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Cdm anetworkdesign
- 1. CDMA Network DesignCDMA Network Design Robert Akl, D.Sc.
- 2. OutlineOutline CDMA overview and inter-cell effects Network capacity Sensitivity analysis – Base station location – Pilot-signal power – Transmission power of the mobiles Numerical results
- 3. How to match cell design to user distribution for a given number of base stations? – CDMA network capacity calculation – Reverse signal power and power control – Pilot-signal power – Base station location Problem StatementProblem Statement
- 4. CDMA Capacity IssuesCDMA Capacity Issues Depends on inter-cell interference and intra-cell interference Complete frequency reuse Soft Handoff Power Control Sectorization Voice activity detection Graceful degradation
- 5. Relative Average Inter-CellRelative Average Inter-Cell InterferenceInterference [ ] )Area( 10E .deviationstandardandmean zerohasandshadowing,todue nattenuatiodecibeltheis exponent.losspaththeis 102 j j j ζ ii s i C n ω |ζχ σ ζ m i = = − = ∫∫ j j C j i m i ζm j ji x,ydAω /χx,yr x,yr I )( )( 10)( E 2 10
- 6. Soft HandoffSoft Handoff User is permitted to be in soft handoff to its two nearest cells.
- 7. Soft HandoffSoft Handoff [ ] [ ] [ ] [ ]∫∫ ∫∫ ∫∫ ∫∫ <= <= <= <= (c)region 1010210 (c)region 1010210 (b)region 1010210 (a)region 1010210 )(101010E )(101010E )(101010E )(101010E x,yωdAr|rχ r r I x,yωdAr|rχ r r I x,yωdAr|rχ r r I x,yωdAr|rχ r r I jkk kjj ikk ijj ζm j ζm ki ζ m i m k ki ζm k ζm ji ζ m i m j ji ζm i ζm ki ζ m i m k ki ζm i ζm ji ζ m i m j ji
- 8. Inter-Cell Interference FactorInter-Cell Interference Factor .toequalcellinceinterferen averagerelativeaproducecellinusers .celltocellfrom factorceinterferencell-interuserper jij j ji κni jn ij κ
- 9. Capacity RegionCapacity Region .,...,1for 1 11 eff Δ 1 0 req0 Mi c α κnn N E I E R WM j jiji bb = =+ −≤+ = ∑ ( ) .,...,1for 1 req0 0 1 Mi I E NR/WEκnαR/WEnα E b b M j jijbi b = ≥ ++− ∑=
- 10. Network CapacityNetwork Capacity .,...,1for ,0subject to capacity)(network,max 1 1 ),...,( 1 Mi cκnn n eff M j jiji M i i nn M = ≤−+ ∑ ∑ = = Transmission power of mobiles Pilot-signal power Base station location
- 11. Power Compensation FactorPower Compensation Factor Fine tune the nominal transmission power of the mobiles PCF defined for each cell PCF is a design tool to maximize the capacity of the entire network
- 12. Power Compensation Factor (PCF)Power Compensation Factor (PCF) Interference is linear in PCF Find the sensitivity of the network capacity w.r.t. the PCF .,...,1for)( 10 E 1 2 10 Miβc β κβ nn dAω /χr rβ I ieff M j i jij ji j C i m i ζm jj ji j j =≤+ = ∑ ∫∫ =
- 13. Sensitivity w.r.t. pilot-signal powerSensitivity w.r.t. pilot-signal power Increasing the pilot-signal power of one cell: – Increases intra-cell interference and decreases inter-cell interference in that cell – Opposite effect takes place in adjacent cells
- 14. Sensitivity w.r.t. LocationSensitivity w.r.t. Location Moving a cell away from neighbor A and closer to neighbor B: – Inter-cell interference from neighbor A increases – Inter-cell interference from neighbor B decreases
- 15. Optimization using PCFOptimization using PCF .,...,1for ,0)( ,1subject to capacity)(network,max 1 max 1 Mi βc β κβ nn ββ n ieff M j i jij ji M i i β = ≤−+ ≤≤ ∑ ∑ = =
- 16. Optimization using LocationOptimization using Location .,...,1for ,0 ),( subject to capacity)(network,max )( 1 1 Mi c β LCκβ nn n i eff M j i ijjij ji M i i L = ≤−+ ∑ ∑ = =
- 17. Optimization using Pilot-signal PowerOptimization using Pilot-signal Power .,...,1for ,0 ),( subject to capacity)(network,max )( 1 1 Mi c β LCκβ nn n i eff M j i ijjij ji M i i T = ≤−+ ∑ ∑ = =
- 18. Combined OptimizationCombined Optimization .,...,1for ,0)( ),( ,1subject to capacity)(network,max 1 max 1 Mi βc β LCκβ nn ββ n ieff M j i ijjij ji M i i T,L,β = ≤−+ ≤≤ ∑ ∑ = =
- 19. Twenty-seven Cell CDMATwenty-seven Cell CDMA NetworkNetwork Uniform user distribution profile. Network capacity equals 559 simultaneous users. Uniform placement is optimal for uniform user distribution.
- 20. Three Hot Spot ClustersThree Hot Spot Clusters All three hot spots have a relative user density of 5 per grid point. Network capacity decreases to 536. Capacity in cells 4, 15, and 19, decreases from 18 to 3, 17 to 1, and 17 to 9.
