Multiuser Detection E Suresh Kumar M Tech Multiuser

  • Slides: 24
Download presentation
Multiuser Detection E Suresh Kumar , M. Tech

Multiuser Detection E Suresh Kumar , M. Tech

Multiuser Detection In all CDMA systems and in TD/FD/CD cellular systems, users interfere with

Multiuser Detection In all CDMA systems and in TD/FD/CD cellular systems, users interfere with each other. In most of these systems the interference is treated as noise. Systems become interference-limited Often uses complex mechanisms to minimize impact of interference (power control, smart antennas, etc. )

 Multiuser detection exploits the fact that the structure of the interference is known

Multiuser detection exploits the fact that the structure of the interference is known Interference can be detected and subtracted out Better have a darn good estimate of the interference

MUD System Model Synchronous Case MF 1 y(t)= s 1(t)+ s 2(t)+ s 3(t)+

MUD System Model Synchronous Case MF 1 y(t)= s 1(t)+ s 2(t)+ s 3(t)+ n(t) sc 1(t) MF 2 sc 2(t) MF 3 y 1+I 1 y 2+I 2 Multiuser Detector y 3+I 3 sc 3(t) Matched filter integrates over a symbol time and samples

MUD Algorithms Multiuser Receivers Optimal MLSE Suboptimal Linear Decorrelator Non-linear MMSE Multistage Decision -feedback

MUD Algorithms Multiuser Receivers Optimal MLSE Suboptimal Linear Decorrelator Non-linear MMSE Multistage Decision -feedback Successive interference cancellation

Optimal Multiuser Detection Maximum Likelihood Sequence Estimation Detect bits of all users simultaneously (2

Optimal Multiuser Detection Maximum Likelihood Sequence Estimation Detect bits of all users simultaneously (2 M possibilities) Matched filter bank MF 1 s 1(t)+s 2(t)+s 3(t) sc 1(t) sc 2(t) sc 3(t) MF 2 MF 3 y 1+I 1 Viterbi Algorithm y 2+I 2 y 3+I 3 Searches for ML bit sequence

Suboptimal Detectors Main goal: reduced complexity Design Near tradeoffs far resistance Asynchronous Linear versus

Suboptimal Detectors Main goal: reduced complexity Design Near tradeoffs far resistance Asynchronous Linear versus synchronous versus nonlinear Performance Limitations conditions versus complexity under practical operating

Common Methods used in Suboptimal Detectors Decorrelator MMSE Multistage Decision Feedback Successive Interference Cancellation

Common Methods used in Suboptimal Detectors Decorrelator MMSE Multistage Decision Feedback Successive Interference Cancellation

Mathematical Model Simplified system model (BPSK) Baseband signal for the kth user is: sk(i)

Mathematical Model Simplified system model (BPSK) Baseband signal for the kth user is: sk(i) is the ith input symbol of the kth user ck(i) is the real, positive channel gain sk(t) is the signature waveform containing the PN sequence k is the transmission delay; for synchronous CDMA, k=0 for all users

 Received signal at baseband K number of users n(t) is the complex AWGN

Received signal at baseband K number of users n(t) is the complex AWGN process

Matched Filter Output Sampled output of matched filter for the kth user: 1 st

Matched Filter Output Sampled output of matched filter for the kth user: 1 st term - desired information 2 nd term - MAI 3 rd term - noise Assume two-user case (K=2), and

Symbol Detection Outputs of the matched filters are: Detected symbol for user k: If

Symbol Detection Outputs of the matched filters are: Detected symbol for user k: If user 1 much stronger than user 2 (near/far problem), the MAI rc 1 x 1 of user 2 is very large

Decorrelator Matrix representation where y=[y 1, y 2, …, y. K]T, R and W

Decorrelator Matrix representation where y=[y 1, y 2, …, y. K]T, R and W are Kx. K matrices Components of R are cross-correlations between codes W is diagonal with Wk, k given by the channel gain ck z is a colored Gaussian noise vector

 Solve for x by inverting R Analogous to zero-forcing equalizers for ISI Pros:

Solve for x by inverting R Analogous to zero-forcing equalizers for ISI Pros: Does not require knowledge of users’ powers Cons: Noise enhancement

Multistage Detectors Decisions produced by 1 st stage are 2 nd stage:

Multistage Detectors Decisions produced by 1 st stage are 2 nd stage:

 and so on…

and so on…

Successive Interference Cancellers Successively subtract off strongest detected bits MF output: Decision made for

Successive Interference Cancellers Successively subtract off strongest detected bits MF output: Decision made for strongest user:

 Subtract this MAI from the weaker user: all MAI can be subtracted is

Subtract this MAI from the weaker user: all MAI can be subtracted is user 1 decoded correctly MAI is reduced and near/far problem alleviated Cancelling the strongest signal has the most benefit Cancelling the strongest signal is the most reliable cancellation

Performance of MUD Rayleigh Fading

Performance of MUD Rayleigh Fading

Near Far Resistance Received signals are received at different powers MUDs should be insensitive

Near Far Resistance Received signals are received at different powers MUDs should be insensitive to near-far problem Linear receivers typically near-far resistant Disparate power in received signal doesn’t affect performance Nonlinear MUDs must typically take into account the received power of each user Optimal power spread for some detectors (Viterbi’ 92)

Channel Estimation (Flat Fading) Nonlinear MUDs typically require the channel gains of each user

Channel Estimation (Flat Fading) Nonlinear MUDs typically require the channel gains of each user Channel estimates difficult to obtain: Channel changing over time Must determine channel before MUD, so estimate is made in presence of interferers Imperfect estimates can significantly degrade detector performance Much recent work addressing this issue Blind multiuser detectors Simultaneously estimate channel and signals

Multipath Channels In channels with N multipath components, each interferer creates N interfering signals

Multipath Channels In channels with N multipath components, each interferer creates N interfering signals Multipath signals typically asynchronous MUD must detect and subtract out N(M-1) signals Desired signal also has N components, which should be combined via a RAKE. MUD in multipath greatly increased Channel estimation a nightmare Current work focused on complexity reduction and blind MUD in multipath channels (Wang/Poor’ 99)

Power Control for Fixed Channels Seminal work by Foschini/Miljanic [1993] Assume each node has

Power Control for Fixed Channels Seminal work by Foschini/Miljanic [1993] Assume each node has an SIR constraint Write the set of constraints in matrix form

Summary MUD a powerful technique to reduce interference but has practical issues such that

Summary MUD a powerful technique to reduce interference but has practical issues such that not currently used. Power control is an important aspect of managing interference in CDMA Hard to do under changing channel conditions. Power and rate adaptation in CDMA very powerful To avoid “chicken and egg” problem in CDMA adaptation, can look at system in the wideband limit