节能高效的机会约束组播认知OFDM网络资源分配策略
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"这篇研究论文探讨了在组播认知正交频分复用(OFDM)网络中的节能高效机会约束资源分配问题。该问题通过一个概率约束编程模型来建模，考虑了服务质量要求、总功率限制以及概率干扰限制。文章作者为Lei Xu和A. Nallanathan，他们在 IEEE Journal on Selected Areas in Communications 上发表，尽管具体刊期信息未给出，但已知论文已被接受待发布。"
正文:
在现代通信网络中，尤其是在多用户环境下的组播服务中，资源分配策略是至关重要的。组播认知OFDM网络结合了多播传输的效率与认知无线电的智能利用空闲频谱资源的能力，旨在提高网络性能同时减少能耗。本文的研究主要关注如何在满足服务质量(QoS)、总发射功率限制以及避免对主用户(PU)造成干扰的概率约束条件下，实现这样的高效能、低能耗的资源分配。
首先，论文提出了一个基于机会约束的资源分配模型。在认知无线电网络中，机会约束允许次级用户(SU)在不损害主用户服务的前提下利用空闲频段。这种模型考虑了随机的信道状态信息(CSI)，即CBS与PU之间的信道状态可能存在的不确定性。通过引入概率约束，可以确保SU的使用不会超过预先设定的干扰容忍阈值，从而降低对PU通信的影响。
其次，为了实现能源效率，论文中的优化目标是在满足上述约束条件下，最小化整个网络的总能耗。这需要在保证服务质量的同时，寻找最佳的功率分配策略和频谱分配方案。这样的优化问题通常具有非线性和复杂的数学结构，需要采用先进的算法求解。
论文可能探讨了各种可能的解决方案，包括动态规划、随机优化算法或者启发式方法，这些方法能够处理复杂的约束条件并找到近似最优解。此外，论文可能还分析了不同约束条件对能耗和网络性能的影响，比如QoS要求的严格程度、总功率预算的大小以及PU容忍的干扰水平。
最后，论文可能会通过仿真结果展示所提出方法的性能优势，比较其与其他传统资源分配策略的差异，并验证在实际网络环境中，该方法是否能够实现预期的节能效果和性能提升。
这篇研究论文深入探讨了组播认知OFDM网络中资源分配的关键问题，为实现能源效率和避免对主用户的干扰提供了新的思路和方法。其研究成果对于未来认知无线电网络的设计和优化具有重要的理论和实践价值。
0733-8716 (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSAC.2016.2520180, IEEE Journal
on Selected Areas in Communications
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. XX, NO. XX, MONTH 2016 1
Energy-Efficient Chance-Constrained Resource
Allocation for Multicast Cognitive OFDM Network
Lei Xu and A. Nallanathan Senior Member, IEEE
Abstract—In this paper, an energy-efficient resource allocation
problem is modeled as a chance-constrained programming for mul-
ticast cognitive orthogonal frequency division multiplexing (OFDM)
network. The resource allocation is subject to constraints in service
quality requirements, total power and probabilistic interference
constraint. The statistic channel state information (CSI) between
cognitive based station (CBS) and primary user (PU) is adopted
to compute the interference power at the receiver of PU, and
we develop an energy-efficient chance-constrained subcarrier and
power allocation algorithm. Support vector machine (SVM) is
employed to compute the probabilistic interference constraint. Then,
the chance-constrained resource allocation problem is transformed
into a deterministic resource allocation problem, and Zoutendijk’s
method of feasible direction is utilized to solve it. Simulation results
demonstrate that the proposed algorithm not only achieves a tradeoff
between energy efficiency and satisfaction index, but also guarantees
the probabilistic interference constraint very well.
Index Terms—Multicast cognitive OFDM network, energy efficien-
cy, SVM, Zoutendijk’s method of feasible direction.
