congestions and performing automatic load balancing with the help of novel .... Antenna Agents according to the geographical load distribution. A Simulation ...
Applying Agent Technology into Radio Resource Management of WLAN System Yapeng Wang and Laurie Cuthbert Department of Electronic Engineering Queen Mary, University of London Mile End Road, London E1 4NS, UK Tel: +44 20 7882 7408 Fax: +44 20 7882 7997 {yapeng.wang/laurie.cuthbert}@elec.qmul.ac.uk ABSTRACT IEEE 802.11 based Wireless Local Area Network (WLAN) system has been rapidly developed and deployed. With limitations of the standard and rapidly growing wireless application demand, the air interface acts as bottleneck even in high-speed WLAN systems such as 802.11a and 802.11g. To improve overall performance of 802.11 WLAN under large deployment and heavy application demand environment, a novel agent-based Radio Resource Management is proposed with the help of proposed low-cost sectored semi-smart antennas. With dynamically changing radio cover patterns of Access Point (AP) and agent negotiation, simulation results show great dramatic system performance improvement over traditional configurations. Issues of implementation are also discussed in this paper.
Keywords: Agent, WLAN, radio resource management.
1. INTRODUCTION Current IEEE 802.11 based WLAN systems have been widely deployed in many places. They offer mobility and are especially appropriate for temporary configuration like conference centres, or public areas such as an airport, coffee bar (theses places are also called “hot spots”). The original 802.11 standard specifies a common Medium Access Control (MAC) layer and three Physical (PHY) layers [1]. Two of these PHYs facilitate communications in the 2.4GHz Industrial Scientific and Medical (ISM) bands using Direct Sequence (DS) and Frequency Hopped (FH) Spread Spectrum techniques. The third PHY facilitates communication over infra-red links. Data rates of up to 2 Mbits/s are facilitated by each of the PHYs. The 802.11 standard has subsequently been expanded considerably with several PHY extensions, and the data rate can support up to 54 Mbits/s. In a multi-access WLAN system, the MAC layer is the main element for determining the efficiency in sharing the communication bandwidth of the wireless channel. The MAC has two alternative multi-access mechanisms: basic Distribute Coordination Function (DCF) and additional Point Coordination Function (PCF). The DCF is mandatory and most commonly used for 802.11 devices. It is based on a Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which imitates the mechanism used in Ethernet system and supports best effort delivery of packets and allows multiple users fairly sharing a common communication channel. Every 802.11 station wants to transmit a frame will first sense the air channel, if it is busy, the
station must wait for a random “back off” time and try again. As all stations in an 802.11 WLAN implement the same carrier sense and back off mechanism, all stations fairly share the communication channel in statistics for a long period of time. Public deployment of 802.11 WLAN systems in large areas, multiple APs are used and geographically distributed in an overlapping manner (similar to cellular systems). Each AP normally coordinates one air channel and covers a certain area offering all Stations (STAs, STA is the formal abbreviation for Station in 802.11 standard) accessing the AP. With more STAs gathering around a same AP and heavier application demand (such as downloading), these WLAN users will experience lower data throughput and longer delay due to congestions occur in the air channel. One way of expanding the WLAN system capacity is to mount more APs in the heavily congested area, but this method is an expensive approach because of limited available air channels, accurate site surveys in order to minimise interference between APs, and more capital investment (purchasing more APs). Moreover this approach cannot cope with a dynamic environment when the geographically distributed congestion varies with time (this usually happens in public area such as in an airport where WLAN users trend to gather into a certain area to wait for a flight and thus forming a temporary hot spot). Based on experience [4] of load balancing for cellular mobile networks, we propose a dynamic, distributed intelligent agent controlled WLAN system to cope with congestions and performing automatic load balancing with the help of novel semi-smart sectored antennas for APs. Section 2 discusses the data rates vs. range for different 802.11 PHY extensions and is the base for our simulation; section 3 briefly introduces the proposed semi-smart antennas; section 4 illustrates the agent architecture for WLAN and negotiation procedures; section 5 discusses the in-door path loss model used in our simulation; section 6 discusses the software simulation and various results analyses, real world implementation of agents are also discussed.
