A Brief Reading Report about Cognitive Radio Networks

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A Brief Reading Report about Cognitive Radio Networks
Fenglin Luo
Ⅰ INTRODUCTION
With the rapid growth of wireless technologies, current spectrum scarcity has
become a serious problem. Cognitive radio (CR) is a promising solution to the
problem of spectrum scarcity. Devices using CR technology can automatically detect
and use the idle spectrum resources. In order to ensure that the overall system
performance is maximized, it is necessary to design an effective spectrum sharing
scheme, so that the secondary user has the opportunity to use the primary user's idle
spectrum without affecting the normal communication of the primary user. Therefore,
the resource allocation of CR has received many research attractions .
Ⅱ BRIEF REPORT
ZHAO et al. formulated a Non-cooperative Power Control game model by
considering an underlay spectrum sharing method in [1] . On the basis of this model, a
logic utility function based on signal-to-interference-noise ratio and a novel algorithm
suitable for CR network power control are further developed. The proposed new
iteration algorithm can not only meet the QoS requirements, but also reduce the
number of iterations. Finally, the value indicates the power allocation algorithm
enables secondary users to iteratively adjust their powers to approach an nash
equilibrium.
In [2], centralized cognitive architecture under interference temperature
limitation was proposed to achieve the best cognitive network performance. Based on
the practical model of multi-users, the problem of conversion of power allocation
converted to nonlinear programming is studied. In order to effectively solve this
nonlinear programming problem, an improved simulated annealing genetic algorithm
is proposed, which can make iterative rapid convergence and meet the transmitter
power requirements of cognitive wireless networks.
An AF-relay-aided CR network that is operated with the OFDM scheme and the
underlay spectrum sharing strategy was studied in [3]. The problem of resource
allocation is analysed in this network. Joint subcarrier pairing and power allocation to
maximize SU’s throughput. In addition, in order to reduce the computational
complexity, a sub-optimal resource allocation algorithm is proposed. Finally, the
results show that the proposed algorithm is superior to ordinary algorithms in
improving network performance.
In [4], a method to optimize the cooperative spectrum sensing performance by
designing the relay function is proposed. This paper focused on not only local
cooperative perception and global cooperative perception, but also an estimation and
forwarding relay function. And the results show that the performance of the proposed
spectrum sensing algorithm is significantly better than the existing algorithms.

In recent years, cooperative relaying has been widely used in cognitive radio for
spectrum sharing and sensing in cognitive radio, [5] analysed an OFDM spectrum
sharing protocol to optimize resource allocation strategy. And then, joint optimization
of the set of subcarriers used for cooperation, subcarrier pairing, and subcarrier power
allocation to obtain the optimal transmission rate of the secondary system. In addition,
the main system is given higher priority to achieve its target transmission rate. Finally,
the dual optimization method is used to solve the joint optimization problem. The
simulation results show that the method is effective.
Different with traditional full-duplex(FD) relaying ,a FD opportunistic spectrum
sharing protocol was proposed in [6]. In this protocol, the auxiliary system can act as
the decode-and-forward(DF) relay of the main system, and only works in FD mode in
the first phase. On this basis, distributed auxiliary user selection and resource
allocation strategies are studied, and the maximum transmission rate is obtained
through joint optimization of subcarriers and power allocation.
In [7], Zhao et al considered the scenario that the PU transmits with multiple
levels of power and it is much closer to the reality, and proposed a joint power control
algorithm with multiple primary transmit powers, taking the total system bandwidth
and transmit power of cognitive users as the most optimal joint goal.
Since the STBC MC-CDMA system can effectively use spectrum resources and
improve the throughput of the system, [8] proposed a power control algorithm based
on Asynchronous Distributed Pricing Algorithm with the space-time block coded
multi-carrier code division multiple access (STBC MC-CDMA) system in cognitive
radio networks.
A TRS scheme to select the best relay for the cooperative transmission is
proposed in [9]. The proposed timer model can be implemented decentralized and
requires no global knowledge of the wireless condition across all possible paths.
A distributed uplink power control scheme for a shared spectrum two-layer
heterogeneous network is proposed in [10]. Furthermore, a convex optimization
problem was proposed to study the interference problem of femtocells, and a
closed-form solution of the power allocation strategy was obtained. The paper
concludes with a closed-form solution to make resource allocation available in the
actual communication system.
Hybrid spectrum sharing has the advantage that CUs can dynamically switch
between Overlay and Underlay states according to channel's state, so it has become a
research hotspot in recent years. Zhao et al proposed a joint power and bandwidth
allocation algorithm based on the CUs positions changing under the condition of
hybrid spectrum sharing in CR in [11]. Finally, it is proved that the proposed
algorithm is superior to the average bandwidth allocation method and single spectrum
sharing in many aspects.
The price-based power allocation for femtocells underlaying a macrocell
heterogeneous cellular network is studied in [12]. The interference management
problem is formulated as a Stackelberg game by exploiting interference pricing
mechanism. Finally, the researchers proposed a distributed interference pricing
algorithm to solve the unified interference pricing problem.

Many studies have shown that multi-target parameter adjustment can effectively
improve the performance of cognitive radio (CR) systems. [13] considers the
shortcomings of the universal genetic algorithm in the adjustment of CR multi-object
parameters, and proposes an improved genetic algorithm with linear scale
transformation, adaptive crossover probability and mutation probability. The proposed
algorithm can reach the global optimal solution faster than the ordinary genetic
algorithm in CR application.
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