Current Systems Of Gsm And Is41 Computer Science Essay

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The current systems of GSM and IS41 both use a centralized approach to location management.

The future wireless communication systems enable the mobile terminal to roam between different wireless systems, network operators and geographical regions. These networks could be heterogeneous networks as 3G cellular network/WiFi network or homogeneous. The network must be able to track the mobile terminal to provide the service anytime and anywhere. A location management service is needed to track the mobile terminal within his network and other networks. We consider the location management for indoor wireless network due to the multipath phenomena. The mobile terminal

To guarantee delivery of the service

Frequent signaling for location update and paging may result in degradation of QoS of other services. Thus efficient location management strategy is needed to balance the location update and paging operations and minimize the overall signaling cost.


For an in-building pico-cellular system would be quite different from those for a conventional micro-cellular system. For a micro-cellular system, it is customary to assume that the user mobility characteristics and user distribution are random and uniform. For a pico-cellular system, however, such an assumption is invalid [Takafumi]. This is because the location registration area size for a pico-cellular system is much smaller than that for a micro-cellular system in proportion to their cell sizes.

5.2.8 Cell-based Localization

Cell-based localization means that we localize a mobile based on the room resolution instead of absolute geometry coordinates. In this way, the only requirement is to correctly position a mobile within a right room without actual consideration of the detail location.

In this experiment, we treat each room as a cell, and the long and the short corridors (between room #108 and #111) also denote two cells.

User mobility model:

5.2.9 Humidity Effect

IEEE 802.11b standard uses radio frequency in the 2.4GHz band. Signal of this frequency

is subjected the influence of the environmental humidity. We conducted an experiment in such an environment with humidity of over 95%, and we compared the signal strength measurement with that of normal dry environment (humidity around 30%). The comparison for sniffer configuration (d) in Figure 5.8 is given in Table 5.11. Compared with the signal strength measurement variance in the first experiment (Please, see Table 5.1), the humidity does greatly affect the signal strength measurement. This again indicates that dynamic signal strength map SS-MAP construction is necessary for the real-time indoor localization.

Signal Strength vs. Distance [Location estimation in wireless networks, p 116]

In order to estimate the relation between the distance and the received signal strength indicator (RSSI) along a straight line inside the building, we carried out two experiments: the first one measured the RSSI in the corridor, where there is no walls between the transmitter and the receiver (line-of-sight); and the second one measured the RSSI within rooms from 101 to 112.

a) Map Generation: Establish signal strength map where location is either measured or theoretically estimated.

b) Location Search: User measure signal strength of a mobile and search for the signal strength map for the "closest" location.

We need to interpolate in order to obtain a distribution of the signal properties at locations from which no calibration data is available.


2.5 RANGE ESTIMATION BASED ON RECEIVED SIGNAL STRENGTH [position location techniques and applications pp. 56].


Advantages and Problems in RSS Location [Book, Wireless positioning technologies and application, pp. 139]

Received Signal Strength Indicator (RSSI), this method measures the strength of the received signal in order to deduce the possible range the signal has propagated from the sender to the receiver. It is applicable if the transmission power is constant or known in advance. RSSI is a low accuracy localization method. However, it is simple and doesn't require extra equipment. Consequently, it is widely supported by most current wireless devices.

RSSI does not require extra equipment and is widely supported by current transceivers.

We use received signal strength (RSS) to estimate the coverage area. However, RSS varies substantially owing to fading, shadowing and multipath effects. Attenuation constant varies according to indoor environment.

To reduce the effect of noise and interference in profiling, we measure RSS values multiple times in different directions and use the average value as the reference.

RSS-based systems require the simplest hardware (only a power detector), which is readily available in several applications such as WiFi, Zigbee, and Bluetooth chipsets. These schemes do not require synchronization [Position location techniques... pp. 9].

Location systems based on RSS are highly dependent on the propagation model used to infer distance. Further, it can be shown that the estimation performance of RSS-based distance inference decreases as the distance between a source and the receiver increases. RSS systems perform very well in short-range scenarios where TOA systems are limited due to finite time resolution.

RSS systems are very sensitive to shadowing and to non-LOS scenarios since,

in these cases, signal power decreases considerably causing large estimation

errors. These systems are also highly affected by local variations on the average

received power (i.e.,to small-scale fading). Note however that these systems are

more robust to multipath because they do not rely on timing information [12].

