绿色异构无线网络能耗优化策略

28 浏览量更新于2024-08-28 收藏 1.57MB PDF 举报

“绿色异构无线网络中的能耗成本最小化” 这篇研究论文《绿色异构无线网络中的能耗成本最小化》探讨了如何在物联网(IoT)背景下，优化无线网络的能源效率，特别是在绿色异构无线网络中降低能耗成本的问题。论文发表在《计算机网络》(Computer Networks)期刊上，由 Bang Wang、Qiang Yang、Laurence T. Yang 和 Chunsheng Zhu 等多位来自中国华中科技大学、加拿大英属哥伦比亚大学和圣弗朗西斯泽维尔大学的专家共同撰写。文章指出，随着物联网的广泛应用，未来无线网络面临着新的挑战，其中最大的问题之一是能源消耗。物联网设备的海量增长使得网络能耗急剧增加，而传统能源的有限性和环境问题使得研究绿色、节能的无线网络解决方案变得至关重要。因此，论文重点研究了如何在利用可再生能源的同时，通过智能的资源分配策略来最小化网络的能耗成本。论文关键词包括：能源效率、可再生能源、异构无线网络、资源分配和物联网。这表明研究涵盖了以下几个关键领域： 1. **能源效率**：提高网络设备的能效是减少能耗的关键。论文可能提出了新的算法或模型，以确保网络在提供服务的同时，能有效地管理和减少能量消耗。 2. **可再生能源**：由于传统能源的局限性，论文可能会讨论如何整合太阳能、风能等可再生能源，以支持无线网络的运行，同时减少对环境的影响。 3. **异构无线网络**：这种网络结构包含多种类型的无线通信节点，如宏蜂窝、微蜂窝、微微蜂窝以及WiFi等，每个节点可能有不同的功率需求和性能特征。优化这样的网络需要综合考虑各种因素，包括覆盖范围、带宽、服务质量(QoS)等。 4. **资源分配**：为了降低能耗，资源分配策略必须高效且灵活。论文可能提出了新的优化算法，以确保在网络中动态调整资源分配，根据实时需求来最小化能源消耗。 5. **物联网**：物联网设备的广泛部署对网络架构提出了新的要求。论文可能探讨了如何适应这些设备的低功耗特性，以及如何在大规模连接的环境下实现绿色通信。通过深入研究这些主题，论文可能提供了理论分析、仿真验证以及可能的实际应用案例，以证明提出的策略在实际网络环境中的有效性和可行性。这篇论文对理解和构建更加环保、经济的无线网络具有重要的理论和实践意义。

524 B. Wang et al. / Computer Networks 129 (2017) 522–535

Fig. 1. An example green heterogeneous wireless network architecture. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web

version of this article.)

the practical traﬃc distribution, so as to minimize the total energy

consumption cost.

3. System model

3.1. Network model

In this paper, we consider a heterogeneous wireless network

consisting of both macro BSs and pico BSs. Each macro BS covers a

larger area, and each pico BS within a macro cell covers a smaller

area. Mobile users are assumed to be evenly distributed in the net-

work. For energy supply, all the BSs in our model are powered by

both on-grid energy and renewable energy sources. We consider

the use of solar panels as the source of green energy. Fig. 1 illus-

trates an example green heterogeneous network with hybrid en-

ergy supplies.

Let N

, N

, and M denote the set of macro BSs, pico BSs and

mobile users, respectively. |N

| = N

, |N

| = N

, and |M| = M. We

use N = { 1 , 2 , ··· , N} to denote the set of all BSs in the network,

i.e., N = N

∪ N

, and N = N

+ N

. We use the subscript i ∈ N to

denote the i th BS ,

and j ∈ M index the j th user. The operational

time of our algorithm is divided into K =

time slots, the length

of each slot is τ seconds and k ∈ K denotes the k th slot.

3.2. Traﬃc model

The mobile traﬃc shows both temporal and spatial diversi-

ties [35] . In the temporal domain, individual BS exhibits high traﬃc

dynamics over time. We ﬁnd that the peak hour spans from 10 AM

to 6 PM, and off peak hours are from 1 AM to 5 AM. However, the

traﬃc volume has near-term stability. It is almost constant over a

short term like several minutes of the same time in consecutive

days. Thus, we can predict the average traﬃc load across several

time slots based on the historical mobile traﬃc statistics.

In the temporal domain, we use a peak and off-peak tempo-

ral traﬃc model for mobile users. The mean number of users in

the peak period is much larger than that in the off-peak period. In

each period, the number of users is uniformly distributed around

the mean value. In the spatial domain, we assume that mobile

users are randomly distributed in the area.

Without speciﬁcally stated, a BS can be either a macro BS, or a pico BS.

3.3. Data transmission model

In this paper, we focus on the downlink data transmission as

the main energy consumption of all BSs. Let X = { X

, X

, . . . , X

}

denote the user-BS association matrix. We use X

to denote the

user-BS association relationship at the k th slot. The element X

( i, j )

stands for the connection relationship between user j and BS i at

the k th slot, i.e.,

(

i, j

)



1 , user j is served by BS i,

0 , otherwise .

(1)

Note that a user in the system can be associated with

only one BS, either a macro BS or a pico BS. That is,



i ∈N

(

i, j

)

= 1 , ∀ j ∈ M , ∀ k ∈ K .

For simplicity, we ignore the time slot index k in this subsec-

tion below. During the connection period, according to the Shan-

non Theorem, we can obtain the downlink transmission data rate

of user j :

= W

i, j

log

(1 +

i, j

) , (2)

where P

i,j

is the transmission power of BS i for user j data trans-

mission, and g

i, j

is the channel gain between user j and BS i ,

which in general includes path loss, shadowing and antenna gain.

denotes the noise power level, and W

i, j

is the bandwidth of

user j allocated by its associated BS i . To reduce the computa-

tional complexity, we adopt a simple equal share strategy to allo-

cate the available bandwidth of each BS to its associated users. We

use L



j∈M

(

i, j

)

to denote the number of users served by the

BS i . So the bandwidth allocated for a user j associated with BS i

is computed by W

i, j

, where W

is the available bandwidth of

BS i .

3.4. Energy consumption model

In this paper, we assume that each user has the same data rate

requirement R

when admitted to the network. But different users

may have different service times due to their different traﬃc de-

mands. By letting R

= R

, we can obtain the transmission power

for data transmission of user j from its associated BS i as:

i, j

( 2

/ W

i, j

− 1)

i, j

. (3)

剩余13页未读，继续阅读

weixin_38545332

粉丝: 6
资源: 979

绿色异构无线网络能耗优化策略

绿色异构网络中能源成本最小化策略

绿色异构蜂窝网络：无线回程的能源成本最小化策略

DEEC算法在异构WSN中优化能耗与网络生命周期

具有无线回程的绿色异构蜂窝网络中的能源成本最小化

异构无线传感器网络中用于分布式估计的传感器部署

基于动态门限的异构无线网络协作节能算法 (2014年)

绿色异构网络多点协作通信

绿色异构云无线接入网：潜在技术，性能折衷和挑战

异构无线传感器网络中一种可扩展的代码分发技术

异构无线传感器网络中的无障碍睡眠唤醒调度策略

最新资源