What Are The Advantages And Disadvantages Of Multiple Antena

Decent Essays
3.9.3 Advantages of multiple antenna systems
Array gain: Array gain is the average increase in the SNR at the receiver that arises from coherent combining effect of Multiple Antennas. The signals arriving at the receiver have different amplitudes and phases. The receiver can combine the signals coherently to enhance the resultant signal. This can improve the reliability, and hence the capacity of the system.
Spatial Diversity (SD) gain: Signal power will fluctuate in a wireless channel. When signal power drops significantly the channel is said to be in thefade. Diversity is used to combat fading. Spatial diversity is the supply of multiple, independent copies of a signal at the receiver. Thus, we exploit the rich scattering nature of the channel,
…show more content…
This diversity gain provides reliable and energy efficient transmission.
Virtual MIMO
When multiple single user antennas share their antennas to create virtual antenna array, then virtual MIMO is developed. This is possible because of Distributed Space Time Block Codes (DSTBCs). The following figure shows the system model of the virtual MIMO.
A. Simple Relay Selection Algorithm
In the simple relay algorithm, a set of nodes is assigned random positions in the relay selection after calculating the Euclidean distance. Only the source and the destination is fixed. Following is the simple relay selection algorithm:
1. For each node calculate the distance between the node N and the S of origin i.e. DNS and the distance between node N and the destination D i.e. DND.
2 VAA-1 and VAA-2 are formed by sorting the nodes in ascending order based on DNS and DND.
3 The first two nodes from VAA-1 and VAA-2 forms VMIMO-1 and VMIMO-2.
The major disadvantage of the algorithm is that the performance of the system degrades if the distance is more.

B. Adaptive Relay Selection
…show more content…
6Else » 3-hop system is continued.

3.11 Genetic MIMO
In the genetic method, the chromosomes are the set of solutions for the optimization problems. Over multiple iterations, these chromosomes crossover, mutate and evolve to get the optimal solutions. If the chromosomes pass their solution as aparameter to the next order, then these chromosomes are considered as fit. The genetic algorithm is defined below:
The initialization stage: A set of N chromosomes is randomly set. Each chromosome consists of a vector K. This vector K indicates the schedule of the user. The user is scheduled if the value of K is 1 whereas the user is not scheduled if the value of K is 0. The chromosomes are constrained to be between 1 and k0, i.e. if there are multiple k0 available, then they all can be scheduled at one time. There is also a second part of the chromosome known as the tail. The tail of the chromosome indicates a user precoding order. But the tail is left off since for the block diagonalization we do not need the encoding order. So only the genetic algorithm is used to schedule users in the context of zero-forcing beam
    Get Access