Essay On Wifa

Abstract— The demand for wireless access is permanently growing and the transmission bandwidth is limited. Higher data rates and signal quality is always desired among the users; therefore a more efficient use of the frequency resource is needed to fill the user capacity needs. Space Division Multiple Access (SDMA) is used to increase the capacity given by regular multiple access techniques (FDMA, TDMA and CDMA) using the user position information to provide access to the best communication channel based on their spatial location. This paper presents Smart Antenna techniques applied to SDMA systems as an efficient frequency usage technique.

In the last few years, number of mobile users/wireless users has been increased explosively all over the world. The demands for wireless communication network has been increased day by day and it cannot be satisfied with wired networks because it has been seen that communication channels are more contaminated than wired networks. The main characteristics of the wireless communication are the multipath reception. We can receive signal not only through Line of Sight but also reception can be made through a large number of reflected radio waves that arrive at the receiver at different times. The difference in arrival time is caused due to trees, vehicles, buildings, rocks etc.

The combinatorial optimization problems such as Travelling Salesman Problem, Minimum Spanning Tree Problem, Vehicle Routing Problem etc. aims at finding an optimal object from a finite set of objects. Brute force methods which include exhaustive search are not feasible for such problems. In recent years many new and interesting methods are applied for the solution of such problems. These methods such as genetic algorithms (GA), Simulated Annealing, Tabu Search, and Neural Networks are inspired from physical and biological processes.

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

In some existing work, the transmission range of an omnidi-rectional antenna was simply assumed to be a disk-shaped area around the transceiver. Based on this assumption, given a point in the plane, the neighbor set of this point consists of all sensors within the disk-shaped area around this point. However, due to the uncertainties of a wireless environment, such as signal fading, reflection from walls and obstacles, and interference, it is hard to estimate the boundary of the transmission range without real measurement [42], [43]. Therefore, in practice, it is almost impossible to obtain the neighbor set of an unknown point, unless the M-collector has moved to this point and tested wireless links between it and its one-hop neighbors, or

As the real world problems are getting complex and intricate day by day, the need of fast, simple (with few control parameters) and effective optimization algorithms is increasing among the researchers from various fields. New algorithms are required to cope up with the existing problems. SMO is a new meta-heuristic nature-inspired algorithm and is a hit and trial based mutual iterative strategy for global optimization over discrete and continuous spaces. It has performed superior to other evolutionary and swarm intelligence based algorithms which is clear from the fact that it gave better results when tested on various benchmark problems. As few control parameters are involved in SMO, so it becomes easy to implement SMO in various types of optimization problems. It is exceptionally evident that some inborn disadvantages are likewise there in each algorithm. To overcome these, various modifications have been made in the original SMO. These modifications has enhanced the basic algorithm and also improved its efficiency due to which, it can be applied to solve various other real world complex optimization problems.

Abstract—Although fingerprint based localization is promising for indoor applications, its accuracy remains a huge challenge. Most existing approaches depends on the Radio Signal Strength (RSS) to generate fingerprints. Merely using RSS is unable to accurately localize since such an one-dimensional fingerprint is serious influenced by the interference and multipath effect in the indoor environment. In this paper, we propose a new localization approach based on multidimensional WiFi fingerprint. Instead of using RSS only, we employ RSS, transmitted power, and channel information to construct fingerprints. The extension of fingerprints enables fine-grained localization and tracking services. We also deign a cosine similarity based matching algorithm and enhanced particle filter mechanism to realize the localization and tracking. Extensive experiment and implementation results show that using the new fingerprint can achieve an accuracy within 2m in 90% testing points, with good adaptability to complex indoor environments.

The signal data obtained in the measurement phase is then used to approximate the position of the target node. Since, due to many factors range data often gets attenuated by noisy signal measurements, optimization methods are to be employed to filter the noise and arrive at a more definite position estimate.

Wi-Fi Local Area Network using RSSI (Received Signal Strength Indication) value for determining the position, accuracy, etc. RSSI is the relative received signal strength in a wireless environment, in arbitrary units. It is implemented and widely-used in 802.11 standards [24]. RSSI is an indication of the power level being received by the antenna. Therefore, the higher the RSSI number (or less negative in some devices), the stronger the signal and in Received Signal Strength Method reference receivers (GBSs) measure the carrier phase. The differential GPS can improve location accuracy from about 20m to within 1m.[15] There is ambiguity resulting from periodic property of the signal phase. There are more errors that can be caused due to

The authors apply NA, which has two important tuning parameters. Those parameters help to achieve faster convergence. It is important to note that those parameters depend on the problem, but the authors never mention how those parameters were tuned and convergence was decided.

The below flow diagram shows how that Lightweight location verification algorithm work, in the first level, the data owner collect the basic information of the network. The content of the information about the packet and the time of sending and receiving the information is stored in the basic information layer. The third layer is packet formatting which have the details of the data. In wireless network, the data are sent through packets so the packets content the information in a header which contents the information of the source and the destination information and the parole represent the data here. Packet generation the fourth layer gathers all the collected information and also may content many useful data for the host source, after that there are two types of verification, one is on-spot, other is in-region. In on-spot verification the sensor hold the data and checks the packet is on place whether the data owner placed it. However, if the spot is changed then the measurement detail will send to the host node.

In some literature, the multiplicative noise has been used. Spinello and Stilwell [7] have presented a recursive algorithm using the Maximum Likelihood Estimation method coupled with Gauss-Newton algorithm in which a statedependent

A decade ago, Communication systems haven’t been able to properly exploit the spatial component of mobile radio channel. Communication systems has been facing two main problems. One is using very small amount of energy and second is interference problem i.e., mixing of main signal with signal from other communication links. Here, the solution is eliminating interference and collecting total radiated energy. It may sound simple, but yes it is the solution. The system which can able to do these two works is smart antenna system.

In order to eliminate or minimize problems caused by multipath wave propagation, a new technology has been introduced and it is Smart Antenna Technology. There are three forms of smart antenna, known as SIMO (single input, multiple output), MISO (multiple input, single output), and MIMO (multiple input, multiple output).

In proposed work, different coefficients values have been generated by using the FDA tool of MATLAB software based on Bartlett-Hanning and Blackman-Harris window in order 10. The filter coefficients have been estimated from ranges 0.025 to 0.975 cut off frequencies. At each cut off frequency, it gives a total 38 samples in order 10.