Interference-Aware Radio Resource Allocation for 5G Ultra-Reliable Low-Latency Communication

Malik, Hassan; Alam, Muhammad Mahtab; Moullec, Yannick Le; Ni, Qiang

doi:10.1109/glocomw.2018.8644301

Cited by 12 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For illustration convenience, we set Q = 2, where q = 1 indicates high-quality (HQ) data streams and q = 2 indicates low-quality (LQ) data streams. To meet the BER requirements (q) 0 , q ∈ Q (or in other words, the QoS requirements) of the q-th data stream, the minimum transmit power required for the BS to deliver the q-th data stream of the m-th user in the k-th subchannel using the l-th modulationcoding mode is [33]…”

Section: System Modelmentioning

confidence: 99%

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Yuan¹,

Hu²,

Ni³

et al. 2023

Preprint

View full text Add to dashboard Cite

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is nontrivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winnertakes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value.

show abstract

Section: System Modelmentioning

confidence: 99%

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Yuan¹,

Hu²,

Ni³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the URLLC traffic is generated by applications with intermittent transmissions that exchange more or less important payload during short periods of time [25]. Therefore, it can be modeled with a power-law distribution namely a Pareto distribution with arrival rate λ u i and parameters a and x m [1, 3,5,6,8,9,23]. Flow sizes are independently and identically distributed with mean 1…”

Section: Iia Traffic Modelmentioning

confidence: 99%

Latency-Aware Offloading in Integrated Satellite Terrestrial Networks

Abderrahim¹,

Amin²,

Alouini³

et al. 2020

Preprint

View full text Add to dashboard Cite

Next-generation communication networks are expected to integrate newly-used technologies in a smart way to ensure continuous connectivity in rural areas and to alleviate the traffic load in dense regions. The prospective access network in 6G should hinge on satellite systems to take advantage of their wide coverage and high capacity. However, adopting satellites in 6G could be hindered because of the additional latency introduced, which is not tolerable by all traffic types. Therefore, we propose a traffic offloading scheme that integrates both the satellite and terrestrial networks to smartly allocate the traffic between them while satisfying different traffic requirements. Specifically, the proposed scheme offloads the Ultra-Reliable Low Latency Communication (URLLC) traffic to the terrestrial backhaul to satisfy its stringent latency requirement. However, it offloads the enhanced Mobile Broadband (eMBB) traffic to the satellite since eMBB needs high data rates but is not always sensitive to delay. Our scheme is shown to reduce the transmission delay of URLLC packets, decrease the number of dropped eMBB packets, and hence improve the network's availability. Our findings highlight that the inter-working between satellite and terrestrial networks is crucial to mitigate the expected high load on the limited terrestrial capacity.

show abstract

“…we use the successive convex approximation (SCA) and solve the approximated convex problem (23) iteratively where f v is updates at each iteration using the previous solution of (23).…”

Section: Resource Allocation In the Terrestrial Networkmentioning

confidence: 99%

Latency-Aware Offloading in Integrated Satellite Terrestrial Networks

Abderrahim¹,

Amin²,

Alouini³

et al. 2020

Preprint

View full text Add to dashboard Cite

Next-generation communication networks are expected to integrate newly-used technologies in a smart way to ensure continuous connectivity in rural areas and to alleviate the traffic load in dense regions. The prospective access network in 6G should hinge on satellite systems to take advantage of their wide coverage and high capacity. However, adopting satellites in 6G could be hindered because of the {additional latency introduced}, which is not tolerable by all traffic types. Therefore, we propose a traffic offloading scheme that integrates both the satellite and terrestrial networks to smartly allocate the traffic between them while satisfying different traffic requirements. Specifically, the proposed scheme offloads the Ultra-Reliable Low Latency Communication (URLLC) traffic to the terrestrial backhaul to satisfy its stringent latency requirement. However, it offloads the enhanced Mobile Broadband (eMBB) traffic to the satellite since eMBB needs high data rates but is not always sensitive to delay. Our scheme is shown to reduce the transmission delay of URLLC packets, decrease the number of dropped eMBB packets, and hence improve the network's availability. Our findings highlight that the inter-working between satellite and terrestrial networks is crucial to mitigate the expected high load on the limited terrestrial capacity.<br>

show abstract

Interference-Aware Radio Resource Allocation for 5G Ultra-Reliable Low-Latency Communication

Cited by 12 publications

References 10 publications

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Latency-Aware Offloading in Integrated Satellite Terrestrial Networks

Latency-Aware Offloading in Integrated Satellite Terrestrial Networks

Contact Info

Product

Resources

About