International Journal of communication and computer Technologies

Fine Tuning SSP Algorithms for Mimo Antenna Systems for Higher Throughputs and Lesser Interferences

Cao S. Yang, Huang Lu, Sun, Fan Qian — Tue, 10 Sep 2024 00:00:00 +0300

This research deals with improving signal processing techniques for largescale multiple-input, multiple-output (MIMO) antenna systems with the goal of high data rate transmission, and low interference in the seventh generation of wireless communication networks. In the current society, MMIMO has shown enhanced improvements in spectral efficiency, capacity and coverage due to its large number of antennas. However, it also posess disadvantages which are an enhanced computational complexity and an interference between the users. This paper inventories linear precoding, minimum mean square error (MMSE) detection and interference alignment to eliminate the interference and to optimize the through put of data. Further, machine learning for adaptive beamforming and channel estimation in the contexts of signal transmission in environments that can vary from one time instance
to another is explored. The performance of these algorithms is then analyzed in simulations and theoretically by comparing their: spectral efficiency, power consumption, and bit error rate (BER). This is shown in the results, which point out that the adoption of advanced signal processing strategies can indeed mitigate interference and improve the system’s throughput of envisaged environments such as 5G and beyond, more specifically, densely urbanized environments with high user density. This work has implications for the creation of better and more efficient massive MIMO systems to allow networks in the future to support the ever-growing need for enhanced and improved communication.

Techniques on Controlling Bandwidth and Energy Consumption for 5G and 6G Wireless Communication Systems

Baotic Antoniewicz, Silva Dreyfus — Fri, 20 Sep 2024 00:00:00 +0300

This work aims at exploring novel modulation schemes and waveform designs for 5G and the emerging 6G telecommunication systems for high band width utilization and energy consumption. Thus, as generation of wireless communication networks progresses, requirement toward higher data rate, lower delay and power consumption increases, encouraging the emergence of new layer of modulation. This paper reviews more advanced waveforms for 5G systems, such as f-OFDM, GFDM, and NOMA which enable higher spectral efficiency and better interference tolerance. Further, we unravel various modulation schemes including AMC and higher order QAM through which modulation and coding rate can be selected based on the quality of channel to allow maximum throughput with minimal power consumption. The efficacy of these new schemes in scenarios of the proposed system is tested through simulations and analysing analytical models by comparing parameter such as bandwidth usage, error rates and energy efficiency of these new schemes to conventional modulation techniques like OFDM. The analysis demonstrates that these novel solutions can enhance the total efficiency of the following generation communi- cation networks by enhancing the potential of the crucial use instances of 5G and 6G realizations, such as M มC, URLLC, and eMBB. Hence, this research is part of the continuing endeavours to advance better techniques for wireless communication.

Novel Approaches in AI Processing Systems for their Better Reliability and Function

Meinhardt Dorofte, Kjaer Krein — Sat, 14 Sep 2024 00:00:00 +0300

It looks into new innovative architectures based on the structure and functionality of the human brain to improve AI computing systems. As AI uses become more sophisticated and required, common computer hardware architectures have problems with power consumption, scalability and efficiency. These challenges can be effectively addressed by the neuromorphic computing which incorporates the biological structure and functionality of neural networks. In this paper, several brain-related structures like spiking neural networks and synaptic plasticity are discussed to design new effective and resource-sharing systems. These architectures attempt to emulate particular aspects of the brain, such as sparse coding, event-driven processing and real-time learning all in an effort to reduce power consumption while increasing processing speed as well as flexibility. The study also explores the usage
of additional hardware components, including memristors and neuromorphic processors, to enhance core AI applications, including pattern identification, decision-making, and sensory analysis. This has been shown through simulations and prototyping hardware using six hardware implements exhibiting great improvements of computational effectiveness and execution compared
to regular architectures. This work is a useful addition toward the continuous advancement of next generation AI systems, which presents a way forward toward AI hardware that is efficient, scalable, and closely modeled after biological neural structures that would effectively support complex machine learning in real life situations.

Secure Computing Protocols without Revealing the Inputs to Each of the Various Participants

Shefer Roper, Plessner Bar — Sun, 22 Sep 2024 00:00:00 +0300

This work focuses on analyzing cryptographic protocols through which two or more parties can compute functions of their joint inputs without divulging their inputs in their raw form which is a building block of SMPC. Due to the security one gets to protect the data in the computations SMPC allows participants to engage in the computations while protecting the data it is used in fields such as finance, healthcare and data analytics whereby data is sensitive. The subject of the study is approaches for introducing security into the examined protocols to guarantee their correctness while preserving user privacy, through the use of such tools as homomorphic encryption, secret sharing, and zero-knowledge technology. We consider various security configurations, such as semi-honest and
malicious security, to understand how vulnerable these protocols are to feasible attacks or data exposure. Also, important issues, such as scaling and computational complexity, are discussed where we give solutions for minimizing communication cost and time in the context of big data applications. The outcome indicates that carrying out secure and practical SMPC with strong security assurances is feasible, irrespective of performance requirements of various actual-world application scenarios. This work is relevant to the current state of the art in cryptography and provides new protocols enabling sensitive computations for real privacy-preserving applications in the modern digital environment.