A series of measured instantaneous RSSI values are used to derive the mean ? and standard deviation ? of the RSSI. The mean and standard deviation of the RSSI during the kth measurement report are given by equation (8.10):

where RSSI[k] is the kth measured values of RSSI, and is an averaging parameter whose value is implementation specific and can in principle be adapted, depending on the coherence time of the channel.7 In equation (8.10), the instantaneous value, mean, and standard deviation of the RSSI are all expressed in the linear scale. The mean and the standard deviation of the RSSI are then converted to the dB scale before being reported to the BS. [Fundamentals of WiMax, pp. 303]

Mobility management in WiMAX [fundamentals of WiMAX, pp. 327]


[Position location techniques... pp. 57].

RSS in 802.11

All IEEE 802.11 WLAN cards measure the RSSI continuously. The Physical Medium Dependent (PMD) sublayer returns a continual RSSI to the Physical Layer Convergence Procedure (PLCP) in the PMD service primitive PMD_RSSI.indicate. In the 802.11 Frequency Hopping Spread Spectrum system this is a four bit field with a range of values from 0 (weakest) to 15 (strongest) signal strength (Geier 2001, p. 133). Other 802.11 physical layer specifications (including 802.11 DSSS, 802.11a and 802.11b), use an eight bit field which allows 256 levels of signal strength (pp. 140, 149, 154). Both 802.11 Direct Sequence Spread Spectrum and 802.11b also optionally report signal quality in an eight bit primitive named PMD_SQ.indicate.

The RSSI is used by the PLCP for its clear channel assessment functions but, given suitable drivers, can be read by applications. Many adaptors are supplied with a configuration utility that includes the capability to display RSSI for the currently active channel or in some cases for all received channels. The RSSI indicator is implemented to provide necessary information for the limited internal use of the WLAN system and there is no guarantee that it will provide precise measurement to external applications.[ location awareness in wireless networks]




Frequency (f) = 2400 MHz

Transmitted Power (Pt) = 13 dBm

Antenna Gain (Gt) = 2 dBi

Receiver Gain (Gr) = 7 dBi

Cables losses = 5 dB

Minimum Distance (d) = 1 m

Maximum Distance (d) = 10m


RSSI (1m) = -23.04600 dBm

Pathloss (1m) = 40.04600 dB

RSSI (10m) = -43.04600 dBm

Pathloss (10m) = 60.04600 dB


Inssider from NetStumbler was used to measure the propagated signal

An arbitrary transmitted power level of +22 dBm. +20dBm (100 mw).

The RSSI of packets on the IEEE 802.11b wireless network technique has the great advantage that it may be implemented using off the shelf hardware that is generally already deployed to support the data network.

The direct line of sight is not a dominant component in indoor wireless environments; the other metrics such as TOA, AOA and TDOA are not suitable for indoor location update and paging due to the multipath phenomena.

As the distance of the transmission path increases, the amount of change in signal strength for a given change of distance decreases [location awareness in wireless networks].

Figure Indoor free path loss

This decrease in sensitivity is better illustrated in Figure 3-2 which shows the sensitivity of signal strength as an indicator of distance.

Sensitivity of path loss

RSS disadvantage

The dynamic nature of the RF environment.

The imperfect precision of the measurement devices.

The effect of receiver orientation to RSS.

The effect of man movement to the received RSS. [Location awareness in wireless networks]

The variance of RSS-based techniques increase with distance


[Position location techniques and applications pp. 56].


[Position location techniques and applications pp. 57].


The power safety margin is set to a few dBm's to account for the effects of interference.


The local average power of a mobile radio signal is obtained by smoothing out (averaging) the fast fading part, and retaining the slow fading part. [estimate of local average power]

Small changes in signal strength, changes in environmental conditions may cause RSSI data to "drift" over time. This could be so whether the locations are derived by the engineering approach by triangulation, or by using a scene analysis approach using Bayesian or some other machine learning technique.

Ranging estimation errors __,i (i_1, . . . , M ) that arise from TOA, TDOA,

or RSS measurement inaccuracies may be modeled as independent zero mean

random variables with variance _2

__ .[position location tech… pp. 98]

Self correction approach to make such systems more robust in the face of variations in the signal propagation environment.

This could be done by preventing extreme RSS values from rising beyond the neighboring known RSS signals. By:

Optimize the number of averaged RSS.

