动态频谱租赁：认知无线电网络中的能源控制策略

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"这篇论文探讨了在认知无线网络中通过主次用户功率控制游戏实现动态频谱租赁的方法，旨在提高频谱利用率并让主用户因允许次用户使用其授权频谱而得到奖励。" 在认知无线电网络中，动态频谱租赁（Dynamic Spectrum Leasing, DSL）是一种创新的频谱管理策略，它旨在解决传统通信系统中的频谱稀缺问题。与传统的分层动态频谱接入（Hierarchical Dynamic Spectrum Access, DSA）不同，后者主要关注如何控制次级用户的传输功率，DSL则引入了主用户的角色，让他们因允许次级用户使用其授权频谱而获得利益。该论文由Sudharman K. Jayaweera和Tianming Li提出，发表在2009年6月的《IEEE Transactions on Wireless Communications》第8卷第6期中。他们构建了一个游戏理论框架，使主次用户都能参与动态频谱租赁过程。在这个框架中，主用户不仅仅是频谱的被动管理者，而是主动参与者，他们有权决定何时以及在多大程度上允许次级用户进入其频谱，以此获得奖励。在认知无线电的功率控制游戏中，以往通常只考虑次级用户作为玩家的行为。然而，DSL网络中，主用户也成为了游戏的一部分。这种模式增加了频谱使用的灵活性，同时也确保了主用户的权益，因为他们可以通过租赁频谱获取经济或其他形式的回报。论文提出的DSL网络模型鼓励主用户尽可能多地让次级用户使用频谱，这有助于提升整体网络的效率和容量。通过这种方式，主用户可以在不影响自身服务的前提下，利用闲置的频谱资源，同时次级用户也能找到更多的可用频谱进行通信，降低了频谱资源的浪费。此外，该文还可能涉及了如何设计公平且有效的激励机制，以确保主次用户之间的合作是双赢的，以及如何通过优化算法来协调双方的功率控制策略，达到频谱利用率的最大化。论文可能会讨论各种可能的博弈策略，如合作博弈、非合作博弈，以及如何在这些博弈模型中平衡竞争与合作，以实现网络性能的优化。这篇研究工作为认知无线电网络的频谱管理提供了一种新的视角，通过动态频谱租赁促进了主次用户的互动，有望推动未来无线通信系统的高效和公平使用。

3302 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 8, NO. 6, JUNE 2009

Secondary

Cognitive Radio K

Secondary

Cognitive

Radio 2

Secondary

Cognitive

Radio 1

Secondary

User Base

Station

Primary User Base

Station

Primary User

Transmitter

Fig. 1. The primary-secondary user communications system model.

although we limit ourselves to one primary transmitter for the

simplicity of exposition, the p roposed scheme can be extended

to include more than one primary user. Since the key ingre-

dient in the proposed concept of dynamic spectrum leasing is

the interaction between the primary system and the secondary

system, a single primary user is enough to demonstrate the key

aspects of dynamic spectrum leasing while avoiding extrane-

ous complications. However, in a realistic network, of course,

there will be multiple primary transmitters and their own

internal interactions will add an important dimension to th e

problem of dynamic spectrum leasing. There is one primary

receiver and one common secondary receiver in the system

(again,generalization to more than one is possible). The cross

correlation coefﬁcients between the signalling waveforms of

the k-th secondary user and that of a primary user is denoted

by ρ

, between a primary user and the k-th secondary user

is by ρ

and between the k-th and the j-th secondary

users is by ρ

for all k, j ∈{1, ···,K}. For simplicity,

throughout we will assume that ρ

= ρ

,forall

k ∈{1, ···,K}. The channel gain between the k-th secondary

user and the common secondary receiver is h

, between the

k-th secondary user and the primary receiver is h

, between

the primary user and the primary receiver is h

, and between

the primary user and the common secondary receiver is h

In the proposed formulation, the p rimary user can adapt its

interference cap, denoted by Q

, which is the maximum total

interference the primary user is willing to tolerate from all

secondary transmissions. However, the primary user should

always ﬁrst strive to achieve its target SINR to ensure its

required QoS. This is an important constraint in the concept of

dynamic spectrum leasing since it is expected that the primary

system should ﬁrst focus on its communication needs and

spectrum leasing is only an option to improve the spectrum

utilization. Note that, the QoS requirement in conjunction with

the c hosen interf e rence cap will directly determine the primary

user’s transmit power level. By adjusting the interference cap,

the p rimary user can indirectly control the total transmit power

the secondary users impose on the channel at any given time.

All secondary users adapt their transmission powers to achieve

a certain transmission quality. However, their transmission

powers must be carefully controlled in order to ensure low

interference to the primary user (within the a llowed interfer-

ence cap) as well as to other secondary users. We use P

and

to represent transmission powers of the primary user and

the k-th secondary user, respectively.

In the above cognitive dynamic spectrum leasing network,

the primary and secondary users interact with each other by

adjusting their actions in response to those of the others:

the primary u ser by adjusting its interfere nce cap (which, in

turn, determines its transmit power) and the secondary users

by controlling their transmit power levels. In essence, both

primary as well as secondary users act as rational decision

makers, thereby making game theory a n atural framework to

analyze and predict the behavior of this system. Formally, we

model our proposed scheme as the following non-cooperative

game:

1) Players: K = {0, 1, 2, ..., K},where0-th user is taken

to be the prima ry user and k =1, 2, ..., K represents the

k-th secondary user.

2) Action space: P = Q×P

×P

... ×P

,whereQ =

[0,

] represents the p rimary user’s action set and P

[0,

],fork =1, 2, ..., K, represents the k-th secondary

user’s action set.

and

represent, respectively, the

maximum allowed interference cap of the primary user

and the maximum allowed transmission power of the

k-th secondary user. The action vector of all users is

denoted by p =[Q

, ..., p

],wherep

∈P

and

∈Q. The action vector excluding the action of the

k-th user, for k =0, 1, 2, ..., K, is customarily denoted

by p

−k

3) Utility function: We use u

, p

−k

) , ∀k =1, 2, ..., K

to represent the k-th secondary user’s utility function

and u

, p

−0

) to represent the primary user’s utility

function.

Throughout this paper, we assume that the primary receiver

is based on a matched-ﬁlter detector since we are limiting our-

selves to a primary system with only a single user. However,

it is possible to modify the proposed scheme for situations

in which the primary receiver can b e an ad vanced multiuser

detector, as will be required when one considers a primary

system with multiple transmitters. A ssuming a matched- ﬁlter

based primary receiver, the primary user’s target SINR is

deﬁned as:

¯γ

+ σ

, (1)

where P

and Q

represent the primary user’s transmission

power and its chosen interference cap, respectively, and σ

the variance o f the additive noise at the primary receiver. Note

that, since Q

is the maximum interference from secondary

users the primary user is willing to tolerate at any given

time, ¯γ

in (1) represents the worst-case transmission quality

the primary user can expect with its chosen Q

. Since this

worst-case SINR needs to guarantee a required QoS constraint,

the prima ry user’s tr ansmit power is thus directly determined

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