Inter-domain radio resource management for ... - Semantic Scholar

Inter-Domain Radio Resource Management for Wireless LANs Yasuhiko Matsunaga

Randy H. Katz

Internet Systems Research Laboratories NEC Corporation 4-1-1 Miyazaki, Miyamae-ku, Kawasaki, 216-8555, Japan [email protected]

Computer Science Division University of California, Berkeley 637 Soda Hall, Berkeley, CA, 94720-1776, U.S.A. [email protected]

Abstract— The rapid increase of wireless LAN (WLAN) deployments in enterprises and public places will likely cause frequent geographical coverage overlap among different domains. This paper presents a radio resource broker (RRB) architecture that ensures fair allocation of radio resources across domains. The RRB keeps track of each provider’s resource usage, and enforces fair resource allocation across providers by limiting the number of available channels or introducing network-initiated load balancing. We derive an empirical workload model based on measurements from a university campus environment, and then use it to evaluate our approach with simulated dynamic radio resource usage. The simulation results demonstrate that channel compensation and load balancing are effective for redistributing radio resources fairly over a long time span, and that load balancing also performs well over a short span.

To overcome this situation, we propose a radio resource broker that enables coordinated radio resource management among WLAN systems. The radio resource broker (RRB) collects radio link configuration and statistics from different domains, and optimizes radio resource usage by changing frequency channel or transmission power at access points. The RRB also performs network-initiated handover for dynamic load balancing across domains.

KeywordsWireless LAN; Resource Management; Interference; Load Balanding; Heterogeneous Networks

Although our current research is targeted for WLANs, the basic principles of radio resource redistribution can be applied to other wireless systems. For example, some European cellular network operators intend to share the same frequency pool as well as radio access network equipment [5]. The notion of an RRB is also useful for cellular operators who share radio resources, and can help enable the spectrum underlay market being investigated by the spectrum policy task force of FCC [6].

I.

INTRODUCTION

Efficient radio resource usage has always been a primary concern in wireless communication systems, such as wireless LANs (WLANs) and cellular systems. Demands for mobile communications have been increasing rapidly over the last decade although the frequency range suitable for personal communications is scarce. To date, radio resource management (RRM) researches have focused on intra-domain issues, particularly radio resource usage optimization by dynamic channel allocation [1][2] or load balancing [3] inside a single wireless network operator. This is a valid assumption in most cellular systems where each spectrum range is exclusively licensed to a specific wireless operator. On the contrary, WLANs use unlicensed frequency bands shared by various public and private systems, and their radio resources are managed independently [4]. As the number of WLAN systems proliferates and systems overlap, lack of radio resource management coordination can cause significant performance degradation due to inter-domain interference. Moreover, the partitioning of frequencies independent of actual user/traffic density leads to congestion in the successful operators’ network, while less popular operators retain unused excess capacity.

Such a cooperative approach is suitable for environments like multi-tenant buildings and university campuses where there is a strong incentive to use spectrum resources efficiently and fairly. Even for competing public hotspot WLAN operators, the RRB offers an approach for maximizing the performance of otherwise uncoordinated independent network deployments.

In this paper, we first introduce our inter-domain radio resource management architecture, functions of the RRB, and radio resource usage optimization and redistribution procedures performed at the RRB in Section 2. Because the workload characteristics affect traffic engineering performance, we measure the WLAN load statistics in a university campus environment and derive an empirical WLAN workload model in Section 3. Using this model, we evaluate the performance of proposed inter-domain RRM scheme by simulation in Section 4. We show the optimality of radio resource usage versus channel stability, and demonstrate the effectiveness of radio resource redistribution through dynamic channel compensation and network-initiated load balancing. Finally, we list the related research works in Section 5 and conclude the paper in Section 6.

Funding for this work was provided in part by California MICRO program, with matching support from Cisco, HP, Nortel, and NTT Communications.

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II.

INTER-DOMAIN RADIO RESOURCE MANAGEMENT

s.t.

