5G蜂窝网络中未授权频谱的流量卸载：双层博弈方法
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"5G移动网络中的未授权频谱流量卸载：双层博弈方法"
这篇论文探讨了5G蜂窝网络中利用未授权频谱进行流量卸载的问题，采用了一种双层博弈（two-layer game）的方法。5G网络面临的主要挑战之一是满足不断增长的数据需求，而未授权频谱的使用成为了解决这一问题的关键。其中，授权辅助接入（LAA, Licensed Assisted Access）是一种创新技术，旨在为5G提供高性能服务。
LAA技术允许5G网络在未授权的频谱上运行，例如Wi-Fi使用的频段，以增加网络容量。为了实现与Wi-Fi系统的和谐共存，论文提到了两种共存策略：监听前传输（LBT, Listen Before Talk）和载波感知自适应传输（CSAT, Carrier Sensing and Adaptive Transmission）。这两种方法有助于有效地分享频谱资源，并且对Wi-Fi友好。然而，它们在实际应用中可能会存在碰撞问题，尤其是在LTE设备开始在CSAT模式下传输数据时。
为了解决这个问题，论文提出了一种改进的基于LBT的CSAT方案，该方案能有效降低传输过程中的碰撞概率，从而提高未授权频谱的使用效率。此外，为了进一步优化频谱资源的利用率，论文还引入了一个两层的联盟拍卖游戏交易（CAGT, Coalition-Auction Game-based Transaction）机制。
在第一层游戏中，LAA和Wi-Fi系统被视为独立的参与者，它们通过拍卖机制竞争未授权频谱资源。每个系统都会根据自身的业务需求和性能目标来决定如何分配和使用这些资源。第二层游戏则是在联盟形成的基础上，通过联盟间的交互和交易来协调不同系统间的利益冲突，以实现整体吞吐量的最大化。
这种双层博弈策略不仅考虑了系统间的竞争，还考虑了合作，使得所有参与者都能在有限的频谱资源中找到最优的平衡点。通过这种方式，LAA和Wi-Fi系统的吞吐量可以得到提升，同时确保了公平性和效率。
总结起来，这篇论文的核心贡献在于提出了一种结合改进CSAT策略和CAGT机制的解决方案，旨在优化5G蜂窝网络在未授权频谱上的流量卸载，促进不同无线技术之间的协同工作，以实现高效、公平的频谱共享。这种方法对于未来5G网络的发展具有重要的理论和实践意义。
Entropy 2018, 20, 88 5 of 22
However, too many stations will increase the collision probability and decrease the capacity [
23
].
Conversely, too few stations will not make full use of the channel spectrum resource.
Thus, the number
of stations in one AP should be carefully determined to maximize the capacity of the WiFi system.
To best utilize the spectrum resources and maximize the system capacity, a coalition game among
WiFi APs in the first layer of our CAGT optimization scheme is designed, and our goal is to
optimize the station number
n
∗
in one AP coalition to maximize the throughput of the WiFi system.
Accordingly, we reshape (2) and the optimization problem can be formulated as
max
n∈N
+
R =
anτ(1 − τ)
n−1
E[P]
(T
σ
− T
c
)(1 − τ)
n
+ (T
S
− T
c
)nτ(1 − τ)
n−1
+ T
c
, (5)
where
a =
T
o f f
T
on
+T
o f f
. Define
f (n) = anτ(
1
− τ)
n−1
E[P]
and
g(n) = (T
σ
− T
c
)(
1
− τ)
n
+ (T
S
− T
c
)nτ(
1
−
τ)
n−1
+ T
c
. Thus, Equation (5) can be expressed as
R =
f (n)
g(n)
=
− f (n)
−g(n)
, where
− f ( n)
is concave and
−g(n)
is convex, which are judged by the Hessian Matrix. Then, according to [
26
], the function
R
is
quasi-concave. Thus, the optimization problem can be expressed as
η(n)
n∈N
+
= max
n∈N
+
R =
f (n)
g(n)
. (6)
As mentioned in [
27
], any optimization problem in fractional form can be transformed into an
equivalent optimization problem in a subtractive form. Hence, the non-linear fractional optimization
problem in Equation (6) can be transformed into a parameterized function and the optimal solution
can be determined by finding the root to the
U(n)
as shown in Equation (7) using various root finding
methods [28,29],
U(n) = max
n∈max
{b
R
+
c
,
d
R
+
e}
{
− f ( n) − η(−g(n))
}
= max
n∈
{b
R
+
c
,
d
R
+
e}
{
− f ( n) + ηg(n)
}
. (7)
The solution to Equation (7) could be formulated as an iterative two-layer solution combining
Dinkelbach type method and Lagrangian dual decomposition approach. In addition, this problem can
be solved by mathematical tools such as CVX or other build-in functions in MATLAB [29].
Due to concavity of the function analyzed above, balancing the stations and making the station
number as close as the optimal value
n
∗
could improve the performance of the whole WiFi system.
Therefore, we form a coalition game by encouraging individuals in the WiFi system to form groups in
the first layer of CAGT mechanism, and then make a traffic transfer between LAA and WiFi in the
second layer of CAGT.
3. Modified CSAT Mechanism
Before discussing the CAGT scheme, the basic and modified CSAT will be devised firstly.
The primary principle of CSAT is to let LTE in unlicensed bands cycle between state “on” and “off”
within a given single cycle while the LTE in licensed band remains connected all the time. During the
activated state “on”, LTE transmits data on the unlicensed band and blocks the transmission of
WiFi systems. However, within the deactivated state “off”, LAA shuts down its transmission to
yield the channel sources to WiFi [
11
], just as shown in Figure 2. Adjusting “on” and “off” time
dynamically by identifying the number of WiFi terminals can be an affordable way for proper “on”
and “off” time assignment and meet better needs between system aggressiveness and fairness [
12
].
However, WiFi nodes
may not be aware of the LAA activity in some actual scenarios. For example,
WiFi causes interference to LTE in the “on” state period since WiFi cannot identify the structure
of signals from non-IEEE systems. When the Energy Detection for Clear Channel Assessment
(CCA-ED) measurement is below the predefined inter Radio Access Technology (inter-RAT) threshold,
the ongoing LAA messages cannot be identified and will be regarded as noises. Therefore, a LBT
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