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
In the location-based routing, sensor nodes are distributed randomly in an interesting area. They are positioned mostly by utilizing of Global position system. The distance among the sensor nodes is evaluated by the signal strength obtained from those nodes and coordinates are computed by interchanging information among neighbouring nodes. Location-based routing networks are;
Having obtained the edge information, the distances from nodes $n_2$ to $n_1$, $n_5$, and $n_6$ are computed.
Proposed algorithm consider three types of nodes every type have different initial energy level. normal nodes have E_0 energy. m advanced nodes have a times energy more than normal nodes with E_0 (1+a) energy level. m_0 super nodes have b times energy more than normal nodes with E_0 (1+b) energy level, where a and b are energy factors. As N is the number of total nodes in network, then for number of normal nodes, advanced nodes and super nodes N(1-m) , Nm〖(1-m〗_0) and Nmm_0 in the network, respectively.
In this example, here node A wants to send data packets to node D and starts to find the shortest path for its destination, so if node D is a malicious node then it will show that it has active route to the specified destination. It will then send the response In the example, data packets transfer in a hierarchic data center network. The link capacity is 1000 kb/s. The number on each is the traffic load. The distribution of traffic is based on equal cost multi-path (ECMP). In figure 8, we can see that the 3). Congestions
This protocol use Dijkstra algorithm. It maintains a complex data base, also called as link state database, which contains full information about the remote routers and the exact network topology. The goal from this protocol is to provide similar information about network connection to each router, so each router can calculate the best route to each network this is happen when each router generates information about itself and pass these information to other routers in the network so each router make a copy of this information without changing it.
In order to avoid this problem, a technique called probability distribution algorithm is introduced. In probability distribution algorithm, the random traffic between the primary network users are analyzed. The nearby nodes behaviors are learnt by the secondary node. The probability of the traffic in the neighboring nodes are studied by the node that tries to transmit data. When the traffic is free then the secondary node establishes the connection. If there is traffic then the secondary node searches for other nodes. Thus the data transmission occurs in this CR
It traces the distance based group of users with same distance from the source .Now we use the probability function to form the probability based group.
Step 1: Construct a network diagram for the project. (NOTE: EF for activity H should be 19)
With the increasing dependency on wireless networks, the need for proper reliability analysis for Mobile ad hoc networks (Manets) is also increasing. Failure of Manets in areas like warfare, nuclear reactors, medical equipment and airplanes can lead to catastrophe. Unlike traditional networks, measuring the reliability of Manets is a tedious task as it involves dynamically changing topology. The existing methods for calculating reliability use two terminal analysis as the basis for calculation. It uses the same method used for traditional computer networks to calculate reliability. However, the method is not very efficient when it comes to the wireless networks as they are far different from traditional networks. It is also a time consuming task to identify all the nodes and links in a wireless network as nodes move freely in the network. In This paper, We are going to discuss about NLN(Node-Link-Node) technique which reduces the complexity of analyzing the reliability in Manets.
Likewise, for each subnet, you cannot use the first and last address. For example, subnet 192.50.1.32
In the present wireless systems, the demand of proliferation of bandwidth for high data rate and reliable communication requirement is increasing. Multiple-input multiple-output (MIMO) systems are investigated to achieve these requirements. Also, diversity techniques are recommended to increase the reliability of system. Orthogonal space time block codes (OSTBCs) are employed in MIMO systems to achieve the full transmit diversity while allowing a simple maximum likelihood decoding algorithm. This algorithm is based on linear processing of received signals [1-2]. MIMO system uses
During the last decade, MIMO techniques in wireless industry have gained a huge interest in the study. MIMO is treated as an extension of conventional smart antenna systems (SAS). In SAS, techniques of beamforming are deployed and the optimal antenna weighting vector that determines antenna radiation pattern is computed based on the optimal criterion such as maximum signal-to-interference plus noise ratio (SINR), minimum mean square error (MMSE) [8]. The ability to exploit and use the multipath propagation can be considered as one of the major advantages of MIMO systems. In contrast to transmit beamforming schemes, channel state information (CSI) is generally not required at the transmitter of MIMO systems. MIMO techniques for transmitting systems can be majorly divided into two categories: spatial multiplexing and space-time coding (spatial diversity techniques). In spatial multiplexing technique, it increases the data rate (throughput) over a MIMO dedicated
As a result of less power loss toward unwanted directions, the multipath and interference effects are reduced. Using wideband circularly
Selection combining is used fir very high frequency and ultra high frequency as maximum ratio combing and equal gain combing is not suitable to work under these high frequencies because tracking performance is not easy in a frequency changing or multipath fading. A simple implementation procedure is used in selection combining and is more beneficial then maximum ratio combining and equal gain combining. In this technique the highest signal level branch is selected. In short selection combining is used to look after all the diversity branches and
Cooperative diversity for a simple three-terminal relay channel was first introduced in [12]. Later, in [1], several improvements were made in capacity bounds and cooperative schemes, such as decode-and-forward, were introduced. Modifications to amplify-and-forward scheme were proposed in [13-16]. Based on these, more relaying schemes were introduced in [17-24]. The performance and other characteristics of the aforementioned schemes in several environments were studied in [17-27]. Extensions to a multi-terminal, multihop network were made in [13-17], where a clustered model and ad hoc network architecture were studied and useful results for transmit and receive diversity gains and relaying strategies were obtained.