2. CONGREGATED THROUGHPUT PERFORMANCE FOR 802.11 WLAN Whilst the MAC specification in 802.11 standard family has remained largely unchanged (several new PHY layer specifications have been added [2]. The 802.11b PHY facilitates data rates of up to 11 Mbits/s, employing Complementary Code Keying (CCK) and Direct Sequence Spread Spectrum (DSSS), also in the 2.4 GHz ISM band. l
l The 802.11a PHY facilitates link adaptive data rates of up to 54 Mbits/s, employing coded orthogonal frequency division multiplexing modulation (COFDM) in the 5GHz Unlicensed National Information Infrastructure (UN-II) band. l The recent 802.11g PHY specification is a high rate extension of 802.11b. The new PHY was based upon the link adaptive COFDM modulation scheme (maximum data rate of 54 Mbits/s) of 802.11a with mandatory backward compatibility with 802.11b.
As distance from the AP increases, 802.11 based products provide reduced data rates to maintain connectivity. The 802.11g standard has the same propagation characteristic as 802.11b, because it transmits in the identical 2.4 GHz band. Because the 5 GHz radio signals do not propagate as well as the 2.4 GHz radio signals, the 802.11a product range is limited compared to the 802.11b or 802.11g product range Figure 1 .
usage (ideal free space radio propagation model) and static user traffic demand. For WLAN systems, the “cell” concept can still be used to represent the AP layout and multiple APs can cover a wider area seamlessly to support cross-AP mobility. The most common antennas used in WLAN AP are omni-directional diverse antennas, which applies a circular shape coverage area. In order to achieve more controllable radio coverage patterns, we propose 4-sector semi-smart array antenna for APs. Each sector has multiple antenna elements and individual power control in order to achieve directional and controllable pattern, thus the combination of four sector antennas will give an AP the ability to produce flexible radio coverage patterns (Figure 2, 3). The four sector antenna patterns are combined to give the overall cell coverage pattern. The design of a four-sector antenna array is a compromise between performance and cost. More sectors will increase the flexibility of coverage shaping but has disadvantages: •
More RF power controllers are needed.
•
Each sector needs a narrower antenna pattern, so that each sector antenna needs to be physically bigger.
Figure 2: The appearance and working diagram of the foursector ceiling mounted antenna array. Figure 1: Expected 802.11a, 802.11b and 802.11g data rates at varying distance from access point Throughput is not the same as data rate for networking systems, because of overhead, environment and network compositions. The maximum throughput of 802.11b is only 47% of 11 Mbits/s. For 802.11a and 802.11b a net throughput of 55% of 54 Mbits/s has been observed [3]. a) 3. THE PROPOSED SEMI-SMART SECTORED ANTENNA FOR ACCESS POINT In a cellular system, radio resources are reused after a certain distance. The whole area is divided up into a number of small cells, with one base station giving radio coverage for each cell. In the most common usage, the antennas’ power control is fixed so that each base station’s radio coverage is static. Previous study [4] has developed a dynamic distributed agent-controlled system to change the cells’ coverage patterns according to the geographical traffic load1. The formation of cells is based upon call traffic needs. Capacity in a heavily loaded cell can be increased by contracting the antenna pattern around the source of peak traffic and expanding adjacent antenna pattern to fill in the coverage loss. So far, the study only concentrate on out-door
1
This is ignoring multi-sectored cells.
b) Figure 3: Coverage patterns of the antenna sectors with different power control
4. AGENT-BASED NEGOTIATION TO REDUCE OVERALL SYSTEM LOAD The combination of Intelligent Agent (IA) and Multi-Agent Systems (MAS) has been widely used in cooperative environments like e-commerce, distributed artificial intelligence (AI), new IP routing protocols [5] [6] and resource management of CDMA networks [7]. The advantage of the MAS is that it provides a distributed, robust platform that allows agents to act both individually and cooperatively. Within our WLAN simulation, these agents are AP agents and antenna agents; they all have their own initiative and co-operation ability. The AP agents communicate with each other through the MAS platform and change the radio coverage patterns through the Antenna Agents according to the geographical load distribution. A Simulation Agent initialises the simulation environment, provides the program interface, and collects the resulting data. It also acts as a directory facilitator to allow AP Agents to find their neighbours. The basic negotiation process for the WLAN resource management can be shown as in Figure 4.
