Network Slicing for eMBB and mMTC with NOMA and Space Diversity Reception

Tominaga, Eduardo Noboro; Alves, Hirley; López, Onel L. Alcaraz; Souza, Richard Demo; Rebelatto, João Luiz

doi:10.1109/vtc2021-spring51267.2021.9448974

Cited by 16 publications

(5 citation statements)

References 9 publications

(22 reference statements)

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“…In this article, we assume that the gNodeBs have a (near) perfect knowledge of the channel state information [20], [21]. The availability of (near) perfect channel state information at the gNodeB in a real 5G network can be justified when fading varies slowly over time and the mobility of the end-users is low since the wireless channel does not change rapidly, which is similar to our case.…”

Section: System Modelmentioning

confidence: 84%

Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services

Filali¹,

Mlika²,

Cherkaoui³

et al. 2022

Preprint

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Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services. In a large time-scale, an SDN controller allocates radio resources to gNodeBs according to the requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB allocates its available resources to its end-users and requests, if needed, additional resources from adjacent gNodeBs. We formulate this problem as a non-linear binary program and prove its NPhardness. Next, for each time-scale, we model the problem as a Markov decision process (MDP), where the large-time scale is modeled as a single agent MDP whereas the shorter time-scale is modeled as a multi-agent MDP. We leverage the exponentialweight algorithm for exploration and exploitation (EXP3) to solve the single-agent MDP of the large time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the multi-agent MDP of the short time-scale resource allocation. Extensive simulations show that our approach is efficient under different network parameters configuration and it outperforms recent benchmark solutions.

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Section: System Modelmentioning

confidence: 84%

Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services

Filali¹,

Mlika²,

Cherkaoui³

et al. 2022

Preprint

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“…In the case of coexistence between enhanced Mobile Broadband (eMBB) and mMTC, they also studied the use of NOMA with SIC decoding but in a simple model that considers perfect CSI and a single-antenna BS. Their framework was extended for the case of a BS equipped with multiple antennas in our previous work [25], but still considering perfect CSI. In [25],…”

Section: A Related Workmentioning

confidence: 99%

“…Their framework was extended for the case of a BS equipped with multiple antennas in our previous work [25], but still considering perfect CSI. In [25],…”

Section: A Related Workmentioning

confidence: 99%

Performance Analysis of MIMO-NOMA Iterative Receivers for Massive Connectivity

et al. 2022

Self Cite

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The Fifth Generation (5G) of wireless networks introduced support to Machine-Type Communications (MTC), which is the wireless connectivity solution for Internet of Things (IoT) applications. MTC is split into two different categories: massive MTC (mMTC) and critical MTC (cMTC). Current 5G standards and technologies are not capable of fully satisfying the requirements of both mMTC and cMTC use cases, thus industry and academia have already started developing solutions for MTC in beyond-5G and 6G networks. In some mMTC use cases, receivers might not be equipped with a large number of antennas owing to cost, size or power limitations, thus the number of active devices in a time slot may surpass the number of antennas. Due to the limited spatial multiplexing capabilities, only multi-antenna techniques are not enough to provide connectivity to a massive number of devices in such scenarios. In this paper, we propose and evaluate the performance of iterative linear receivers that can address this issue. By combining Multiple-Input Multiple-Output (MIMO) techniques with Non-Orthogonal Multiple Access (NOMA) exploiting Successive Interference Cancellation (SIC) or Parallel Interference Cancellation (PIC) decoding, the proposed novel receivers are capable of performing dynamic ordering SIC/PIC decoding of multiple overlapping signals even when the number of active devices surpasses that of receive antennas. The performance of the receivers is studied in terms of outage probability and computational complexity. Simulation results show that, among all the receivers studied in this paper, the PIC-based Minimum Mean Square Error (MMSE) receiver presents the best performance while at the same time reducing the number of complex signal operations such as matrix inversions.

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“…algorithm introduces frequency diversity, simultaneously increasing the eMBB achievable rate and the URLLC reliability. In the context of eMBB and mMTC slicing, [10] considers the use of multiple receiving antennas in the uplink. The results show that the space diversity provided by the multiple antennas are more beneficial to H-NOMA than to H-OMA, and that such difference increases with the number of antennas.…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Network Slicing for eMBB and mMTC with Distributed Power Allocation

Hupalo,

Souza,

Rebelatto

2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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This work addresses the network slicing of radio resources between enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC) services, considering both heterogeneous orthogonal multiple access (H-OMA) and heterogeneous non-orthogonal multiple access (H-NOMA). For increasing the number of supported active mMTC devices, we adapt the transmission power of these devices in a decentralized fashion. The results indicate that the number of supported mMTC devices can be increased by up to 40% under H-NOMA, and that such gain achieves up to 60% in an orthogonal scenario.

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Network Slicing for eMBB and mMTC with NOMA and Space Diversity Reception

Cited by 16 publications

References 9 publications

Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services

Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services

Performance Analysis of MIMO-NOMA Iterative Receivers for Massive Connectivity

Physical Layer Network Slicing for eMBB and mMTC with Distributed Power Allocation

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