The next step in mobile communication technology, known as 5G, is set to go live in a number of countries in the near future. New wireless applications have high data rates and mobility requirements, which have posed a challenge to mobile communication technology researchers and designers. 5G systems could benefit from the Universal Filtered Multicarrier (UFMC). UFMC is an alternate waveform to orthogonal frequency-division multiplexing (OFDM), in filtering process is performed for a sub-band of subcarriers rather than the entire band of subcarriers Inter Carrier Interference (ICI) between neighbouring users is reduced via the sub-band filtering process, which reduces out-of-band emissions. However, the UFMC system has a high Peak-to-Average Power Ratio (PAPR), which limits its capabilities. Metaheuristic optimization based Selective mapping (SLM) is used in this paper to optimise the UFMC-PAPR. Based on the cognitive behaviour of crows, the research study suggests an innovative metaheuristic optimization known as Crow Search Algorithm (CSA) for SLM optimization. Compared to the standard UFMC, SLM-UFMC system, and SLM-UFMC with conventional metaheuristic optimization techniques, the suggested technique significantly reduces PAPR. For the UFMC system, the suggested approach has a very low Bit Error Rate (BER).

A wide range of applications, such as the 4G to 5G evolution, Internet of Things (IoT) [

When used in conjunction with OFDM, SLM is a well-known PAPR reduction method [

To reduce PAPR in UFMC, we suggest an OSLM (optimal SLM). Replacement of the constant for active sub-carrier in UFMC with complex number of ej represents the idea. Phase factor must be carefully selected in order to reduce the PAPR of the subsequent time series, and this is accomplished by solving a non-linear optimization problem.

Another special situation of the suggested OSLM, in which equals zero, is also examined. Linear Integer Programming is used to approximate the optimization problem in this scenario. Estimated and original solutions are compared. Additionally, we devised a low-complexity iterative method for the suggested problem.

In conclusion, we demonstrate that the suggested PAPR reduced UFMC has the same maximum likelihood (ML) detector as the standard UFMC. As a result, there is no loss in BER performance between the suggested approaches and the original non-coherent OFDM-IM.

It is a multicarrier technology that exchanges high data rates into parallel low data rates. Gray-coded 16-QAM N symbols, designated as S, are separated into M subbands to represent the data bits b. As a final step, the frequency-domain symbols Si are transformed into a time-domain signal s i by the use of an N-point IFFT (inverse fast Fourier transform). A length L is applied to each sub-band, and these time-domain signals are combined to give the output signal x of length N + L − 1 as depicted in

where

Matrix conversion can also be used to describe the transmitted signal x as

where

Let’s pretend that the frequencies are perfectly synchronised at this point. An N − L + 1 padding is used to zero pad the received signal before employing a 2-point FFT to turn the received sequence into the signal y (earlier channel equalisation) which may be signified as a vector.

where

SLM-UFMC model incorporating PAPR difficult and projected efficient SLM-UFMC scheme are discussed in this part.

where, V signifies the signal with the lowest PAPR from the set U,

where

The transmitted signal

where

Crows (corvids) are regarded as the most intellectual birds in the animal kingdom. They have the greatest brain-to-body ratio of any mammal On the basis of a human brain-to-body ratio, they have a smaller brain than us. Many examples of crows’ ingenuity can be found. In mirror tests, they have shown that they are aware of their own bodies and have the ability to make tools Unlike humans, crows are able to recognise each other’s faces and warn each other when an unpleasant one is near. As a result, they are able to use tools, communicate in sophisticated ways, and remember where their food is hidden for months at a time.

The optimization problem, as well as the various choice variables and constraints, are all well stated. When the CSA parameters (flock size (

As the

Each crow’s memory is set up in the beginning. Because the crows have no previous experience, it is presumed that they have hidden their food in the same places they did at the beginning of the

By putting the values of the choice variables into the objective function, we can calculate the quality of each crow’s position.

Suppose that crow I is looking to create a new role. In order to reach its goal, this crow picks a random crow from the flock and follows it to find the concealed food (mj). Eq. determines the new location of crow i.

where

Each crow’s new position is tested for its viability. If a crow’s new location is possible, the crow updates its position. Instead of moving to its new location, the crow remains at its current location.

Each crow’s new position is assigned a fitness function value.

The crows’ memories are updated as follows:

where

If the novel position of a crow has a better fitness function value than the one it has learned, the crow refreshes its memory by using the new location.

Once

Assuming an AWGN channel, random data bits with QAM and 64 QAM modulation techniques are utilised to appraise and compare the proposed hybrid system performance. Initially, to ensure the proposed UFMC technique BER performance, UFMC is compared with conventional OFDM, FBMC, WOLA and FOFDM systems. To ensure the proposed UFMC system Pre-allocate Transmit Power and Pre-allocate Power Spectral Density are measure and plotted. The performance of the Optimized SLM UFMC technique, simulation results of PAPR and BER plots are presented. All the system evaluations are made with the Number of Monte Carlo repetitions over which we take the average is 1000. Simulation SNR in dB is

FBMC | |

Parameter | Value |

Number of subcarriers | 24 |

Number of symbols in time | 30 |

Subcarrier spacing | 15e3 |

Proto type filter | Hermite OQAM |

Overlapping factor | 4 |

OFDM | |

Number of subcarriers | 24 |

Number of symbols in time | 14 |

Subcarrier spacing | 15e3 |

Cyclic prefix length | 1/(14 * OFDM subcarrier spacing) |

WOLA | |

Number of subcarriers | 24 |

Number of symbols in time | 14 |

Subcarrier spacing | 15e3 |

Cyclic prefix length | 0 |

Window length TX and RX | 1/(14 * 2 * WOLA subcarrier spacing) |

FOFDM | |

Number of subcarriers | 24 |

Number of symbols in time | 14 |

Subcarrier spacing | 15e3 |

Cyclic prefix length | 1/(14 * FOFDM subcarrier spacing) |

Filter length TX and RX | 0.2 * 1/(FOFDM subcarrier spacing) |

UFMC | |

Number of subcarriers | 24 |

Number of symbols in time | 14 |

Subcarrier spacing | 15e3 |

Cyclic prefix length | 0 |

Filter length TX and RX | 1/14 * 1/(UFMC subcarrier spacing) |

Filter cyclic prefix length | 1/(14 * UFMC subcarrier spacing) |

System evaluation and comparison are done in this section, with the suggested model being compared to original SLM UFMC systems. The CCDF, which is distinct as the probability of PAPR exceeding a specific threshold PAPR0, is used to quantify PAPR reduction capacity. According to [

In order to reduce PAPR, an effective UFMC SLM optimization technique is presented in this study. An SLM-UFMC system with PAPR reduction capabilities that outperforms any other system has been demonstrated by simulations. In order to reduce PAPR and BER, we devised an optimization problem. The suggested CSA with SLM-UFCM scheme reduces PAPR significantly compared to other schemes, according to simulation findings. Heuristic schemes have also been found to have a lower PAPR than previous SLMs and are beneficial for real-time implementations, as has been demonstrated. While this system’s principal advantage comes from using the side information index to de-randomize the data it receives, it is not without its drawbacks.