order to maintain the whole system stability, all the messages are carrying the minimum data and endless loops are checked to avoid deadlock. Unlike cellular systems, the “handover” is initialised only by STAs. In the 802.11 MAC standards, the station scans for all available radio channels and tries to associate/re-associate to the AP that provides the best signal strength and quality [8]. When sensing a stronger signal, it first disassociates from the currently associated AP and then initiates the association process to the “better” AP and thus achieving “handover”. Most WLAN vendors’ products already support seamless hand-over so that the application layer connection will stay connected during the whole handover process. The AP agent needs to know its own geographic radio coverage pattern as the basis for negotiation. We are using a site-survey2 and machine-learning based technique to find the AP’s radio propagation in an in-door environment. 1. Select sample points distributed for all WLAN coverage; these points can be geographically randomly selected but more sample points and more even distribution will increase the resolution and accuracy of the radio coverage pattern generated. Several metres for intervals can be a good practice. 2. Use the 802.11 client card to record the signal strength at each sample point under maximum and minimum power control of all AP antennas. These raw data will be inputted to the agent system to help to calculate the radio patterns. 3. A generalised radio coverage pattern will be produced. By mapping this to 2-D polar coordinates, we can represent simplified patterns in our agent system. The polar coordinates make the complex radio coverage patterns mathematically easier to handle.
Figure 4: The basic negotiation sequence diagram.
Each AP agent monitors its own traffic load in real time; when the overall traffic load is over a certain threshold (we use 95% of the maximum data rate supported by the AP), a negotiation procedure will be triggered. The overloaded AP agent first creates 4 hypotheses that could drop associated STAs by decreasing some of the antenna sectors’ power (discrete steps). Neighbour APs need to increase their certain sector’s power and enlarge their patterns to cover the coverage loss. The result will cause some of the STAs to handover to the lighter loaded APs thus achieving load balancing. These hypotheses will be evaluated by the AP agent according to the disturbance (more antenna power change will cause more disturbance); in the mean time, a set of Ask For Help (AFH) messages are created and sent by the AP agent to adjacent APs. Each hypothesis could cause one or more AFHs to be sent because it could need more than one neighbouring AP to help. After evaluating the AFHs, these helper APs will send back Reply AFH (RAFH) messages along with a price if they could help. When it has collected enough responses, the heavy-loaded AP chooses the most suitable hypothesis (causing least disturbance to the whole system) and sends Change Your Pattern (CYC) messages to relevant neighbour APs. All affected APs change the radio coverage patterns simultaneously to make sure of continuous coverage. In
Simplified polar coordinates representing the radio coverage (path loss) are shown Figure 5. It shows an AP’s radio coverage pattern measured in our department and finally represented by polar coordinates (shadowed sectors in the dashed concentric circles)
The Location of AP
Figure 5: Polar coordinate representation of radio coverage pattern (only half circle of the radio coverage pattern to show the concept).
2
The survey was performed using the Ekahau positioning engine: available at: http://www.ekahau.com. This is also used to track the location of individual STAs.
In the polar coordinates representation of our radio patterns, the smallest division is called Quantisation Cell (QC); each QC is the smallest representing area that can contain many WLAN user stations. When negotiation is triggered, the hypotheses are actually abandoning a number of QCs by changing the power control of the antenna sectors. The AP knows how much traffic will be shifted L (1) with this hypothesis.
L=
∑ ∑∑
TSTA
(1)
Sectors QCs STAs
TSTA
is the current traffic load of WLAN user station.
5. MEASUREMENT AND PATH LOSS CALCULATION As most WLAN systems are deployed in indoor environments, the free space radio exponential path loss model fails to represent the actual radio propagation characteristic because of severe multi-path effects. Experimental work has been published [9] showing that signal strength can dramatically change for a distance of as little as a single wavelength. Even worse, the radio propagation is site-specific (affected by layout of building structure, building material etc). In addition to our measured model of radio propagation, the calculation of the signal strength between sample-points utilises the in-door log-distance path loss model described in [10] (Equation 2).
PL(dB ) = PL(d 0 ) + 10n log(
d ) + Xσ d0
(2)
Here the value of n depends on the frequency used and the surroundings and building type, and
Xσ
represents a normal
random variable in dB having a standard deviation of σ dB. With a 802.11b WLAN that uses the 2.4 GHz ISM band and a typical in-door building, the path loss propagation model (2) can be written as (3) [11].
PL(dB) = 40 + 35 log D + X σ
(3)
Here D is the distance between the transmitter and receiver in metres.