A. Radio Resource Broker Concept Since our goal is to allocate shared radio resources fairly across multiple WLAN domains, we need to have a trusted third party agent who is independent from each domain’s financial interests. Figure 1 depicts the role of a proposed radio resource broker (RRB), which acts as a point of radio resource coordination across domains. In Figure 1, the geographical coverage of WLAN domains A, B, and C are overlapped. The RRB collects measured radio resource usage statistics from access points and mobile clients in each domain by SNMP (Simple Network Management Protocol) [7]. It is a reasonable assumption that the WLAN access points can be monitored and controlled remotely because most enterprise-class access points support SNMP. SNMP version 3 also provides user authentication and message encryption functions [8]. Prior to the measurement, the RRB should know the IP address and generalized region location of each access point to create the coverage overlap map. To collect statistics from individual mobile clients and control them, we need a new client Management Information Base (MIB) for radio resource management. The RRB then analyzes the measured data and provides feedback to optimize the radio resource usage and to redistribute the radio resources fairly across domains. Feedback includes optimal channel and power allocation for the access points, and network-initiated inter-domain handover for mobile clients. The details of the feedback mechanisms are described in the following sections.

Radio Resource Broker

Measurement Data

WLAN Domain A

Optimal Channel/ Power Allocation

Power( x) ∈ Set{Px1 ,..., Pxk } where Nx is the number of cells in domain x, Φ(y) is the link cost function, and ρ(i, Channel(x), Power(x)) is the link utilization at cell i in domain x. We designed the RRB to optimize radio resource usage per each domain instead of optimizing it globally. This allows each domain to have its own network management system that optimizes radio resource usage under the constraints set by the RRB. The first constraint in (2) means the normalized link utilization must be less than the link capacity. The numbers of available channels (the second constraint in (2)) and power levels (the third constraint in (2)) are set by the RRB, and remain constant during each optimization process. It is well known that load balancing can be an effective allocation strategy, when the cost is convex as a function of the allocated loads [3]. Therefore we adopted a piecewise linear convex link cost function Φ(y) similar to the one used in the traffic engineering research paper [9].

for Φ' ( y) = 1 for =5 = 50 for = 500 for = 5000 for

WLAN Domain C

B. Resource Usage Optimization In this section, we describe the radio resource usage optimization framework based on an integer programming formulation. The RRB attempts to optimize each domain’s radio resource usage by minimizing the network cost function F(x) under the restrictions of available channels and power levels. The objective function and the constraints are:

min F( x) =

∑ Φ(ρ(i, Channel( x), Power ( x)))

0 ≤ y < 1/ 3 1/3 ≤ y < 2/3 2/3 ≤ y < 3/4 3/4 ≤ y < 4/5 y ≥ 4/5

(3)

ρ(i, Channel( x), Power ( x)) = (cell i ' s utilization) +

Figure 1. Radio Resource Broker.

Nx

(2)

ρ(i, Channel(x), Power(x)), the link utilization function of cell i, is defined as the sum of own-cell utilization and cochannel utilization by neighbor cells. Strictly, the cell utilization should consider link adaptation because the wireless medium usage depends on the link speed (e.g. 1, 2, 5, 11 Mb/s for 802.11b) used at the time of packet transmission. If per link-speed statistics are not available from the access points, the utilization can be approximated by calculating the ratio of the measured load to the maximum throughput.

Third-Party Roaming Infrastructure

NW-Initiated Load Balancing

WLAN Domain B

ρ(i, Channel ( x), Power ( x)) ≤ 1

Channel( x) ∈ Set{C x1 ,..., C xj }

(1)

(co-channel utilization by neighbor cells) = (cell i ' s utilization) + Nd

(4)

Nx

∑ ∑ [δij • (cell j' s utilization) • S ij / S i ] x =1 j =1, j ≠ i

where  1 if cell i ' s channel = cell j ' s channel δ ij =   0 otherwise

(5)

i =1

Nd is the number of domains, Si is cell i’s service area, and Sij is the overlapped region of cell i’s service area and cell j’s interference area. We assume every cell uses non-overlapped


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channels, and clients are uniformly distributed and associate with the nearest access point for the sake of simplicity. The service area and the interference area shapes are approximated as circles. The radius of the interference area is where the received signal level equals the carrier sense threshold. In general, the interference area is much larger than the service area [10]. The overlapped region Sij can be calculated analytically under the condition that each area is represented by a circle. To calculate the overlapped region Sij, we assumed indoor path loss model recommended in ITU-R P.1238-2 [11],

