WiMAX Network Design

Fernando Andrés Sánchez Gonzálezfernandoandressanchez@gmail.com

References can be found in last slides

21.Jun.2007

Demand is a fact

In many countries around the world, the frequency bands in which WiMAX systems will be implemented has already been assigned by regulatory bodies.

Software packages, like EDX and ICS Telecom, already offer WiMAX tools, however they can be complemented, through convenient studies.

A good design let us bring a better service to our clients with a lower cost.

This presentation encompass a literature based algorithm to design or expand a WiMAX Network.

Optimal WiMAX Network Design

SS 1,

(Subscriber Station)

SS 2

SS 3

SS n

BS 1

(Base Station)

BS 2

BS m ??

Optimal WiMAX Network Design

We want to determine the best Base Station Configuration, from CPE (Customer Premises Equipment) related conditions. For example an ISP(Internet Service Provider) has demand predictions for a particular city, we can select the optimal position, frequency assignment and operational parameters in order to maximize their RoI (Return on Investment), and optimize the network operation.

WiMAX Forum, WiMAX: The Business Case for Fixed Wireless Access in Emerging Markets June 2005

Considered equipment

The proposed algorithm considers: IEEE 802.16 2004 Standard. Propagation Models and Received Signal Strength (Link

Budget). Data Rate Capacity (BS´s and SS´s restrictions). Adaptive Modulation. Cost of the new design with regard to ISP´s old

infrastructure. CAPEX (Capital Expenditure). Characteristics of equipment from different vendors. Signal, noise and interference power. Minimum Outage Probability due to Rayleigh Fading.

(Convex Optimization approach). Frequency Assignment. Power Assignment. Propagation model at operation frequency. (attenuation due

to objects like walls could be considered). Handover smoothes a transition to IEEE 802.16 2005.

The next 7 slides present some important characteristics of IEEE 802.16 2004

Standard and key ideas to obtain a good WiMAX network design.

Some characteristics of IEEE 802.16 2004 Standard

• Ranging: UL(Up Link) Power Control and DL(Down Link) Power Control.

• System Profiles.

• Adaptive modulation.

• OFDM(n, Nused, Nfft,...).

• Scheduling Services (UGS, rtPS, nrtPS, BE).

How could we model WiMAX traffic?o UGS (VoIP, T1, E1).o rtPS (MPEG).o nrtPS (FTP).o BE.

• For example, rtPS and nrtPS are high-speed fractal (self-similarity)traffic, and can be modeled by PPBP(Poisson Pareto Burst Process), with parameters: λi, Ti, Ri .

• It is possible to get a relation for 802.16 Wireless MAN traffic between the block probability and the required capacity (this is analogue to Erlang B Formula in Old Circuit Switched Networks). For example, for a given block probability we could determine the required capacity to support some SS’s with 2 VoIP Service Flows and one Internet Service Flow.

These traffics can be modeled by Stochastic

Processes

How could we model WiMAX traffic?Consider the number of SSs in a BS area is N(1≤j≤N), the number of classes of traffic(scheduling services) is 4(1≤i≤4). The amount of traffic of class i arriving at SS j requesting for transmission from BS per second is . are independent random variables.

Now, assume that at the BS, the bandwidth demanded per second for traffic i to ensure that the blocking probability is less than or equal to is , then using the Chernoff Bound and teletraffic theory we can find the following relation between and its associated blocking probability :

C i=∑j=1

N

μij+√2∑j=1

N

σij2

(−ln pi )

μij σ ij

aij

C ip i

aij

C ip i

Where and are the mean and variance of random variable .aijThis capacity corresponds to the BS’s required data rate.

How can we take into account adaptive modulation?

• For each SS we can estimate, from the received power, the modulation scheme at which it is operating, and that let us know the maximum transmission data rate achieved. This is determined as follows.

First we find the effective bandwidth W used by OFDM scheme in IEEE 802.16. The sampling frequency is defined as:

where n is the sampling factor and BW is the channel bandwidth in Hz. But DC and guard band subcarriers transport no information, so OFDM bandwidth efficiency is defined in IEEE 802.16 standard as:

So, we have:

Now thermal noise can be calculated by: k T W. Where k is the Boltzmann’s constant and T is the temperature in Kelvin.

So using the noise figure of the receiver in dB, and taking as the required signal-to-noise ratio at the receiver in dB, we can find the receiver sensitivity in dBm:

Fs=⌊ 8000⋅n⋅BW ⌋8000

BW efficiency=FsBW

N used

N fft

BW efficiency=FsBW

N used

N fft

N subchannels

16

W=FsN used

N fft

N subchannels

16

And when

channelization is used:

SNRrx

Pr ,min=SNRrx+10 log (W )+F+N 0

F

bm

IEEE 802.16 standard specifies the Modulation Scheme and the Coding Rate, when the receiver achieves certain levels. But the last equation shows us that we can relate the with received power levels.

Finally we may calculate the maximum transmission data rate that can be achieved in OFDM PHY as defined in IEEE 802.16 standard:

SNRrxSNRrx

R=N used⋅bm⋅cr

TsWhere is the number of bits per modulation symbol and is the coding rate. is the symbol duration.

Tscr

corresponds to the data rate that can be delivered by the BS.R

We need to balance:

• the BS required data rate and • the data rate that can be delivered by the BS,

taking into account that each BS supports QoS(Quality of Service) using SS admission

control.

We can use an heuristic optimization method (e.g. Simulated Annealing), to design a WiMAX network for a given scenario.