I. INTRODUCTION
C
OGNITIVE radio (CR) network can solve the spectral
resource scarcity problem, where CR user is permitted to
access the PU’s spectrum by controlling the interference power
[1]–[5]. Since video traffic is becoming more and more popular
in recent years, the future wireless communication system re-
quires high transmission rate. In addition, OFDM and multicast
technologies can further enhance the spectral efficiency [6], [7].
Hence, multicast cognitive OFDM network improve quality of
experience (QoE) for CR users greatly.
In multicast cognitive OFDM network, there are some chal-
lenges to design the resource allocation algorithms [8]–[11]. For
example, the same data is transmitted from the CBS to multiple
CR users at the same subcarriers in a multicast group, and it
leads to the mismatching data rates for different CR users in
the same multicast group, due to their asymmetric channel gains.
With maximizing the expected sum rate, a risk-return model is
used to design a distributed joint subcarrier and power allocation
algorithm [8]. The multicast model in [8] is usually based on
the full buffer traffic and it does not consider the nature of
limited traffic. Taking this into account, a distributed resource
allocation algorithm, based on the Lagrangian dual decomposi-
tion, is proposed [9]. In [10], [11], multiple description coding
is combined with multicast cognitive OFDM network, and two
heuristic distributed resource allocation algorithms to maximize
the weighted sum rate are proposed.
One limitation with the existing radio resource allocation
algorithms in [8]–[11] is that they only maximize the spectral
efficiency. However, the energy efficiency is very important due to
Lei Xu is in School of Computer Science and Engineering, Nanjing University
of Science and Technology, Nanjing, China (E-mail: xulei marcus@126.com).
A. Nallanathan is with the Department of Informatics, King’s College London,
London WC2R 2LS, U.K. (nallanathan@ieee.org).
steadily rising energy consumption in the communication network
and environmental concerns. On the other hand, the perfect CSI
is assumed in [8]–[11], and the interference power at the receiver
of PU can be calculated precisely. However, the cooperation
between cognitive network (CN) and primary network (PN) is not
perfect, which is assumed in unicast cognitive OFDM network
[12]. This leads it is difficult to estimate the CSI between CR
users and primary base station precisely, and only the statistic
CSI between CR users and primary base station can be used.
Additionally, PU does not belong to the management of CBS,
and channel estimate between CBS and PU increases the control
overhead and the management complexity. Hence, we adopt the
statistic CSI between CBS and PU to perform the resource
allocation algorithm like [12]. Chance-constrained programming
is developed by Charnes, and it offers a powerful mean of
modeling stochastic wireless network by specifying a confidence
level [13]. Since the statistic CSI between CBS and PU is adopted,
the subcarrier and power allocation based on the statistic CSI
for multicast cognitive OFDM network is casted into a chance-
constrained programming problem. Different from the determined
resource allocation problem, the probabilistic constraint needs to
be calculated.
In this paper, we propose an energy-efficient chance-
constrained resource allocation algorithm for multicast OFDM
network. Specially, we summarize the contributions of this paper
as follows:
(i) An energy-efficient subcarrier and power allocation problem
is formulated as a chance-constrained programming for multicast
cognitive OFDM network, with minimum required QoS and the
probabilistic interference constraints.
(ii) Using the SVM method, the energy-efficient chance-
constrained resource allocation problem is transformed into a
deterministic optimization problem, which can be solved by
Zoutendijk’s method of feasible direction.
(iii) The performance of the proposed algorithm is evaluated
in comparison with upper bounder → 0, lower bounder
→ 1, max-min algorithm and unicast case. Simulation results
demonstrate the proposed algorithm not only improves the energy
efficiency, the total throughput and QoS satisfaction index, but
also satisfy the probabilistic interference constraint.
The rest of the paper is organized as follows. The system model
and energy-efficient resource allocation problem are presented in
section II and III, respectively. The chance-constrained resource
allocation algorithm is in section IV. Section V gives the com-
putational complexity and signaling overhead. Simulation results
and conclusions are given in section VI and VII, respectively.
II. SYSTEM MODEL
Consider a primary base station to communicate with N
primary users at M OFDM subcarriers, and a CBS is allowed to
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