Learning-based joint power and channel assignment for hyper dense 5G networks

Arani, Atefeh Hajijamali; Mehbodniya, Abolfazl; Omidi, Mohammad Javad; Adachi, Fumiyuki

doi:10.1109/icc.2016.7511450

Cited by 10 publications

(11 citation statements)

References 17 publications

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“…Dynamic BS ON/OFF switching affects the UE association mechanism and, consequently, the BSs' loads. We assume that each BS

b \in scriptB

periodically broadcasts its estimated load by a beacon signal . At each time t , UE k is associated with and served by BS b k ( t ) in accordance with the following equation:

b_{k} false(t false) = {\arg \max}_{b \in B_{ON}} 10 {normall normalo normalg}_{10} ((), P_{b} false(t false) g_{b, k} false(t false) false(1 - {\overset{L}{true}}_{b} false(t false) false)),

where P b ( t ) g b , k ( t ) is the received power from BS b at the location of UE k at time t .…”

Section: Problem Formulationmentioning

confidence: 99%

“…We assume that each BS

b \in scriptB

periodically broadcasts its estimated load by a beacon signal . At each time t , UE k is associated with and served by BS b k ( t ) in accordance with the following equation:

b_{k} false(t false) = {\arg \max}_{b \in B_{ON}} 10 {normall normalo normalg}_{10} ((), P_{b} false(t false) g_{b, k} false(t false) false(1 - {\overset{L}{true}}_{b} false(t false) false)),

where P b ( t ) g b , k ( t ) is the received power from BS b at the location of UE k at time t . Here,

{\overset{L}{true}}_{b} false(t false)

is the estimated load of BS b at time t , and it is calculated as follows:

{\overset{L}{true}}_{b} ((), t) = ((), 1 - {((), \frac{1}{t})}^{α}) {\overset{L}{true}}_{b} ((), t - 1) + {((), \frac{1}{t})}^{α} L_{b} ((), t - 1),

where α>0 denotes the learning rate exponent for the load estimation.…”

Section: Problem Formulationmentioning

confidence: 99%

“…We adopt a power consumption model that is proportional to the BS's operation mode. Therefore, the total power consumed by BS b can be expressed as follows:

P_{b}^{Total} = ({, \begin{matrix} P_{b}^{OFF} + \frac{1}{η_{b}^{PA} Λ_{b}} P_{b}, & if b \in B_{ON} \\ P_{b}^{OFF}, & if b \notin B_{ON}, \end{matrix})

with

Λ_{b} = ((), 1 - λ_{b}^{DC}) ((), 1 - λ_{b}^{MS}) ((), 1 - λ_{b}^{Cool}) ((), 1 - λ_{b}^{Feed}),

where

P_{b}^{OFF}

is the fixed power consumption for BS b .

{normalη}_{b}^{PA}

indicates the power amplifier efficiency of BS b .…”

Section: Problem Formulationmentioning

confidence: 99%

“…The cost function captures the BS's load and the total power consumption. Therefore, the utility function is defined as follows:

\begin{matrix} right \begin{matrix} π_{b} = & \underset{Benefit}{\underset{⏟}{ω_{b}^{k} \frac{(||, A_{b})}{(||, K)}}} - \underset{Cost}{\underset{⏟}{((), ω_{b}^{l} L_{b} + ω_{b}^{p} \frac{P_{b}^{Total}}{P_{b}^{OFF} + P_{b}^{TXMax}})}}, \end{matrix} \end{matrix}

where

P_{b}^{TXMax} = \frac{1}{η_{b}^{PA} Λ_{b}} P_{b}^{Max},

and

{normalω}_{b}^{normalk}

{normalω}_{b}^{normall}

, and

{normalω}_{b}^{normalp}

indicate the weight parameters of the benefit, load, and energy for BS b , respectively. Here, A b and | A b | denote the set of UEs served by BS b and the cardinality of A b , respectively.…”

Section: Problem Formulationmentioning

confidence: 99%

“…We assume that each BS b ∈  periodically broadcasts its estimated load by a beacon signal. 20 At each time t, UE k is associated with and served by BS b k (t) in accordance with the following equation 20…”

Section: Ue Associationmentioning

confidence: 99%

See 4 more Smart Citations

Distributed ON/OFF switching and dynamic channel allocation: Decreasing complexity and improving energy efficiency

Arani

Mehbodniya

Omidi

et al. 2017

Trans Emerging Tel Tech

Self Cite

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Heterogeneous network (HetNet) has emerged as a promising and viable approach to deliver higher‐rate data services. In HetNets, switching OFF base stations (BSs) is known as an effective solution to reduce the energy consumption of networks and thus enables the “greener” networks concept. In this paper, we investigate the impact of the number of energy‐saving modes for BSs on improving energy efficiency. We assume that BSs are able to switch among several modes, ie, different transmission power levels. We apply a no‐regret learning algorithm to choose the BS's operation mode while modeling the BS's operation under dynamic traffic with a Markov process. As a result, we face a trade‐off between energy savings and computational complexity. Furthermore, we use a distributed channel allocation algorithm based on the average cochannel interference power. Simulation results show that energy consumption reduces with increased number of modes. However, increasing the number of modes beyond a certain limit will not have a significant effect on energy efficiency.