- 21. Optimization using PCFOptimization using PCF Network capacity increases to 555. Capacity in cells 4, 15, and 19, increases from 3 to 12, 1 to 9, and 9 to 14. Smallest cell- capacity is 9.
- 22. Optimization using Pilot-signal PowerOptimization using Pilot-signal Power Network capacity increases to 546. Capacity in cells 4, 15, and 19, increases from 3 to 11, 1 to 9, and 9 to 16. Smallest cell- capacity is 9.
- 23. Optimization using LocationOptimization using Location Network capacity increases to 549. Capacity in cells 4, 15, and 19, increases from 3 to 14, 1 to 8, and 9 to 17. Smallest cell- capacity is 8.
- 24. Combined OptimizationCombined Optimization Network capacity increases to 565. Capacity in cells 4, 15, and 19, increases from 3 to 16, 1 to 13, and 9 to 16. Smallest cell- capacity is 13.
- 27. Combined Optimization (m.c.)Combined Optimization (m.c.) .,...,1for , ,0)( ),( ,1subject to capacity)(network,max min 1 max 1 Mi nn βc β LCκβ nn ββ n i ieff M j i ijjij ji M i i T,L,β = ≥ ≤−+ ≤≤ ∑ ∑ = =
- 30. Combined Optimization (m.c.)Combined Optimization (m.c.) Network capacity increases to 564. Capacity in cells 4, 15, and 19, increases from 3 to 17, 1 to 17, and 9 to 17. Smallest cell- capacity is 17.
- 34. ConclusionsConclusions Solved cell design problem: given a user distribution, found the optimal location and pilot-signal power of the base stations and the reverse power of the mobiles to maximize network capacity. Uniform network layout is optimal for uniform user distribution. Combined optimization increases network capacity significantly for non-uniform user distribution.
Editor's Notes
- #2: We are interested in solving the cell design problem. Given a user distribution profile what are the best locations of the base stations, the pilot-signal power and the transmission power of the mobiles to maximize capacity.
- #3: I will start with an overview of CDMA. I will describe what I mean by capacity and find the sensitivity of capacity to base station location, pilot signal power and transmission power of the mobiles. I will use the sensitivity analysis to maximize capacity. I will introduce mobility to differentiate between new calls and handoff calls. I will describe a call admission control algorithm that will guarantee the blocking probabilities for an arbitrary traffic distribution. Finally I will analyze the network performance by calculating the maximum throughput the network can achieve.
- #5: CDMA is interference limited. It’s capacity depends on the inter-cell interference and the intra-cell interference. All mobiles use the same frequency not only in the same cell but in all cells. So no frequency planning is necessary. When users move from one cell to another they switch channels. This is called handoff. In CDMA a user will communicate with both base stations near the boundary and will not relinquish its old channel until the new one is well established. Power control is a necessity in CDMA to solve the near-far problem. The signal of users close to a base station will drown out the signals of users that are far from the base station. So power control ensures that all users’ signals are received at the base station with the same level. Sectorization gives a gain of a factor of almost 3. During silent periods in two-way conversations the transmission rate is reduced so voice activity detection give us a gain of 2.6. Finally CDMA does not have a hard limit on the number of users that the system can admit. The more users the more interference so we get a graceful degradation in quality.
- #6: Consider cells i and j. This user in cell j is at distance rj from its own base station and ri from base station i. The inter-cell interference from cell j to cell i is given by this equation. The numerator is the gain adjustment through power control. The denominator is the propagation loss to base station i. This equation is for hard handoff.
- #7: READ SLIDE
- #8: The interference on cell i is now given by these equations. To calculate Iji we divide the cells into grids and calculate the integrals numerically. This could be a computationally intensive task. But it depends on the user distribution profile.
- #9: Therefore we define the per user interference factor kappa ji. For n_j users in cell j, the inter-cell interference to i is n_j kappa ji. So we only calculate the kappa ji once.
- #10: The signal to interference density ratio required for a given BER give these inequalities. From each we get this inequality for cell i. This has to hold for i equal 1 to M. These set of inequalities have to be satisfied by the ni. This feasible set forms the capacity region. The capacity region is the set of simultaneous users that we can have that satisfy the Eb over Io constraint.
- #12: Starting with the transmission power parameter we introduce the notion of power compensation factor.
- #13: Beta J is the power compensation factor for cell j. Relative average inter-cell interference is linear in the Beta j so adjusting the beta j of one cell does not require the recalculation of the inter-cell interference factors. So the capacity region for the network is now given by these inequalities.
- #14: Increases the cell coverage increases the number of users in that cell. Find the effect of increasing pilot-signal power of one cell on the capacity of the network
- #15: Find the effect of moving one cell on the capacity of the network So now we have found all the derivatives… we want to use them to maximize capacity.
- #16: Use the derivatives in steepest descent algorithm
- #26: Next I will introduce a call admission control algorithm that will guarantee blocking probability for arbitrary traffic distribution.
- #27: Next I will introduce a call admission control algorithm that will guarantee blocking probability for arbitrary traffic distribution.
- #35: Uniform network optimal for uniform user dist. PCF best of three parameters. Increasing pilot signal power is better. Imposing a minimum constraints does not penalize total network capacity when used with our combined optimization but cases a severe penalty for a uniform network topology.