Tightness of Jensen’s Bounds and Applications to MIMO Communications

Yuan, Jide; Matthaiou, Michail; Jin, Shi; Gao, Feifei

doi:10.1109/tcomm.2016.2623945

Cited by 34 publications

(16 citation statements)

References 39 publications

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“…Bm,n i∈Bm,n ρκξ λ (i) , n > m. tightness of the bound improves when the maximum number of antennas grows, which is consistent with the analysis of [33].…”

Section: A High-snr Approximationsupporting

confidence: 84%

“…According to the results in [33], the offset of (12) converges to a certain value as ρ grows, while decreases with the maximum dimension of W, and increases with the minimum dimension of W. In practice, the minimum dimension of the matrix typically corresponds to the number of user antennas which normally varies from 1 to 4. Thus, the approximation in (12) is considerably tight in most of the scenarios.…”

Section: A High-snr Approximationmentioning

confidence: 99%

“…The tightness of the approximation, which is obtained by the adoption of the Jensen's bounds, has been discussed in [33]. According to the results in [33], the offset of (12) converges to a certain value as ρ grows, while decreases with the maximum dimension of W, and increases with the minimum dimension of W. In practice, the minimum dimension of the matrix typically corresponds to the number of user antennas which normally varies from 1 to 4.…”

Section: A High-snr Approximationmentioning

confidence: 99%

See 2 more Smart Citations

User-Centric Networking for Dense C-RANs: High-SNR Capacity Analysis and Antenna Selection

Yuan

Jin

et al. 2017

IEEE Trans. Commun.

Self Cite

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“…Bm,n i∈Bm,n ρκξ λ (i) , n > m. tightness of the bound improves when the maximum number of antennas grows, which is consistent with the analysis of [33].…”

Section: A High-snr Approximationsupporting

confidence: 84%

Section: A High-snr Approximationmentioning

confidence: 99%

Section: A High-snr Approximationmentioning

confidence: 99%

See 1 more Smart Citation

User-Centric Networking for Dense C-RANs: High-SNR Capacity Analysis and Antenna Selection

Yuan

Jin

et al. 2017

IEEE Trans. Commun.

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“…1) User Rate with Accurate Reporting: It's challenging to derive the exact closed-form expression for the ergodic rate. Similar to [14], applying Jensen's inequality, we can obtain a lower bound of the per user rate when M > K. This Jensen lower bound becomes exact as M → ∞ [15]. Hence, the per user rate is expressed as…”

Section: A All Users Served In One Resource Blockmentioning

confidence: 87%

Impact of Channel State Misreporting on Multi-user Massive MIMO Scheduling Performance

Zhang

Sun

Sabharwal

et al. 2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

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The robustness of system throughput with scheduling is a critical issue. In this paper, we analyze the sensitivity of multi-user scheduling performance to channel misreporting in systems with massive antennas. The main result is that for the round-robin scheduler combined with max-min power control, the channel magnitude misreporting is harmful to the scheduling performance and has a different impact from the purely physical layer analysis. Specifically, for the homogeneous users that have equal average signal-to-noise ratios (SNRs), underreporting is harmful, while overreporting is beneficial to others. In underreporting, the asymptotic rate loss on others is derived, which is tight when the number of antennas is huge. One interesting observation in our research is that the rate loss "periodically" increases and decreases as the number of misreporters grows. For the heterogeneous users that have various SNRs, both underreporting and overreporting can degrade the scheduler performance. We observe that strong misreporting changes the user grouping decision and hence greatly decreases some users' rates regardless of others gaining rate improvements, while with carefully designed weak misreporting, the scheduling decision keeps fixed and the rate loss on others is shown to grow nearly linearly with the number of misreporters.

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“…The non-existence of the explicit form of π 0 determines that it is hard to achieve the closed-form of the global optimal N . Furthermore, according to (26), (30) and (37), E{W q } is neither mathematically tractable.…”

Section: B Average Power Minimization Problemmentioning

confidence: 99%

Traffic-Aware Two-Stage Queueing Communication Networks: Queue Analysis and Energy Saving

Miridakis

Xiao

et al. 2020

IEEE Trans. Commun.

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To boost energy saving for the general delay-tolerant IoT networks, a two-stage and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware Nthreshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art.Index Terms-Delay tolerant IoT relaying networks, mean waiting time, N -threshold and gated policy, two-hop queueing, and traffic-aware power saving.

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Tightness of Jensen’s Bounds and Applications to MIMO Communications

Cited by 34 publications

References 39 publications

User-Centric Networking for Dense C-RANs: High-SNR Capacity Analysis and Antenna Selection

User-Centric Networking for Dense C-RANs: High-SNR Capacity Analysis and Antenna Selection

Impact of Channel State Misreporting on Multi-user Massive MIMO Scheduling Performance

Traffic-Aware Two-Stage Queueing Communication Networks: Queue Analysis and Energy Saving

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