Take the average of some measured RSS (mean received RSS)

Matches the RSS to a tile set using thresholds (calculated as expected RSS).

MH scans all AP's in its range and determines their signal levels. To overcome the noise effect it is essential to do this process multiple times and use the average value.

RSSI calculate distances using transmitted and received signal strengths between reference and blind nodes [Erin-Ee-Lin Lau, Boon-Giin Lee, Seung-Chul Lee, and Wan-Young Chung ,Enhanced Rssi-Based High Accuracy Real-Time User Location Tracking System For Indoor And Outdoor Environments].

RSSI =-(10nlog10d+A) (1)

The value A is obtained in a no-obstacle one-meter distance signal strength measurements from the reference nodes while d is the distance measured between references and the blind node.

n: signal propagation constant

d: distance from sender

A:Measured received signal strength at one meter LOS distance from the transmitter.

From our analysis, it shows that value of A equal to 41 and value of n as 3.875 is the best substitution for distance estimation calculation for all the cells (rooms).

Propagation constant is calculated by reversing the linear RSSI equation as shown in (1)

Main challenge in RSSI-based location tracking is its high sensitivity to the environmental changes

The mobile target does not move and yet, signal strength varies over time

Smoothing algorithm is proposed to minimize the dynamic fluctuation of radio signal received

Calculate the approximated theoretical distance from the transmitter

One approach is that of environmental profiling where multiple sets of data are recorded throughout the day and the base stations probe the environment periodically and pick a dataset on which to base the estimate (Bahl and Padmanabhan 2000a).

k is a constant that represents the minimum signal strength of WLAN that can be detected.


Bayesian network, the data given the Bayesian model.

A database is formed from the use of analytic equations or derived from ray tracing software.



When a mobile terminal is called or a service is initiated, paging is operated over the location registration area where the mobile terminal has been registered.

The network pages the called user over the location registration area where he has been registered.

Cost for mobility management [location registration and paging for in-building]

We define two criteria to evaluate the cost for mobility management of each method; one is the total uplink cost Cu, and the other is the total downlink cost C d.

Total uplink cost

The total uplink cost C, is defined as follows:

X : type of area.

Nx : total number of type X areas in the whole building. (The employed values are shown

in table 2.)

CUx : the maximum uplink cost per minute for a type X area, which means the peak value of the total number of registration and erase-memory messages in a type X area.

Total downlink cost

The total downlink cost cd is defined as follows:

C d X : the maximum downlink cost per minute for a type X area, which means the peak value of the number of paging messages in a type X area.

Load location management

Source [Optimizing location management cost through registration area overlapping: models, problems and methods]

L = fscs + fucu

fs- Number of paging messages (search rate)

cs- Search (paging) execution cost

fu- Number of update messages (update rate)

cu- Location update execution cost

Update/search tradeoff optimizations

Characteristic: Optimal point depends on call to mobility ratio

Call to mobility ratio (CMR)

CMR = fs/fu, fs-search, fu-update

RA overlapping as LM improvement

Can contain more of a mobile terminal's mobility. a

- Less registrations. a, b, c.

- eliminate registrations of "border movements" b

Load balancing. a

Minimize call loss. d

a. Okasaka and Onoe, Proceedings IEEE VTC'91

b. Markoulidakis et al., ACM/Baltzer Wireless Networks 1(1):1729,


d. Bejerano and Cidon, Proceedings ACM MobiCom'98

c. Wang and Akyildiz, Proceedings ACM MobiCom'00


Call arrival rate vs. update cost

Reduce the uplink cost much below that for the conventional method. This owes to the structure of the location registration area which removes the burst location registration traffic.


Tracking the mobile terminal using the existing infrastructure networks, no hardware is needed. (Location update systems based on the strength signal have the distinct advantage that they can be implemented using the existing hardware infrastructure of a wireless network)

The algorithm uses the RSS metric associated with ….. channel of the cellular and wireless networks.

The algorithm aims to reduce the signaling traffic generated by location management on the wireline links of the backbone network.

There is no exact information about the position of the mobile terminal. Position determination is limited to the coverage boundries

For security, the algorithm indicates that a device is within the coverage area of one or several wireless access points.

The algorithm is mobile-based, thus reduces the bottle neck problem in the network by distributing the location update decision among all the individual mobile terminals. (The location update process is distributed along the mobile terminals; the mobile terminal has to monitor the received RSS signals from the surrounding access points or base stations. The decision to perform update is related to mobile terminal to reduce the centralization of the process at the base station).

