A serial IFFT precoding scheme to mitigate the periodic noise in OFDM system

In emerging Cyber-Physical Systems (CPS), the demand for higher communication performance and enhanced wireless connectivity is increasing fast. To address the issue, in our recent work, we proposed a dynamic programming algorithm with polynomial time complexity for effective cross-layer downlink Scheduling and Resource Allocation (SRA) considering the channel and queue state, while supporting fairness. In this paper, we extend the SRA algorithm to consider 5G use-cases, namely enhanced Machine Type Communication (eMTC), Ultra-Reliable Low Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB). In a simulation study, we evaluate the performance of our SRA algorithm in comparison to an advanced greedy cross-layer algorithm for eMTC, URLLC and LTE (long-term evolution). For eMTC and URLLC, our SRA method outperforms the greedy approach by up to 17.24%, 18.1%, 2.5% and 1.5% in terms of average goodput, correlation impact, goodput fairness and delay fairness, respectively. In the case of LTE, our approach outperforms the greedy method by 60%, 2.6% and 1.6% in terms of goodput, goodput fairness and delay fairness compared with tested baseline.

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A serial IFFT precoding scheme to mitigate the periodic noise in OFDM system

Cited by 2 publications

References 13 publications

An Enhanced Approach of MIMO-OFDM Data Transfer by Varying User Location Optimized by Machine and AI Learning

An Enhanced Approach of MIMO-OFDM Data Transfer by Varying User Location Optimized by Machine and AI Learning

Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems

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