Radioelectronics and Communications Systems

Enhancing peak power efficiency of NOMA waveform for 5G and beyond 5G using SLM algorithm

2023-08-17T08:24:30+03:00

The paper presents a novel approach for mitigating the detrimental effects of the high Peak-to-Average Power Ratio (PAPR) inherent in Non-Orthogonal Multiple Access (NOMA) waveforms using the Selective Mapping (SLM) technique. NOMA, a prominent multiple access scheme in modern wireless communication systems, facilitates concurrent transmission of multiple users over the same time-frequency resource. However, NOMA is susceptible to elevated PAPR, causing efficiency degradation and inter-symbol interference. The proposed method leverages SLM to address the PAPR challenges in NOMA waveforms. By generating a set of diverse phase sequences, SLM constructs alternative versions of the original NOMA signal. The phase sequence resulting in the lowest PAPR is then selected for transmission. This dynamic adaptation significantly reduces peaks in the transmitted signal, thereby enhancing efficiency, minimizing distortion, and reducing the risk of nonlinear amplification. Extensive simulations are conducted to evaluate the efficiency of the projected procedure. The results demonstrate remarkable PAPR reduction in NOMA waveforms compared to conventional transmission methods. Additionally, the method maintains signal quality, improving the Bit Error Rate (BER), power spectral density (PSD) and enhancing the overall reliability of the radio framework. The paper concludes with insights into the feasibility of integrating SLM into existing NOMA-enabled systems, offering a promising avenue for optimizing the efficacy of advanced radio networks.

Deep learning framework for analysis of health factors in Internet-of-Medical Things

2023-08-26T16:00:53+03:00

The introduction of IoT technologies, such as those used in remote health monitoring applications, has revolutionized conventional medical care. Furthermore, the approach utilized to obtain insights from the scrutiny of lifestyle elements and activities is crucial to the success of tailored healthcare and disease prevention services. Intelligent data retrieval and classification algorithms allow for the investigation of disease and the prediction of aberrant health states. The Convolutional Neural Network (CNN) strategy is utilized to forecast such anomaly because it can successfully recognize the knowledge significant to disease anticipation from amorphous medical heath records. Conversely, if a fully coupled network-topology is used, CNN guzzles a huge memory. Furthermore, the complexity analysis of the model may rise as the number of layers grows. Therefore, we present a CNN target recognition and anticipation strategy based on the Pearson Correlation Coefficient (PCC) and standard pattern activities to address these shortcomings of the CNN model. It is built in this framework and used for classification purposes. In the initial hidden layer, the most crucial health-related factors are chosen, and in the next, a correlation-coefficient examination is performed to categorize the health factors into positively and negatively correlated groups. Mining the occurrence of regular patterns among the categorized health parameters also reveals the behavior of regular patterns. The model output is broken down into obesity, hypertension, and diabetes-related factors with known correlations. To lessen the impact of the CNN-typical knowledge discovery paradigm, we use two separate datasets. The experimental results reveal that the proposed model outperforms three other machine learning techniques while requiring less computational effort.

5G rollout challenges and opportunities for frontier and emerging markets

2023-11-18T11:18:56+02:00

Cost efficiency is an essential metric in the design of major part of what is expected to be the 5G network. Some of the 5G solutions, mainly those involving mmWave technology and network densification, are extremely expensive. Much of what was said about 5G rollout strategies has been said with regard to the world’s major leaders in 5G technology and telecom infrastructure investment. However, a much more numerous group of countries often referred as emerging markets or middle-income economies has never hold a leadership in the development of cellular technologies. To date, few of them have fully leveraged the potential of 4G systems while many still have not ensured a return on investment in 3G. This gave rise to the views that these countries are not ready for 5G challenges. However, all of them are doomed to enter the era of another industrial revolution, where exactly 5G is to become the main driver. And yet, the use cases and the portfolio of services of the future networks will apparently depend on economic realities of the country where it will be deployed. There is a rather vague picture at the moment of how the 5G rollout may take place in this part of the world and that is the main reason for this study. In this paper, we outline the technical challenges and economic tensions associated with the developing of 5G physical infrastructure and address spectrum allocation issues with a particular focus on the needs of the future enhanced mobile broadband component. Then we discuss the role of government in establishing favorable regulatory framework, providing supportive policies and finding local drivers to facilitate 5G rollout and the challenges, confronting the operators paving their way to 5G.

Comparative study of block ciphers implementation for resource-constrained devices (review)

2023-09-06T19:14:52+03:00

Lightweight cryptography (LWC) is critical for securing data between devices with limited resources. In this paper, we consider hardware-based block ciphers (BC). The BCs are thoroughly described using the substitution permutation network (SPN) and Feistel network (FN) structures. A detailed summary of each SPN and FN-based BC is provided. Furthermore, the paper compares and thoroughly analyses the performance analysis of modern SPN and FN-based BCs. For each block cipher, performance measures such as chip area (slices), frequency, latency, throughput, and hardware efficiency are reviewed. The paper also recommends appropriate ciphers for the greatest and lowest performance indicators. Among BCs, the PRINCE cipher has minimal latency and great throughput. The research also reveals that the LED and PRESENT ciphers are more compact than other ciphers. The open research challenges are also presented with a forward-looking perspective.