Bound-based power optimization for multi-hop heterogeneous wireless industrial networks under statistical delay constraints

Petreska, Neda; Al-Zubaidy, Hussein; Knorr, Rudi; Gross, James

doi:10.1016/j.comnet.2018.09.009

Cited by 9 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, EEE is a suitable metric to quantify and optimize the throughput of the communication link per each consumed watt for energy-limited, low latency IoT. Hence, the optimization of EEE is of great importance for IoT devices, which are isolated from stationary power sources and are required to deliver packets with low latency in the order of milli-seconds [26]. In what follows, we study the EEE for short packet communication.…”

Section: B the Relation Between Effective Capacity And Effective Energy Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Shehab

Alves

Jorswieck

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption. We analyze the EEE of ultrareliable networks operating in the finite blocklength regime. We obtain a closed form approximation for the EEE in quasi-static Nakagami-(and Rayleigh as sub-case) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the QoS constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultra-reliability using one transmission consumes huge amount of power, which is not applicable for energy limited IoT devices. In this context, accounting for empty buffer probability in machine type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the non-empty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

show abstract

Section: B the Relation Between Effective Capacity And Effective Energy Efficiencymentioning

confidence: 99%

“…Returning to (26), which represents the average power consumption, we study the effect of varying the non-EBP 1 on the power consumption by obtaining the first derivative of (26) as…”

Section: A Power Savingmentioning

confidence: 99%

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Shehab

Alves

Jorswieck

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Returning to (26), which represents the average power consumption, we study the effect of varying the non-EBP 𝑝 1 𝑛𝑏 on the power consumption by obtaining the first derivative of (26) as…”

Section: A Power Savingmentioning

confidence: 99%

Effective Energy Efficiency of Ultra-reliable Low Latency Communication

Shehab¹,

Alves²,

Jorswieck³

et al. 2021

Preprint

View full text Add to dashboard Cite

Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption. We analyze the EEE of ultrareliable networks operating in the finite blocklength regime. We obtain a closed form approximation for the EEE in quasi-static Nakagami-𝑚 (and Rayleigh as sub-case) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the QoS constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultra-reliability using one transmission consumes huge amount of power, which is not applicable for energy limited IoT devices. In this context, accounting for empty buffer probability in machine type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the non-empty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

show abstract

“…Based on the upper bounds obtained via (min,×) SNC, a cross-layer power control framework was proposed in [22] for a single device in WirelessHART systems. The framework was further extended into a multi-hop version in [23] to minimize power consumption under statistical end-to-end delay constraints. Utilizing SNC, statistical delay QoS analysis was performed in [24] for millimeter-wave multi-hop systems with full-duplex buffered relays.…”

Section: Introductionmentioning

confidence: 99%

Downlink MIMO-NOMA for Ultra-Reliable Low-Latency Communications

Xiao

Zeng

et al. 2019

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

With the emergence of the mission-critical Internet of Things (IoT) applications, ultra-reliable low-latency communications (URLLC) are attracting a lot of attention. Nonorthogonal multiple access (NOMA) with multiple-input multipleoutput (MIMO) is one of the promising candidates to enhance connectivity, reliability and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA, are Cauchy convergent, and can be asymptotically approached by a finite truncated binomial series in closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. Extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition.

show abstract

Bound-based power optimization for multi-hop heterogeneous wireless industrial networks under statistical delay constraints

Cited by 9 publications

References 32 publications

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Effective Energy Efficiency of Ultra-reliable Low Latency Communication

Downlink MIMO-NOMA for Ultra-Reliable Low-Latency Communications

Contact Info

Product

Resources

About