Mustafa M. Zayer

Background: Network slicing has turned out to be one of the key enablers in the 5G networks due to the ability to support the diverse applications such as ultra reliable and low latency communications for the self-driving cars or IoT-like massive machine type communications. Prior expeditions lacked integrated tools for the dynamic assignment and allocation of resources and no possibility for maintaining constant QoS. Objective: In this article, the primary aim is to synthesis and test a reinforcement learning–driven slicing framework in order to orchestrate the resources of the three types of slices – URLLC, mMTC, and eMBB. This is to improve the performance of the sliced resource, ensure high availability, and minimize competition of the resources in multi-tenant scenarios in 5G networks. Methods: The proposed study design includes a focus on the key stakeholders and their needs for requirements gathering and an experimental field for actual implementation. Resource distribution is guided by the reinforcement learning algorithms by trying to minimize a cost function which incorporates the relation between the latency, isolation, throughput and energy expended. Using a number of runs, quality of performance is monitored to enable assessment of stability as well as response rates. Results: Experimental results show that the proposed framework achieves a lower level of latency violations and capacity oversubscription compared to heuristic methods. Furthermore, it consistently achieves nearly 2.5X better throughput for telemedicine slices and guarantees less than 5 ms latency for time-sensitive services during dynamic traffic conditions. Conclusion: The study shows how reinforcement learning can be effective and applied for end-to-end 5G network slicing. This sort of adaptive orchestration can increase service dependability while optimising overhead and herald instantly climbable multi-tenant networks compatible with various industries

Background: Quantum Key Distribution (QKD) has turned into a crucial point for secure communication in the era of quantum networks. Quantum key distribution provides the client with a theoretically secure key by taking advantage of the principles of quantum mechanics to counteract what could be posed by quantum computing to classical cryptography. Photons are lost in the system and there are some limitations which don’t allow scalability and integration with already existing networks. Objective: The study seeks to assess the viability of QKD systems, review some of the challenges associated with it, and investigate possible methods of utilizing both QKD and PQC to cope with new security threats in telecommunication industry. Methods: An in-depth analysis was made based on the experimental observations of key generation rates, photon loss, error correction, data throughput, and latency. Performance of quantum repeaters was experimented with for the purposes of measuring distance improvement abilities. A combined QKD-PQC approach was assessed for integrated integration for restricted settings. Results: QKD was seen to have high security and high performance in short distances and when quantum repeaters were implemented the distance could be greatly enhanced. In the QKD-PQC model, the rate of error correction, throughput, and scalability was noticed to be higher than in standalone QKD. Challenges that faced the work were photon loss, processing latency, and system vulnerabilities. Conclusion: New opportunities for secure communication are opened with QKD supported by quantum repeaters and hybrid cryptographic approaches. The technical and operational issues need to be resolved to realize the potential role of B3G evolution in enabling global telecommunications for the mass market.