In recent years, wireless channel optimization technologies witnessed tremendous improvements. In this regard, research for developing wireless spectrum for accommodating a wider range of wireless devices increased. This also helped in resolving spectrum scarcity issues. Cognitive Radio (CR) is a type of wireless communication in which a transceiver can intelligently detect which communication channels are being used. To avoid interference, it instantly moves traffic into vacant channels by avoiding the occupied ones. Cognitive Radio (CR) technology showed the potential to deal with the spectrum shortage problem. The spectrum assignment is often considered as a key research challenge in Cognitive Radio Networks (CRNs). In this paper, an evolutionary optimization algorithm is proposed for channel assignment in CRNs. Evolutionary algorithms are inspired by some type of biological evolution technique. In the proposed technology we used Particle Swarm Optimization (PSO). The resulting algorithm is called differential evolution-based particle swarm optimization with the repair process (DEPSO-RP). Moreover, a repair process is introduced to remove conflicts among secondary users (SUs) to increase the spectrum in CRNs. The performance of DEPSO-RP spectrum assignment algorithm has been evaluated by extensive simulations. The proposed spectrum assignment algorithm showed better performance regarding channel assignment in comparison with other existing algorithms in the literature.

The recent increase witnessed in the demand for wireless gadgets in daily communications for example cellular phones, wireless sensors, and tablets has risen exponentially [

Wireless spectrum is of fundamental importance when it comes to efficient network resource utilization. The licensed users known as primary users (PUs), do not utilize the spectrum all the time. The unused spectrum, therefore, can be utilized for different applications. Unlicensed users are known as secondary users [

Traditional mathematical optimization techniques such as the Lagrange multipliers requiring derivatives have difficulty in handling discrete variables [

In this paper, we present an improved hybrid algorithm for channel assignment in CRNs. Moreover, a repair process is introduced to improve spectrum utilization for those SUs which are accessing the same channel. The simulation results are analyzed and compared with other evolutionary algorithms in the literature. The objectives of this study are:

Maximizes the network utilization

Minimizes the interference among SUs

Section 2 explains the system model. Section 3 provides our proposed method. Section 4 discusses the comparisons between the proposed method and four other evolutionary methods under the same benchmark constraints. The paper is concluded in Section 5.

We consider a CRN model consists of X number of PUs and I numbers of SUs that are randomly placed as shown in

The proposed system model is illustrated in _{p} represents the protection region of each PU, whereas d_{i} represents the transmission range of each SU. We also considered orthogonal frequency division multiplexing in the proposed model. In _{p} can communicate provided that their transmission range remains in the allowed limit.

The SU-I transmission range is overlapping with protection regions of PU-I, therefore, the SU is not allowed to communicate. It is noteworthy that the secondary user’s II, III, and IV are far away from PUs so these users can communicate with each other. Although, SU-V and SU-VI are far away from PUs their transmission range is overlapping so they also do not qualify to access the free spectrum.

The SU_{i} can utilize the channel _{p} as long as Dist(i, m)-d(p, m)≥d(i, m), where d(i, m) represents the transmission range of SU_{i} on channel m, Dist(i, p) is the distance between SU_{i} and PU_{p}, and d(p, m) is the transmission range of PU_{p} using channel m.

First, let us define a binary channel availability matrix L_{(I×M)} in which each element l_{(i,m)} = 1 indicates that SU_{i} can use channel m only if its transmission range is below the allowed limit, otherwise l_{(i,m)} = 0. Let A_{(I×M)} represents the binary channel assignment matrix. In this matrix, if a_{(i,m)} = 1 then channel m is assigned to SU_{i}; otherwise a_{(i,m)} = 0. The channel assignment must satisfy interference constraint C = {c_{i,k,m}} which is based on distances between SUs. If two SUs i and k are competing for the same channel m, the channel accessing criteria is

In _{i,k,m}= 0 represents SU_{i} and SU_{k} are not interfering with each other. It indicates that SU_{i} and k are far away from each other. Conversely, if c_{i,k,m} = 1, only one SU can use the channel m. The data rate r_{i,m} of SUi using the channel, m is based on signal to noise γ_{i,m}

