Signalling Storms in 3G Mobile Networks--使用HMM模型，參數： the key parameters of mobile user device behaviour that can lead to signalling storms，功耗角度（analysing signalling behaviour from an energy
consumption perspective,）見論文圖，就是建立的HMM模型。
Abstract—We review the characteristics of signalling storms
that have been caused by certain common apps and recently
observed in cellular networks, leading to system outages. We
then develop a mathematical model of a mobile user’s signalling
behaviour which focuses on the potential of causing such storms,
and represent it by a large Markov chain. The analysis of this
model allows us to determine the key parameters of mobile user
device behaviour that can lead to signalling storms. We then
identify the parameter values that will lead to worst case load
for the network itself in the presence of such storms. This leads to
explicit results regarding the manner in which individual mobile
behaviour can cause overload conditions on the network and its
signalling servers, and provides insight into how this may be
avoided.
Impact of Signaling Storms on Energy Consumption and Latency of LTE User
Equipment——主要是探討影響因素。
Abstract—Signaling storms in mobile networks, which congest
the control plane, are becoming more frequent and severe
because misbehaving applications can nowadays spread more
rapidly due to the popularity of application marketplaces
for smartphones. While previous work on signaling storms
consider the processing overhead in the network and energy
consumption of the misbehaving User Equipment (UE) only,
this paper aims to investigate how signaling storms affect both
the energy consumption and bandwidth allocation of normal
and misbehaving LTE UEs by constructing a mathematical
model which captures the interaction between the UE traffic
and the Radio Resource Control state machine and bandwidth
allocation mechanism at the eNodeB. Our results show that
even if only a small proportion of the UE population is
misbehaving, the energy consumption of the radio subsystem of
the normal UEs can increase significantly while the time spent
actively communicating increases drastically for a normal data
session. Moreover, we show that misbehaving UEs have to spend
an increasing amount of energy to attack the network when
the severity of the signaling storms increases since they also
suffer from the attacks.
previous
work which either investigate the impact of different RRCbased
attacks on the mobile network in terms of signaling
overhead and delay only [7]–[11] or the impact of traffic
behavior on the energy consumption of the misbehaving UEs
only without considering other UEs [12]–[15].
相關工作：
Several existing research papers [12]–[14] have investigated
the energy consumption of UEs due to different
application traffic patterns which can also lead to signaling
storms in LTE networks. In [12], the authors model the
DRX mechanism using a semi-Markov chain to obtain the
trade-off between power consumption and various DRX
parameters such as timeout, for bursty packet data traffic.
The analytical results were also verified against simulations.
[13] also investigates the same impact factors of
the DRX mechanism but it also takes into account the
various signaling messages that are exchanged during RRC
mode transitions. The most important contribution of [14]
is the measurement of the power consumption of LTE UEs
in different operational networks around the world during
different RRC and DRX states, which we use in this work.
In addition, the authors of [14] infer the different DRX
parameters used by operators from their power and traffic
measurements which they then use to build a power model
for a LTE UE so that they can compare the power and delay
performance of a LTE UE against a 3G and WIMAX UE.
Our previous work on signaling storms in the context of
the NEMESYS project [2], [17] has involved the mathematical
modeling, simulation and analysis of the impact of different
RRC-based signaling storms in 3G/UMTS networks
[9]–[11]. In our recent work, we also investigated methods
for the detection and mitigation of signaling storms through
the use of RRC timer’s adjustment and counters [16].
LTE里信令增加的原因——群發消息、永久在線！
2.1基於TAL 的尋呼策略導致信令量成倍上升
與3G 網絡基於RU+AN 的尋呼機制不同，LTE 系統
的尋呼機制是基於TA list（跟蹤區列表）實現的。所謂TA
list 是指將原來CDMA 系統LAC（location area code，位置
區碼） 包括的基站歸類為LTE 系統最小注冊和尋呼單元，
MME 在下發尋呼消息時， 對終端最后注冊的LAC 下的所
2.2 永久在線的功能進一步推高了信令流量
在系統設計上，3G 時代由於語音與數據是分開在兩
張網絡中承載的， 故並不需要電路域中的永久在線功能；
但到了4G 時代，沒有了電路域，語音也在分組域上承載，
此時永久在線的功能就顯得尤為重要， 因此在LTE 系統
中，終端一旦開機就進行附着和默認承載的建立（IP 地址
的獲取），並且IP 地址的存活時間一直延續到終端去附着
或者是超出了PGW 設定的最大時間。眾所周知，IP 地址的
存在是尋呼信令產生的前提，IP 地址存活時間越長， 網絡
尋呼終端的信令量將越大，發生信令風暴的可能也就越大。
2.3 扁平化的系統架構加劇了信令風暴的危害——？？？去中心嗎？？？
與3G 系統架構不同的是，LTE 的系統架構少了中央
控制器節點RNC，無線基站直接對接核心網元MME。由於
MME 並不知道終端的鄰區信息，因此，3G 網絡中基於RU
和neighbor list 的尋呼在LTE 系統中不可行， 阻礙了尋呼
范圍的縮小，使得尋呼信令無法降低。
同時，由於MME 下掛的小區數遠高於（RNC），以廣東
電信LTE 網絡為例， 全省共建設兩套MME， 組成MME
pool，管理上萬個eNB，因此，一旦發生信令風暴問題，MME
處理能力將下降甚至癱瘓，影響面更廣，破壞性更大。
2.4 帶寬拓展推動移動互聯網應用的爆發式增長
根據愛立信消費者實驗室的研究報告， 如圖3 所示，
到2018 年， 全球LTE 網絡的用戶數量將超過20 億戶，年
復合增長率75%。與此同時，QQ、微信等OTT 業務的廣泛
使用，使得點對點尋呼、點對多點的組呼更為頻繁，網絡承
載的信令將出現幾何級增長。
from:《LTE 網絡信令風暴風險分析與對策研究》