This paper discusses subscriber maximization in CDMA cellular networks

Akl, Robert G.

This presentation gives an overview of code division multiple access (CDMA), traffic and mobility models, subscriber optimization formulation, and numerical results

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Subscriber Maximization in CDMA Cellular Network Robert Akl, D.Sc. University of North Texas Outline • Overview of CDMA • Traffic and Mobility Model • Subscriber Optimization Formulation • Numerical Results • Conclusions Code Division Multiple Access (CDMA) Overview • Multiple access schemes  Call 1 Call 3 Call 2 Call 4 Frequency Time FDMA Frequency Time Call 1 Call 2 Call 3 Call 4 Call 5 Call 6 Call 7 Call 8 Call 9 Call 10 Call 11 Call 12 TDMA CDMA Frequency Time Code Call 1 Call 2 Call 3 Call 4 Relative Average Inter-cell Interference Model dACell j Cell i jrirIji  Erjm x,y 10 j10rim x,y / i2Cj   njAj  dA x,y Iji  e s 2 njAjrjm (x,y)rim (x,y)Cj  dA(x,y)jiI Relative average interference at cell i caused by nj users in cell j 11 12 13 … … 1M 21 22 31 32 … … … … M1 M2 MM F j,i Interference Matrix where F[ j,i] Iji /nj for i, j 1,...,M,and nj is the number of users  in cell jHence, the total relative average inter-cell interference experienced by cell i is Ii  njj1M F j,i 11 12 13 … … 1M 21 22 31 32 … … … … M1 M2 MM I2 1I2 1F[1,2]Capacity • The capacity of a CDMA network is determined by maintaining a lower bound on the bit energy to interference density ratio, given by EbI0iEb RE b  ni 1 Ii /W  N0                                    for i 1,...,M W = Spread signal bandwidth  R = bits/sec (information rate)  α = voice activity factor  ni = users in cell i   N0 = background noise spectral  density ni  Ii W /R11Eb /N01  ceff                                    for i 1,...,M   • Let τ be that threshold above which the bit error rate must be maintained, then by rewriting the equation: Mobility Model • Call arrival process is a Poisson process with rate: λ • Call dwell time is a random variable with exponential distribution having mean: 1/μ • Probability that a call in cell i goes to cell j after completing its dwell time: qij • Probability that a call in progress in cell i remains in cell i after completing its dwell time: qii • Probability that a call will leave the network after completing its dwell time: qi  Mobility Model – Handoff Calls • Handoff calls (vji): calls that have moved from cell j to an adjacent cell i.  ji   j 1 B j q ji  1B j q ji  xjxA j ji  1B j q ji j• Bj : Call blocking probability for cell j • Aj : Set of cells adjacent to cell i • ρj  :  Total offered traffic to cell j j  j   j   xj   j  jxA jNew arriving calls Handoff calls i Admissible States • A new call is accepted if the following inequality still holds upon acceptance, where Ni is the maximum number of calls allowed to be admitted in cell i: ni Ni,  for i 1,...,M• The blocking probability for cell i becomes: Bi AiN i /N i!Aik k!k 0N i,    where   Ai iiMaximization of Subscribers max(1 ,...,M ),(N1 ,...,NM )          ii1M ,       subject to          Bi ,                                 N i  N jF[ j,i]  ceffj1M ,                                 for   i 1,...,M.• Solve a constrained optimization problem that maximizes the number of subscribers subject to upper bounds on the blocking probabilities and lower bounds on the bit energy to interference ratios: Simulations • Network configuration • COST-231 propagation model • Carrier frequency = 1800 MHz • Average base station height = 30 meters • Average mobile height = 1.5 meters • Path loss coefficient, m = 4 • Shadow fading standard deviation, σs = 6 dB • Processing gain, W/R = 21.1 dB • Bit energy to interference ratio threshold, τ = 9.2 dB • Interference to background noise ratio, I0/N0 = 10 dB • Voice activity factor, α = 0.375  Simulations – Network Parameters  No mobility probabilities • qij = 0 • qii = 0.3 • qi = 0.7 Ai qij qii qi 3 0.020 0.240 0.700 4 0.015 0.240 0.700 5 0.012 0.240 0.700 6 0.010 0.240 0.700 Ai qij qii qi 3 0.100 0.000 0.700 4 0.075 0.000 0.700 5 0.060 0.000 0.700 6 0.050 0.000 0.700 Low mobility probabilities High mobility probabilities Traditional Network Topology     and Uniform User Distribution • Maximum subscribers is 15,140. Traditional Network Topology     and Non-Uniform User Distribution • Maximum subscribers drops to 14,224. Optimized Network Topology     and Non-Uniform User Distribution • Maximum subscribers increases to 15,164. Summary • Calculate the maximum number of subscribers that a CDMA cellular network can handle for a given grade-of-service, quality-of-service, network topology, user distribution profile, and mobility.  • Solution yields the maximum number of calls that should be admitted in each cell to guarantee the given requirements above.  

Subscriber Maximization in CDMA Cellular Networks

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Subscriber Maximization in CDMA Cellular Networks

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