Client-centric approach has the advantages of being more scalable, not being dependent on the network behavior, and maintaining privacy of MH.

The algorithm does no need any localization algorithm to find the actual position of the mobile terminal

We use the 802.11 Wireless Local Area Network (WLAN) technology in our study, because of its commodity status. Our results however are applicable to any radio technology where there are considerable environmental effects on the signal propagation [The limits of localization using RSS].

However almost every wireless device has the capability of performing RSS measurements and RSS-based localization techniques meet the exact demand from industry on localization solutions with minimal hardware investment. It is this feature of RSS-based localization techniques that drives the tremendous interest in their research and developments. [localization algorithms]. It is this feature of RSS-based localization techniques that drives the tremendous interest in their research and developments.

Walking speed averaged 0.73 meters per second

Paging Algorithm

The algorithm allows the analytical computational of performance characteristics. Performance measures consider location update cost, location query cost, paging cost, combined cost of location update and query, combined cost of location update and paging.

The location update area is considered to be the coverage area around base stations in the case of cellular networks and access point in case of the WLAN networks. The mobile terminal monitoring the RSS from the surrounding transmitters and compare it the threshold value under which the mobile terminal has to perform location update procedure.

The proposed algorithm uses the received signal strength to perform location update whenever the received signal reached a defined threshold value instead of periodically performing location update whenever moves from a certain area ( room, office, corridor, etc) in order to conserve power and operation resource of MS.

Estimating is more convenient than measuring a signal strength MAP especially for a large building.

To build the signal strength map SS-MAP, most indoor localization systems take the manual measurement method, which is carried out by manually measurements of signal strength at short intervals within the building (empirical method). The manual measurement is time-consuming.

Indoor propagation models

We used Site-specific model where it relates to path loss with site-specific parameters (geometrics, materials, and thickness). Compared with the other models, the site-speci_c model does not depend on special assumption, so it works on most general building environment. However, it is complex and requires detailed site-specific parameters. Detailed material characteristics and geometry properties are required if site-specific model is to be used.

Coverage Analysis

[Location estimation in wireless networks]


Monitor the available RSSI

Sort the RSSI and the corresponding channels in descending order

Connect to the first RSSI channel

Perform location update process

Display a warning message that the connection will be lost

Compare the first RSSI with threshold value

First RSSI > Threshold value



Set the threshold value


Global location update and paging algorithm

Set the threshold value



First RSSI > Threshold value

Compare the first RSSI with threshold value

Check the availability of another neighbor networks

Perform location update process

Connect to the first RSSI channel

Sort the RSSI and the corresponding channels in descending order

Monitor the available RSSI



The proposed algorithm reduces the centralizing location information in the HLR/VLR used in the current systems to avoid the signal traffic hotspots found in the HLR/VLR scheme. How. The location update is made only locally and compared with the update costs in HLR/VLR system.

6.3 Algorithm Comparison

6.3.1 Reported performance

In order to evaluate the localization performance of the proposed algorithms, in this section, we first illustrate the reported localization performance from other research groups.


To improve the location update algorithm performance, the following three problems must be solved.

Accurate signal strength readings from all BSs.

Optimize the coverage and minimize the overlapping

Furniture modeling: As discussed in Section ??, the simple modeling method for the furniture can improve the localization performance, however, to model the furniture as additional walls does not fully capture the attenuation properties of the furniture.

Large furniture, like bookshelf, affects the indoor radio propagation; thus, a simple method is to model them as additional walls. This will improves the localization performance if no other mechanisms is available;

We also developed 3 novel algorithms that are area-based. That is, the returned localization answer is a possible area (or volume) that might contain the sensor radio rather than a single point. The key property of such algorithms is that they can trade accuracy for precision, where accuracy is the likelihood the object is within the area and precision is the size of the returned area.

long-term fading

The sufficient number of samples for estimating this local average power values is 36.

[55] Homayoun Hashemi. The indoor radio propagation channel. Proceedings of the IEEE, 81(7), July 1993.

[56] D. Molkdar. Review on radio propagation into and within buildings. Microwaves, Antennas and Propagation, 138:61.73, Feb 1991.

[12] F. Bouchereau, D. Brady, Bounds on range-resolution degradation using RSSI measurements, in: Proceedings IEEE International Conference on Communications, France, June (2004) 3246---3250.