L[dB] = 20 log10 f + N log10 d + L f (n) - 28

(6)

where L is the total loss, f is the frequency in MHz, N is the distance power loss coefficient, d is the separation distance between the access point and the client terminal, and Lf is the floor penetration loss factor. For example, the N value of 30 is suggested for 2.0 GHz in an office environment. Since radio propagation can be very complex in indoor and metropolitan environments, one can elaborate the calculation of Si and Sij by making use of three-dimensional radio propagation prediction tools [12]. C. Radio Resource Redistribution After the RRB performs radio resource optimization for each domain described in the Section 2.2, it compares each domain’s radio resource usage and redistributes those resources. The algorithm is shown in Figure 2. The calculation is executed for all administrative domains sharing radio resources in the region of interest. Examples include an office building, a university campus, and an airport. Start

Repeat for each administrative domain in the region another domain

finished

F1(x)=Domain x’s network cost without assuming the presence of other domains

End

F2(x)=Domain x’s network cost with assuming the presence of other domains

1 Nd

Note that the radio resource redistribution and resource usage optimization processes are separated. Allocating more radio resources to the domain with larger credit can degrade the global performance, because it is suffering more interference from other domains. The RRB optimizes radio resource usage under the constraint set by radio resource redistribution process. III.

WORKLOAD CHARACTERIZATION

A. Measurement Since the workload greatly affects resource allocation control performance, we need an accurate WLAN workload model with which to assess the effectiveness of our RRB architecture. To the authors’ knowledge, previous WLAN measurement studies [13][14][15] show various aspects of WLAN statistics but none of them gave a realistic workload model suitable for simulations. Therefore we collected load distribution statistics from WLAN access points located at the Computer Science Division’s building at the University of California, Berkeley. Measurements were done during weekday daytime (9AM6PM) periods from June to August, 2003. Both 802.11a and 802.11b WLANs are deployed in the building, but the measurement data were collected from only 802.11b access points. The majority of users are graduate students, and the rest are university staff and faculty. Measurement was accomplished by periodically polling WLAN access points’ interface and bridge MIBs. Considering the overhead of measurement, the polling interval was set to five minutes. Figure 3 shows the log-log plot of the complementary cumulative distribution function (i.e., 1−c.d.f.) versus the load. Circles, triangles and squares correspond to the measured WLAN load statistics at three different access points. As we can see from Figure 3, linear relationships appear while the measured load is much smaller than the MAC layer throughput upper limit (∼ 6 Mb/s). Linear regression results are also plotted in solid, broken and dashed lines, with corresponding Pareto distribution parameters. α and β are the scale and location parameters of a generalized Pareto distribution, whose c.d.f. is

I(x) = Domain x’s cross-domain impairment = F2(x)-F1(x)

Credit( x) + = I( x) −

F2(x) in that F1(x) omits per-domain summation of co-channel utilization by neighbor cells in (4). Then the RRB derives the cross-domain impairment I(x) by subtracting F1(x) from F2(x). The cross-domain impairment relative to the average of impairment of all (Nd) domains is accumulated as a credit. Intuitively, a WLAN domain with larger credit than others has been experiencing interference more severe than others. The RRB allocates more radio resources to the domains with a larger credit by loosening channel or power constraints in (2). It also allocates less radio resources to the domains with a smaller credit by tightening channel or power constraints, or initiating an inter-domain handover to a less-congested domain.

Nd

∑ I( x) x =1

Large credit: Increase # channels / Tx power Small credit: Decrease # channels / Tx power, Initiate inter-domain handover

Figure 2. Radio Resource Redistribution Flowchart.

The RRB calculates each domain’s network cost with (F2(x)) and without (F1(x)) assuming the presence of other domains. F2(x) is identical to the network cost function as defined in Equations (1), (3), (4) and (5). F1(x) differs from


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Fpareto(x) = 1 − (α/x)β.

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(7)

Therefore we can approximate the load statistics by the truncated Pareto distribution, with cutoff values equal to the upper limit of the MAC layer throughput.

Complementary Cumulative Distribution Function

Figure 4 shows the measured burst duration statistics of the same WLAN access points. We used a burst threshold of 100kb/s to define ON/OFF states. Observed ON/OFF duration statistics also showed heavy-tailed behavior, and OFF durations were generally longer than ON durations. The distributions of ON and OFF states are also approximated by the Pareto distribution. 100

10-1

A. Simulation Methodology We confirmed the proposed inter-domain radio resource optimization and redistribution schemes by simulation. Figure 6 shows the cell layout and the initial channels of access points. Two WLAN domains are co-located and sharing the radio resources. Each access point is separated by 30 m, and domain A’s access points and domain B’s access points are placed alternately.