WLAN card. The overall accuracy can be up to a few metres and is acceptable for our positioning purposes. An alternative method that uses GPS requires extra equipment and has limitations indoors. 6. SOFTWARE SIMULATION AND RESULT Based on the system we have described, an agent-based WLAN radio resource management simulation system was developed. Client stations are distributed across the whole area and a hot spot (congestion area) is generated to test the efficiency of the negotiation process. The simulation has the following assumptions: • Each AP is mounted with proposed 4-sector semi-smart antennas, each sector has individual power control in order to produce flexible coverage pattern. • 16 APs are laid out on a 4*4 hexagonal pattern (similar to cellular system), so one AP can have up to six neighbours. The maximum data rate for each AP is set to 11 Mbit/s. A maximum net throughput of 5.8 Mbits/s for 802.11b and 24.7 Mbits/s for 802.11g is also simulated. • 96 client stations or STAs are normally distributed to produce a hot spot centred at AP 5. • Each STA has static or dynamic data demand. The simulation results for the two settings are evaluated individually. • Radio Propagation Characteristics and Coverage area for each AP comes from setup file; it imitates the actual system where a site survey phase with sampling would produce the data. • The client stations are presumed to always associate to the AP with the strongest signal strength. The simulation uses Java and the Jade [12] Agent platform. With fully distributed MAS support, the whole system can be easily adapted to multiple machines where each machine represents an individual AP agent. The messages used in the system are based on the Foundation for Physical Intelligent Agents (FIPA) Agent Communication Language (ACL) [13]. With addition of Ontology [13] content language support, the negotiation protocols are scaleable and easy to use.
Comparison between using and Not using agent-based dynamic negotiation radio pattern control (static user demand at 1.1 Mbit/s)
To summarise our proposed approach of modelling, the in-door propagation model has two steps:
b)
Selecting a series of sample points and measuring the signal strength using a WLAN client card (it actually records the Radio Signal Strength Indication (RSSI) parameter available in the PHY layer). The rest of the area is calculated from these samples by using the indoor propagation model (3).
Every AP agent uses this mixed method to model its own surrounding area. Through machine learning, the AP agent gradually improves the propagation model and even observes the longer-term effects. A good example is that aggregation of people can affect radio propagation and in a public area (like an airport), the distribution of people flow is time dependent (such as queues at different check-in counters). By observing this effect, the AP agent can adapt its propagation model accordingly. As noted earlier, the location of the WLAN clients can be achieved by using the Ekahau WLAN positioning engine, which uses a patented probability method to estimate the user’s location according to the signal strength received at a normal
1.0 0.9 0.8
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Figure 6: Simulation result of using static user demand.
Comparison between using and Not using agent-based dynamic negotiation radio pattern control (random user data demand 0 - 2 Mbit/s)
Using agent-based radio pattern control can improve the overall WLAN system performance. An improvement of the order of 40% is shown in Figure 10, 11 (for both 802.11b and 802.11g systems).
1.0 0.9
Comparison between 802.11b and 802.11g
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Figure 7: Simulation result of using variable user demand
0.1 0.0 0
20 independent simulations were carried out with different seeds for random number generation for each set of simulation results (Figure 6, 7 and 8). Figure 6 and 7 show that with AP negotiations and dynamic changing radio coverage patterns. It is shown that traffic distribution dramatically increases the overall WLAN system performance. The former was performed under fixed data demand and the latter was simulated under random data demand (each station’s demand was randomly generated between 0 to 2 Mbits/s). The random user demand simulates multiple users having different data rate applications (such as browsing WWW, Email, downloading, Stream Audio and Video). For the static demand simulation, an average of 1.1 Mbits/s data rate (which is adequate for most user applications) was set for all user stations. Both scenarios show that with the load balancing, the data traffic in congested “hot spot” areas can be effectively balanced. Results in Figure 6 and 7 show the limits of results produced from the different random seeds as confidence bars with the curves being the average across all results. However, Figure 7 additionally shows the result from a single set of results – this demonstrates that the improvement for the average and for this single result is substantially the same. Figure 8 presents net throughput performance improvements both for 802.11b and 802.11g. From the results we can see that with negotiation and dynamic changing radio pattern, the 802.11b WLAN can perform almost as well as 802.11g WLAN with normal configuration (with common omni-directional antennas). All Figure 6, 7 and 8 show that as the number of users (and hence the load) increases, the actual achieved data rate (expressed here as load/demand, or actual load/application demand) available to users decreases. However, with dynamic agent-based radio pattern control, the congested APs negotiate and co-operatively change their radio propagation patterns. The performance is very much better than the system without dynamic radio control. The radio coverage patterns of APs around the “hot spot” area after the result of negotiation for the random data scenario can be seen in Figure 9. It shows how the overloaded AP shrinks its own coverage pattern and the other APs enlarge theirs to help.
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Figure 8: Simulation result of and comparison of 802.11b and 802.11g (variable user demand).
