal frequency division multiplexing modulation approaches in terms of speed and bandwidth. However, it also wastes a lot of energy while transforming a true bipolar signal into a positive unipolar signal by adding direct current-bias. In this paper, a flip-filter bank multicarrier-based visible light communication system was introduced to overcome this problem. In this system, a bipolar signal is converted to a unipolar signal by isolating the positive and negative parts, turning them to positive and then delivering the signal. Also, a new channel estimation scheme for a flip-filter bank multicarrier system is proposed which improves the channel estimation performance compared to that of each of the conventional schemes. The proposed system performance is measured in terms of bit error rate, normalized mean squared error, and constellation diagram. The superiority of the proposed scheme over other conventional structures has been successfully verified by MATLAB 2020b simulation experiments results. These results are evaluated under indoor visible light communication standard