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“…Dynamic BS ON/OFF switching affects the UE association mechanism and, consequently, the BSs' loads. We assume that each BS

b \in scriptB

periodically broadcasts its estimated load by a beacon signal . At each time t , UE k is associated with and served by BS b k ( t ) in accordance with the following equation:

b_{k} false(t false) = {\arg \max}_{b \in B_{ON}} 10 {normall normalo normalg}_{10} ((), P_{b} false(t false) g_{b, k} false(t false) false(1 - {\overset{L}{true}}_{b} false(t false) false)),

where P b ( t ) g b , k ( t ) is the received power from BS b at the location of UE k at time t .…”

Section: Problem Formulationmentioning

confidence: 99%

“…We assume that each BS

b \in scriptB

periodically broadcasts its estimated load by a beacon signal . At each time t , UE k is associated with and served by BS b k ( t ) in accordance with the following equation:

b_{k} false(t false) = {\arg \max}_{b \in B_{ON}} 10 {normall normalo normalg}_{10} ((), P_{b} false(t false) g_{b, k} false(t false) false(1 - {\overset{L}{true}}_{b} false(t false) false)),

where P b ( t ) g b , k ( t ) is the received power from BS b at the location of UE k at time t . Here,

{\overset{L}{true}}_{b} false(t false)

is the estimated load of BS b at time t , and it is calculated as follows:

{\overset{L}{true}}_{b} ((), t) = ((), 1 - {((), \frac{1}{t})}^{α}) {\overset{L}{true}}_{b} ((), t - 1) + {((), \frac{1}{t})}^{α} L_{b} ((), t - 1),

where α>0 denotes the learning rate exponent for the load estimation.…”

Section: Problem Formulationmentioning

confidence: 99%

“…We adopt a power consumption model that is proportional to the BS's operation mode. Therefore, the total power consumed by BS b can be expressed as follows:

P_{b}^{Total} = ({, \begin{matrix} P_{b}^{OFF} + \frac{1}{η_{b}^{PA} Λ_{b}} P_{b}, & if b \in B_{ON} \\ P_{b}^{OFF}, & if b \notin B_{ON}, \end{matrix})

with

Λ_{b} = ((), 1 - λ_{b}^{DC}) ((), 1 - λ_{b}^{MS}) ((), 1 - λ_{b}^{Cool}) ((), 1 - λ_{b}^{Feed}),

where

P_{b}^{OFF}

is the fixed power consumption for BS b .

{normalη}_{b}^{PA}

indicates the power amplifier efficiency of BS b .…”

Section: Problem Formulationmentioning

confidence: 99%

“…The cost function captures the BS's load and the total power consumption. Therefore, the utility function is defined as follows:

\begin{matrix} right \begin{matrix} π_{b} = & \underset{Benefit}{\underset{⏟}{ω_{b}^{k} \frac{(||, A_{b})}{(||, K)}}} - \underset{Cost}{\underset{⏟}{((), ω_{b}^{l} L_{b} + ω_{b}^{p} \frac{P_{b}^{Total}}{P_{b}^{OFF} + P_{b}^{TXMax}})}}, \end{matrix} \end{matrix}

where

P_{b}^{TXMax} = \frac{1}{η_{b}^{PA} Λ_{b}} P_{b}^{Max},

and

{normalω}_{b}^{normalk}

{normalω}_{b}^{normall}

, and

{normalω}_{b}^{normalp}

indicate the weight parameters of the benefit, load, and energy for BS b , respectively. Here, A b and | A b | denote the set of UEs served by BS b and the cardinality of A b , respectively.…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Ue Associationmentioning

confidence: 99%

See 3 more Smart Citations

Distributed ON/OFF switching and dynamic channel allocation: Decreasing complexity and improving energy efficiency

Arani

Mehbodniya

Omidi

et al. 2017

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

A distributed learning–based user association for heterogeneous networks

Arani

Omidi

Mehbodniya

et al. 2017

Trans Emerging Tel Tech

Self Cite

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The coexistence of various base stations (BSs) in heterogeneous networks (HetNets) has emerged as a promising approach to meet the ever increasing network capacity. In these networks, one of the important issues is the problem of associating user equipments (UEs) to BSs. In this paper, we investigate the UE association (UEA) problem in heterogeneous networks and propose a load-aware UEA mechanism based on the BSs' estimated load and signal-to-interference-and-noise ratio. The proposed mechanism can capture the trade-off between UE's quality-of-service requirement and delay. We model this strategic UE-BS association as a noncooperative game. To solve the game, we develop a fully distributed algorithm inspired by machine learning techniques, whereby the proposed UEA scheme corresponds to a Markov chain.In the proposed scheme, each UE senses its environment and decides which BS to select based on the satisfaction technique. Therefore, it achieves a high level of satisfaction for UEs. Furthermore, use of historical information helps UEs select BSs with better long-term performance. Simulation results show that the proposed mechanism reduces fractional transfer time and the number of unsatisfied UEs, respectively, up to about 46% and 52.3% and improves BS throughput up to about 15.4% compared to a benchmark algorithm that is based on the received signal strength and the BS's estimated load.

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