At each step we must model the operation of the network, so we can compare the performance of different designs. But to model the operation of the network, we need to model the power at which equipment operates. IEEE 802.16 standard specifies a distributed power control method, i.e. it uses UL and DL algorithms to implement power control on each link. To model this implies a huge amount of computational effort. Fortunately, distributed power control methods try to achieve the optimal solution that could be obtained with a centralized power control method. So we can find the optimal power assignments that the network could achieve, using a centralized power control method. This can be done in an efficient way using Convex Optimization.

However, optimal power assignment depends on the frequency assignment, but IEEE 802.16 standard does not specify a particular channel frequency assignment (except for minimization of sub-carrier collisions). So different frequency assignments must be considered to find the optimal operation point of the network.

WiMAX Network Design

Proposed Algorithm. (please see the file Proposed_Algorithm.pdf)

Each stage is described briefly in the next slides

A. Associate SS´s with BS´s.

• This stage considers:– Signal strength.

– BS’s required and offered Capacity.

– Admission Control. This is one of the ways by which BS’s guarantees QoS.

– OFDM Characteristics.

– Data rate as a function of distance.

– Equipment characteristics.

– Terrain characteristics.

– IEEE 802.16 standard.

• We find the minimum required powers, Pmin.

A. Associate SS´s with BS´s.

• At this stage we increase the power of all BS’s by small steps, in order to estimate which BS every SS is associated with.

• Here we consider admission control issues, because each BS can’t associate a SS if it can’t provide enough QoS.

• We determine the minimum BS’s powers in order to no SS, than can be covered by a BS, goes out of service.

B1. Initial Frequency Assignment.

This stage uses the classical approximation.

Depends of:

• Number of available channels.

• Frequency Re-use Factor (FRF).

• Since IEEE 802.16 standard does not specify any efficient solution for Channel Assignment Problem, we initially take the classical approach, in which certain pattern is repeated through the coverage area. For this frequency assignment, the network can reach certain powers at optimal operation point. However, it is possible that another frequency assignment yield a better operation conditions, so from optimal operation powers we will find another frequency assignment(Optimal Frequency Assignment). This means that we will consider many Frequency assignments – Optimal power assignments pairs to evaluate each candidate design.

B1. Initial Frequency Assignment.

C. Determine powers at optimal operation point.

• This stage considers:– Relation between CEM(Certainty Equivalent

Margin) and Outage Probability.– Rayleigh Fading.– IEEE 802.16 standard 2004.– Pmin.

• Employs Convex Optimization (which is efficient and deterministic).

• It is based on Perron-Frobenius eigenvalue theory.

We find the optimal power operation point, solving an optimization problem with the form:

C. Determine powers at optimal operation point.

Minimize t

Subject to A P = t P

Pi > 0, i = 1, …, n

This is an eigenvalue problem in which the matrix A has all entries nonnegative. It can be solved efficiently using Convex Optimization. (Matlab is an excellent environment to solve it).

D. Calculate the cost of this design.

• This stage considers:– Signal strength and capacity.

– Infrastructure cost.

– CAPEX.

– Interference levels.

– Operation powers.

– Handover ?

– Outage Probability.

and evaluates a predefined objective function.

B2. Optimal Frequency Assignment.

• In many classic approximations the accumulated influence of several interferers is ignored, a better approach is to determine the channel assignment that maximizes the offered traffic, over the considered area, because many WiMAX users will not be fixed.

• The algorithm employed at this stage is different from Initial Frequency Assignment, and allow us to consider many Frequency assignments – Optimal power assignments pairs.

References• Boyd, Stephen P. CONVEX OPTIMIZATION / Stephen Boyd &

Lieven Vandenberghe, Cambridge University Press 2004. Stanford University.

• H. Wang, B. He, D. P. Agrawal. Admission Control and Bandwidth Allocation above Packet Level for IEEE 802.16 Wireless MAN. Proceedings of the 12th International Conference on Parallel and Distributed Systems. 2006. University of Cincinnati.

• Schafer, T.M.; Maurer, J.; von Hagen, J.; Wiesbeck, W. Experimental Characterization of Radio Wave Propagation in Hospitals.; IEEE Transactions on Electromagnetic Compatibility Volume 47, Issue 2, May 2005. Institut für Höchstfrequenztechnik und Elektronik (IHE), University of Karlsruhe, 76128 Karlsruhe, Germany.

• S. Kandukuri, Stephen Boyd. Optimal Power Control in Interference-Limited Fading Wireless Channels With Outage-Probability Specifications. IEEE Transactions on Wireless Communications. 2002. Stanford University.

• A. Capone, M. Trubian. Channel Assignment Problem in Cellular Systems: A New Model and a Tabu Search Algorithm. IEEE Transactions on Vehicular Technology. 1999.

• L. Betancur, R. Hincapié, R. Bustamante. WIMAX Channel – PHY Model in Network Simulator 2. ACM, WNS2’06, Pisa, Italy. 2006.

• P. Cardieri, F. Figueiredo. Coverage Prediction and Performance Evaluation of Wireless Metropolitan Area Networks based on IEEE 802.16. IEEE Journal of Communication and Information Systems. 2005.

• S. Hurley. Planning Effective Cellular Mobile Radio Networks. IEEE Transactions on Vehicular Technology. 2002.

• C.Prommak, J. Kabara, D. Tipper. Demand-based Network Planning for Large Scale Wireless Local Area Networks. NSF and NIST. School of Telecommunication Engineering, Suranaree University of Technology. University of Pittsburgh. 2004.

References

Thanks a lot !!!