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See detailOn Reliable Key Performance Indicators in Cognitive Radio Networks
Khan, Abd Ullah; Abbas, Ghulam; Abbas, Ziaul Haq et al

in IEEE Networking Letters (2022)

Network serveability (NS), which considers both channel availability (CA) and service retainability (SR), is a key indicator to concisely express the performance of cognitive radio networks (CRNs ... [more ▼]

Network serveability (NS), which considers both channel availability (CA) and service retainability (SR), is a key indicator to concisely express the performance of cognitive radio networks (CRNs). However, CA cannot guarantee connection setup if receiver’s accessibility ( RxA ) is neglected. Likewise, SR yields imperfect results if RxA is ignored. As such, the two rather incomplete performance indicators misrepresent NS and lead to an overestimated satisfaction level of users. Aiming at reliable and concise analysis of CRNs’ performance, this letter introduces the concept of connection availability (CoA), which encompasses both CA and RxA for a user. Besides, it introduces service maintainability (SM), which incorporates the impact of RxA into SR. Consequently, the performance is represented more concisely through network serviceability (NeS), a concept we introduce to consider both CA and SM. We show that CoA, SM, and NeS diverge with various proportions from CA, SR, and NS, respectively, under variable traffic loads and channel failure rates. This indicates the degree of performance difference RxA introduces, and provides network designers the basis for dependable CRN budgeting. [less ▲]

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See detailCompletion Time Minimization in NOMA Systems:Learning for Combinatorial Optimization
Wang, Anyue UL; Lei, Lei UL; Lagunas, Eva UL et al

in IEEE Networking Letters (2021)

In this letter, we study a completion-time minimization problem by jointly optimizing time slots (TSs) and power allocation for time-critical non-orthogonal multiple access (NOMA) systems. The original ... [more ▼]

In this letter, we study a completion-time minimization problem by jointly optimizing time slots (TSs) and power allocation for time-critical non-orthogonal multiple access (NOMA) systems. The original problem is non-linear/non-convex with discrete variables, leading to high computational complexity in conventional iterative methods. Towards an efficient solution, we train deep neural networks to perform fast and high-accuracy predictions to tackle the difficult combinatorial parts, i.e., determining the minimum consumed TSs and user-TS allocation. Based on the learning-based predictions, we develop a low-complexity post-process procedure to provide feasible power allocation. The numerical results demonstrate promising improvements of the proposed scheme compared to other baseline schemes in terms of computational efficiency, approximating optimum, and feasibility guarantee. [less ▲]

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See detailAdmission Control and Network Slicing for Multi-Numerology 5G Wireless Networks
Ha, Vu Nguyen UL; Nguyen, Ti Ti; Le, Long Bao et al

in IEEE Networking Letters (2020)

This letter studies the admission control and network slicing design for 5G New Radio (5G-NR) systems in which the total bandwidth is sliced to support the enhanced mobile broadband (eMBB) and ultra ... [more ▼]

This letter studies the admission control and network slicing design for 5G New Radio (5G-NR) systems in which the total bandwidth is sliced to support the enhanced mobile broadband (eMBB) and ultra reliable and low latency communication (URLLC) services. We allow traffic from the eMBB bandwidth part to be overflowed to the URLLC bandwidth part in a controlled manner. We develop a mathematical framework to analyze the blocking probabilities of both eMBB and URLLC services based on which the network slicing and admission control is jointly optimized to minimize the blocking probability of the eMBB traffic subject to the blocking probability constraint for the URLLC traffic. An efficient iterative algorithm is proposed to deal with the underlying problem. [less ▲]

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