Subband CQI Feedback-Based Multicast Resource Allocation in MIMO-OFDMA Networks

Fuente, Alejandro de la; Femenias, Guillem; Riera-Palou, Felip; Armada, Ana García

doi:10.1109/tbc.2018.2789578

Cited by 22 publications

(12 citation statements)

References 28 publications

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“…Multicast is an efficient technique of providing grouporiented services by feeding all the subscribers by a single transmission. Extensive studies have been conducted to address the adaptive resource allocation problem of video multicast over wireless networks [19]- [23]. The conventional multicast scheme [19] conservatively selected a common modulation and coding scheme (MCS) for each group based on the lowest channel quality indicator (CQI) of all subscribers.…”

Section: Related Workmentioning

confidence: 99%

“…In [22], rate selection was optimized by maximizing the minimum throughput in a multicast group, even when the channel qualities were nonidentically distributed. In general, maximizing the spectral efficiency by relying on opportunistic strategies fails to provide any fairness guarantee among subscribers [23], since some of the subscribers experiencing low channel quality may be denied service. This is unacceptable for delay-constrained flawless lip-synchronized video services.…”

Section: Related Workmentioning

confidence: 99%

“…In (23), H g (t)b g (t) is interpreted as the queue-length-weighted transmission rate. A higher H g (t) implies a long backlog of video data in the queue of group g. In this situation, it prefers to improve the transmission rate by assigning more RBs for this group, in order to maintain queue stability.…”

Section: B Coarse-grained Assignment For Each Groupmentioning

confidence: 99%

“…A higher H g (t) implies a long backlog of video data in the queue of group g. In this situation, it prefers to improve the transmission rate by assigning more RBs for this group, in order to maintain queue stability. Similarly, a higher mean CQIm g (t) implies a better channel condition for the group g, hence more RBs will be allocated to the group g for maximizing the objective function in (23). Consequently, the subscribers in the group g perceive a better QoE.…”

Section: B Coarse-grained Assignment For Each Groupmentioning

confidence: 99%

“…These results imply that both the mean and median criteria are capable of characterizing the channel condition changes of the whole group. 5) performance vs. the trade-off factors: We conducted further experiments for investigating the performance sensitivity of the proposed DRMA vs. the trade-off factor V in (23) and W in (31), where V was set to be equal to W and the value of V varied in the range of 1 to 1000. The number of RBs available for multicast services was fixed to 50 and the remaining parameters were set to the same values as in Fig.…”

Section: ) Performance For Different Channel Bandwidthmentioning

confidence: 99%

See 4 more Smart Citations

Dynamic Resource Allocation for Scalable Video Multirate Multicast Over Wireless Networks

Chen

Yang

et al. 2020

IEEE Trans. Veh. Technol.

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Aided by scalable video coding, multirate multicast has become a promising technique of providing differentiated quality of experience (QoE) for massive numbers of video subscribers operating in heterogeneous channel conditions. Nevertheless, due to the time-varying nature of wireless channels and the subscribers' diverse requirements, it is challenging to dynamically control the video rate in the light of the available radio resource to achieve the best QoE. To elaborate a little further, the time scale of resource scheduling is of short-term nature, which determines the short-term video quality variation, but from a service provider's perspective the design objective is to optimize the long-term QoE for all subscribers. Despite its importance, this problem has not been considered before. Explicitly, we formulated this problem as a time-averaged stochastic optimization problem which avoids the impact of both the shortterm channel quality fluctuation and that of the video bitrates, whilst maintaining both inter-and intra-group fairness. The stratified structure of the problem inspires us to decompose it into a two-phase optimization: coarse grained assignment for each user group and fine grained assignment for each subgroup. We propose an adaptive multicast algorithm based on Lyapunov's optimization theory for solving this problem, by striking a compelling trade-off between the system's utility and its queue stability. We quantify the achievable performance of our proposed solution based on realistic video traces.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B Coarse-grained Assignment For Each Groupmentioning

confidence: 99%

Section: B Coarse-grained Assignment For Each Groupmentioning

confidence: 99%

Section: ) Performance For Different Channel Bandwidthmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Resource Allocation for Scalable Video Multirate Multicast Over Wireless Networks

Chen

Yang

et al. 2020

IEEE Trans. Veh. Technol.

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Uplink transmission with multiuser scheduling in non‐stand‐alone cellular network using virtual MIMO system

Kukade

Sutaone²,

Patil³

2022

Trans Emerging Tel Tech

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5G architecture allows the use of new radio alongside long‐term evolution‐A (LTE‐Advanced) and enables cellular mobile networks to handle extraordinarily high traffic volume data. The major challenge in current uplink data transmission systems is making resource blocks (RBs) available in a continuous form and assigning them to active users in an optimum manner to improve the system's spectral efficiency. The best N‐subset (BNS) minimization technique is suggested to identify the best of the best continuous chunks of RBs from the existing system bandwidth, followed by a modified recursive maximum expansion (RME) algorithm to assign RBs to users in the most optimal manner. The algorithm together is referred to as a multiuser uplink scheduler BNSRME algorithm. A constraint utility maximization problem is formulated to allocate RBs to UEs in the most optimized manner. Utility matrix is then converted into a weighted sum‐rate maximization problem allowing weights to be updated adaptively based on marginal utility values. In addition, a threshold limit is considered based on the signal‐to‐noise ratio in the BNSRME‐TH algorithm, which satisfies multiusers in terms of assigned resources and improves system performance, spectral efficiency, and throughput. This MU scheduling strategy is used in the 5G uplink non‐stand‐alone cellular network. The findings exhibit that the proposed multiuser algorithm increases system spectral efficiency by 31.65% in comparison with the existing opportunistic algorithms. It has been observed that the performance of the system is further increased by using the MU‐MIMO framework.

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NOMA performance enhancement-based imperfect SIC minimization using a novel user pairing scenario involving three users in each pair

Miandoab

Tazehkand

2020

Wireless Netw

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Subband CQI Feedback-Based Multicast Resource Allocation in MIMO-OFDMA Networks

Cited by 22 publications

References 28 publications

Dynamic Resource Allocation for Scalable Video Multirate Multicast Over Wireless Networks

Dynamic Resource Allocation for Scalable Video Multirate Multicast Over Wireless Networks

Uplink transmission with multiuser scheduling in non‐stand‐alone cellular network using virtual MIMO system

NOMA performance enhancement-based imperfect SIC minimization using a novel user pairing scenario involving three users in each pair

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