Pareto (α, β) = : (0.61, 0.0096) : (0.58, 0.0040) : (0.75, 0.0054)

0.01

Cell A4's Service Area

30m

-3

10

PERFORMANCE EVALUATION

The parameters used in the simulation are summarized in Table 1. After each measurement interval (5 minutes), each WLAN cell’s load is updated, the RRB optimizes radio resource usage, and redistributes radio resources among domains if necessary. Throughout the simulation, each cell’s transmission power was kept constant. For radio resource redistribution, only the channel compensation and the networkinitiated handover schemes are evaluated.

: AP1 : AP2 : AP3

10-2

IV.

0.1 1 Load [Mbps]

10 30m

Cell A4's Interference Area

A1 (Ch1)

B1 (Ch2)

A2 (Ch3)

B2 (Ch1)

A3 (Ch2)

B3 (Ch3)

A4 (Ch1)

B4 (Ch2)

A5 (Ch3)

B5 (Ch1)

A6 (Ch2)

B6 (Ch3)

A7 (Ch1)

B7 (Ch2)

A8 (Ch3)

B8 (Ch1)

A9 (Ch2)

B9 (Ch3)

A10 (Ch1)

B10 (Ch2)

A11 (Ch3)

B11 (Ch1)

A12 (Ch2)

B12 (Ch3)

A13 (Ch1)


Figure 3. Measured WLAN Load Statistics.

100

10-1

10-2

:AP1(On) :AP1(Off) :AP2(On) :AP2(Off) :AP3(On) :AP3(Off) : Pareto( α, β)=(0.89,2.1) : Pareto( α, β)=(0.51, 5.3)

5

20 10 50 Burst Duration [min]

Figure 6. Cell Layout.

100 TABLE I.

Figure 4. Measured WLAN Burstiness Statistics.

B. Empirical WLAN Workload Model Following the measurement results in the previous section, we derived an empirical WLAN workload model as shown in Figure 5. The per-cell workload is modeled as a two-state Markovian arrival process. The load is generated only in the ON state, and the load distribution is a truncated Pareto distribution. The ON-state duration, and the OFF-state duration also follow Pareto distribution. We used this empirical WLAN workload model in the simulations for performance evaluation. Pareto

Number of APs

OFF

ON Pareto

Domain A

Domain B

13

12

Measurement Interval

5 minutes

Number of Channels

3

Cell Radius

30 m

Tx Power

15 dBm

Radio Frequency

2.4 GHz

Carrier Sense Threshold ON-State Load α ON-State Load β ON-State Load Cutoff ON-State Duration α

Pareto

SIMULATION PARAMETERS

-82 dBm 0.61

0.75

0.0096

0.0054

6.0 Mb/s

6.0 Mb/s 0.89

ON-State Duration β

2.1

OFF-State Duration α

0.51

OFF-State Duration β

5.3

Figure 5. Empirical WLAN Workload Model.


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B. Dynamic Channel Optimization Dynamic channel allocation helps to reduce the probability of congestion among multiple cells by assigning different channels to congested cells. Figure 7 shows the complementary cumulative distribution function of the percentage of congested cells under various channel stability levels. Congested cells refer to the cells whose link utilization function ρ(i, Channel(x), Power(x)) is larger than 0.8. The channel stability level is the number of access points per domain whose frequency channels can be changed at a time. Stability level 0 means all channels are statically allocated throughout the simulation as shown in Figure 6. At the 99 percentile level, the percentage of congested cells is reduced from 14% (Level 0) to 13% (Level 1), 10% (Level 2), and 7.6% (Level3).

We also simulated a network-initiated load balancing. Figure 9 shows the domain B’s credit value over time with load balancing. Both load balancing with and without channel compensation provide a good convergence of domain B’s credit around zero for the short and long time scales. The difference in convergence time between Figure 8 and Figure 9 is due to network-initiated load balancing. Load balancing immediately reduces congestion by relocating clients to a less congested domain. On the other hand, channel compensation rebalances cross-domain impairment by assigning fewer channels to the domain with the smaller cross-domain impairment. Since only a limited number of cells can change channels at any given time, it takes more time to redistribute radio resources than to perform client load balancing. 40000 30000

10000 0 -10000 -20000 0

2000

40000 Channel Stability Level : Level 0 (static) : Level 1 : Level 2 : Level 3

10-2

10-3

500 1000 1500 Elapsed Time (hour)

Figure 8. Radio Resource Redistribution by Channel Compensation.