Figure 9: Screen shot of part of the simulation GUI
However, that figure shows other features: • The system can handle approximately double the number of users at full capacity, i.e. with the carried load being the same as the demand. • With greater congestion the system cannot carry all the demand, but the improvement continues to increase. • As the congestion grows the benefit will naturally start to tail off, since there is only a finite capacity available and even better distribution of that capacity will not be able to cope with unlimited demand.
7. CONCLUSION
Improvement of (Load/Demand)
50%
load/demand=1 for using negotiation 40%
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8. ACKNOWLEDGEMENT
Figure 10: Improvement obtained with negotiation (802.11b)
Improvement of Load/Demand
50%
We have proposed and discussed an agent-based 802.11 WLAN radio resource management with the help of novel semi-smart sectored antennas. The benefit of such a system is analysed and shows it is suitable for relatively large deployment areas with multiple APs such as in an airport. With carefully designed negotiation protocol of various agent components, the system can dynamically cope with congestions. The traffic load can be balanced at a certain level and users in those congested areas experience better data rate and shorter delays with traditional fixed-antenna systems. Furthermore, applying traffic prediction and agent learning, the system can even change system configuration before the event of congestion. Further studies will be continually carried on to gradually improve our approach.
The authors would like to acknowledge the European Commission for the ADAMANT project under IST program. The work in this paper is part of ADAMANT project (IST2001039117).
Load/Demand=1 for negotiation
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Figure 11: Improvement obtained with negotiation (802.11g)
Our Java based simulation guarantees smooth transfer to a real system. Real traffic load can be monitored through Simple Network Manage Protocol (SNMP) [14] accessing the Manage Information Base (MIB) of the AP. The MIB II [15] defines parameters that can be monitored and controlled for IP layer network management and most network devices (including AP) apply a public interface to it. By monitoring Total Transmitted Counter and Total Received Counter (in bytes) against system time passed, the AP agent can calculate the real time traffic load and thus know the system is congested. Furthermore, by accessing the 802.11 MIB [1], an AP agent can have in-depth knowledge of traffic conditions of its hosted AP. This includes TransmittedFragmentCount and ReceivedFragmentCount (through the MAC layer traffic condition can be estimated), FailedCount, which increments when the number of transmission attempts has been exceeded (it is normal for this counter to rise with increasing load on a particular BSS for evaluation purposes). Other relevant parameters are RetryCount, FCSErrorCount and FrameDuplicateCount.
9. REFERENCES [1] IEEE 802.11 WG, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, Standard, IEEE, Aug 1999. [2] The New Main Stream of WLAN Standard, White Paper, Broadcom, July 2003. [3] A. Kamerman, G. Aben, Throughput Performance of Wireless LANs Operating at 2.4 and 5 GHz, Personal, Indoor and Mobile Radio Communications, 2000. PIMRC 2000. The 11th IEEE International Symposium on , Volume: 1 , 18-21 Sept. 2000 pp.190 -195 [4] L. Du, J. Bigham, L. Cuthbert, Intelligent cellular coverage control according to geographic call traffic distribution, 3G Mobile Communication Technologies, 2002. Third International Conference on (Conf. Publ. No. 489), 2002, pp. 423-427 [5] J. A. Hendler, Intelligent Agents: Where AI Meets Information Technology, Expert, IEEE, Volume:11 Issue 6, pp. 20, Dec 1996 [6] C. E. Perkins, Mobile networking through Mobile IP, Internet Computing, IEEE, Volume: 2 Issue: 1, Jan/Feb 1998, pp. 58-69 [7] IST Project SHUFFLE (An agent based approach to controlling resources in UMTS networks), available, http://www.ist-shuffle.org Jan. 2001 [8] M. S. Gast, 802.11 wireless networks, the definitive guide (Sebastopol, CA: O’Reilly, 2002) [9] G. T. Okamoto, Smart Antenna Systems and Wireless LANs, KAP 1999. [10] T. S. Rappaport, wireless communications, principles and practice, 2nd edition (Prentice Hall, 2002) [11] SSS Online and Pegasus Technologies, A tutorial on indoor radio propagation, Jan. 2001, available, http://sssmag.com/indoor.html [12] The Java Agent DEvelopment Framework (JADE), developed by TILIB, available, http://jade.cselt.it/ [13] The Foundation for Intelligent Physical Agents (FIPA), available, http://www.fipa.org [14] SNMP++v2.8a, available, http://www.agentpp.com/snmp_ppv2_8/snmp_ppv2_8.html [15] “Management Information Base for Network Management of TCP/IP-based internets: MIB-II”, RFC 1213