The spectrum utilization of SUs in a CRN is defined by

_{i} using channel m is maximized subject to fulfill the above constraints. In _{max} corresponds to the upper limit of channels that an SU_{i} can use. In _{min} and d_{max} represents the minimum and maximum transmission range allowed to SUs. If the SU transmission range is d_{i} < d_{min}, the SU is not qualifying to access free channels because its transmission range is below the transmission threshold. Similarly, if d_{i} > d_{max}, the SU is not allowed to communicate because large transmission range d_{i} may result in more interference with neighboring SUs. Spectrum utilization mentioned in

In DEPSO, the PSO is integrated with the differential evolution (DE) algorithm to form a hybrid approach. PSO is a population-based algorithm [_{i} of SU_{i} is adjusted using PSO

In _{i} represents the velocity component of SU_{i} and is updated using

where y_{i} (t) is own best position of particle i, _{1} and c_{2} are amplifying coefficients, and r_{1}, r_{2} ∈ U(0, 1).

DE is a population-based evolutionary algorithm [_{i} (t), select _{1}, x_{2}, and x_{3} are randomly selected positions of particles from the population. The position of SU_{i} is updated based on DE and is expressed mathematically as follows

The position update as follows

In _{i}(t + 1) of the particle, i is updated from x_{i}^{PSO} facilitating exploitation while for a proportion of the population is updated by x_{i}^{DE} facilitating exploration. In

One of the important challenges is mapping between problem and solution. Here, particles correspond to SUs, and positions of particles correspond to the channels accessing by SUs as shown in

In the proposed DEPSO-RP spectrum assignment algorithm, the velocity v__{im} represents the movement of the particle SU_{i} using channel m from the current slot to the next slot. It is assumed that SUs can move three slots forward or backward. For example, if a SU currently connected with the 3rd channel, in next-generation, that SU can join channels 2, 4, or 6.

The algorithm starts to generate a random solution according to the interference constraint defined in section II. The algorithm sequentially assigns the available channels to SUs. The next step is to ensure that each feasible channel should be assigned to only one SU at a given time instant.

The repair process will be performed for SUs to replace their positions. As shown in ^{th} slot so that there is no conflict now between these two SUs. By doing so, the spectrum utilization for SUs is improved.

Simulations are performed to evaluate the efficiency of the proposed solution. The parameter values for the simulations are represented in

Parameters | Value |
---|---|

10 | |

2 | |

2 | |

0.7 | |

1.5 |

The performance of spectrum utilization against a varying number of channels is analyzed and is shown in

If the number of SUs is increased in a fixed area, spectrum utilization would surely be reduced. The performance of DEPSO-RP for spectrum utilization is analyzed against the increasing number of SUs as shown in

Similarly, the number of PUs would reduce the chances of SUs for obtaining channels. The results are shown in

The faraway SUs from PUs can improve their spectrum utilization by increasing their transmission range d_max. However, this increasing transmission power can cause more interference to adjacent SUs. So it depends on SUs location whether to increase their transmission range or not beyond a certain level. In _{max}. The results show that varying transmission range changes the value of spectrum utilization significantly.

In _{min} = d_{max} is evaluated in _{min} = d_{max} = 5. As shown from the results, again DEPSO-RP outclasses the other understudied channel assignment algorithms. The simulations are conducted on a core-i5 processor with 6 GB of RAM.

The above results demonstrate that the DEPSO-RP algorithm converged to the best solutions much faster than the other understudied algorithms. The performance of DEPSO-RP is improved due to two reasons. First, it combines the good characteristics of PSO and DE. Second, the repair process improved the spectrum utilization for conflicting SUs.

The recent demand for the availability of wireless spectrum has been increased dramatically. It is due to the vast-scale invention of various wireless technologies. Spectrum allocation is a key research problem in CRNs. In this paper, a repair process-based channel assignment mechanism is presented. It aims to optimize spectrum utilization. The performance of the proposed algorithm is evaluated against different network topologies with varying numbers of PUs, SUs, and channels. Moreover, the results of the proposed solution are also compared with other state-of-the-art evolutionary algorithms. Simulation results demonstrate that the proposed DEPSO-RP spectrum assignment algorithm has improved CRN spectrum utilization.