100

10-1

: Redistribution Disabled : Channel Compensation

20000

Domain B's Credit


It should be noted that the reduction of congested cells is achieved at the cost of channel stability. When an access point changes its channel, all its associated clients are forced to scan other channels and then re-associate with the original access point. This causes an implementation-dependent temporal communication disruption at each client. Another point is that the computational complexity of the optimization process increases as more access points are allowed to change their channels. It is reasonable to limit the channel stability level at a certain point rather than attempt to achieve optimal usage by changing channels frequently. Channel stability level 2 is used in the simulations of the following sections.

growing infinitely as the time elapses. On the other hand, the dashed line shows that domain B’s credit remains within ±10,000 when the channel compensation is enabled. This means the radio resources are fairly redistributed from domain A to domain B by the channel compensation scheme in the long run.

Domain B's Credit

Assuming the IEEE 802.11b-complient systems, only three non-overlapping channels are available in 2.4 GHz band. The workload parameters for domain A and domain B are taken from the measurement results for AP 1 and AP 3 in Figure 3, respectively. The parameters of ON-State and OFF-State duration distribution are taken from the measurement in Figure 4. The traffic demand in domain A is larger than domain B, therefore domain A consumes more radio resources and incurs less cross-domain impairment than domain B.

30000 20000 10000 0 -10000 -20000 0

10 20 % of Congested Cells

500 1000 1500 Elapsed Time (hour)

2000

Figure 9. Radio Resource Redistribution by Network-Initiated CrossDomain Load Balancing.

Figure 7. Distribution of Congested Cells under Various Channel Stability Levels.

Figure 8 shows the effect of radio resource distribution by channel compensation. The solid line shows the time-evolution characteristics of domain B’s credit without any radio resource redistribution mechanism. As domain A’s traffic demand is larger than domain B’s, domain B suffers more cross-domain impairment than domain A, leading to domain B’s credit


: Load Balance : Load Balance+Channel Compensation

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V. RELATED WORK Intra-domain radio resource optimization is a classical research topic, and much works have been done for circuitswitched cellular network. Dynamic channel allocation was investigated extensively in 1990’s, see Katzela and Naghshineh’s review paper [1] for a variety of channel

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allocation algorithms. Alanyali and Hajek [3] showed a simple least loaded cell selection policy can achieve optimal load balancing result for large call arrival rates. Recently dynamic channel allocation and network-initiated load balancing for WLANs were also investigated [16] and actually implemented by several vendors [17][18]. On the other hand, there are few researches on inter-domain radio resource management. Zhuang et al. [19] presented the architecture of multi-domain resource reservation for end-to-end QoS guarantee in UMTS, but they assumed each domain’s resource is separate and is managed independently. To the authors’ knowledge, there is no previous work in the fair allocation of shared radio resources among different domains.

fellow at University of California, Berkeley from Dec. 2002 to Dec. 2003. REFERENCES [1]

[2] [3] [4] [5]

VI.

CONCLUSION

In this paper, we presented a framework of fair-sharing the radio resources among different administrative domains. Based on the measurement data collected from the WLAN access points and stations, the RRB optimizes and redistributes the radio resources among the domains by dynamic channel compensation, power control, or network-initiated load balancing. To characterize the cell-level workload, we collected statistics from a campus WLAN and derived an empirical two-state Markov model. Using that workload model, we confirmed our proposed schemes by simulating radio resource redistribution between two domains with different traffic demands. Simulation results show each domain’s credit (cross-domain impairment) is kept within an allowable level in the long run by using either channel compensation or load balancing schemes. Thus the fair allocation of radio resources among domains is possible. We also showed that networkinitiated load balancing is effective for redistributing radio resources in a short time span. We are currently implementing the proposed inter-domain radio resource management schemes with 802.11 WLAN and Linux laptops. Future work will include the pricing of shared radio resources and the generalization of the WLAN workload model by making use of other wide-area measurement results.

[8] [9] [10]

[11]

[12]

[13] [14]

[15]

[16]

[17] [18]

ACKNOWLEDGMENT The authors would like to thank Fred Archibald for allowing to collect campus WLAN statistics. The authors also would like to thank Kazuhiro Okanoue and anonymous reviewers for many valuable comments. Yasuhiko Matsunaga was supported by NEC Corporation as a visiting industrial


[6] [7]

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