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  • Blind Interference Cancellation for the Downlink of CDMA Systems

    Dragan Samardzija, Narayan Mandayam, Ivan Seskar

    Wireless Information Network Laboratory (WINLAB), Rutgers, the State University of New Jersey, 73 Brett Road, Piscataway NJ 08854, USA

  • Downlink of CDMA Systems

    • Downlink of CDMA Systems • Getting more important in the case of data traffic

    • Asymmetric nature of internet traffic requires higher data rates for the downlink

    • Mobile does not have knowledge of number of users (interferers) or their parameters

    • Multiple-access interference (MAI) • Intercell MAI (usually a few strong interferers) • Intracell MAI (considered orthogonal)

  • Multiple-access Interference

    • How to combat the MAI a. Resource management: power control and

    admission control b. Receiver beamforming: requires multiple-

    antenna receiver c. Mitigate interference: multiuser detection

    (MUD) and interference cancellation (IC)

  • Our Approach

    • Blind Successive Interference Cancellation (BIC)

    • Blind approach: the absence of any prior information about the interfering signals

    • Based on maximum mean energy criterion (MME), using 2nd order statistics

  • Successive IC - flow chart

    Start detection, K=1

    Detect contribution of the component K in the received baseband signal

    Reconstruct the contribution of the component K

    Cancel the reconstructed contribution

    Stopping rule


    Interference parameters: prior knowledge or estimates


    Detect the desired useryes

  • Maximum Mean Energy (MME) Optimization Criterion

    • Find a vector v that maximizes the mean energy (ME) as

    v = argmax{ME = E[(rTu)2]} u

    Received vector: r = Akbksk + n

    Ak,bk,sk are amplitude, info. bit and signature sequence of the kth user, respectively. n is additive noise.

    • Constrain the vector v such that vTv=1

    ∑ =



  • MME Optimization Criterion, contd.

    • Maximizing the ME, with respect to v, results in solution that satisfies

    E(rrT) v = λ v

    • Note that v and λ are an eigenvector and an eigenvalue of the matrix Rr = E(rrT)

    • Remove the desired user contribution from Rr as

    Ri =Rr – (A1)2 s1s1T

    Note that no knowledge is required of the desired user’s bit decision. Now, the MME criterion is applied for Ri

  • MME Optimization Criterion, contd.

    • The eigenvector of Ri that corresponds to the maximum eigenvalue (λmax) is the vector that maximizes the ME (mean energy). That vector is denoted as vmax (the maximizer of ME).

    • If the contribution of vmax is removed from the matrix Ri as follows: R’i= Ri - λmax vmaxvTmax , then the eigenvector v’max that corresponds to the maximum eigenvalue of R’i is the same as the eigenvector that corresponds to the second largest eigenvalue of Ri.

  • Application of the MME Criterion Blind Interference Cancellation

    • Receiver executes the following steps: 1. Estimate Rr (input covariance matrix) 2. Remove the desired user contribution in Rr i.e.,

    determine Ri 3. Find the maximizer vmax for Ri 4. Remove the contribution of vmax in Ri 5. Cancel vmax from the input vector 6. Execute steps 3-5 repeatedly for successive

    cancellation of interference components or perform detection of desired user

  • Estimation of the covariance matrix Rr

    Reconstruction of the desired user contribution A12s1s1T


    Ri = Rr – A12 s1 s1T



    Matrix flow

    Vector flow

    IC stage 1 … Detection of the

    desired user b1= sgn(s1 T r’L )

    r r’1 r’L

    R’i 1 R’i L-1


    Evaluation of the maximizer vmax (using the ED)

    + – λmax vmax vmaxT


    Reconstruction of the maximizer contribution in the received signal


    – (rT vmax) vmax



    vmax vmax

    Prevention of excessive cancellation of the desired user




    Ri R’i 1 IC stage 1




    Optional block

    IC stage L

  • Signal Space Interpretation

    Example: 5 users in 3D space

    v1…3 – Orthogonal basis for interference subspace

    s2…5 – Interferer signature sequences A2...5 – Interferer amplitudes r – Received vector smax ~ v1 – ME maximizer smin ~ v2 – ME minimizer

    The maximization scheme determines a vector which captures most (on average) of the energy of the interference.











  • Simulation Results

    • Synchronous CDMA system, randomly generated signature sequences, processing gain G=64

    • Case 1: Number of users L=16 , interferers have the same energy: Ai2/A12=25, i=2...16

    • Case 2: Number of users is L=4, the energy of interferers is 16 times higher than in Case 1: Ai2/A12=400, i=2...4

    • Covariance matrix is estimated as

    Rr=E(rrT) ~ r(i) rT(i) /n

    • Comparison with blind MMSE where copt = s1

    ∑ =

    = n

    1i r

    ^ R


    rR 1−

  • BER vs. SNR, G=64, L=16, Ai2/A12=25, n=500.

  • BER vs. Number of the IC stages, G=64, L=16, Ai2/A12=25, SNR=8 dB, n=500.

  • BER vs. SNR, G=64, L=4, Ai2/A12=400, n=500.

  • BER vs. Number of the IC stages, G=64, L=4, Ai2/A12=400, SNR=6 dB, n=500.

  • BER vs. Window size, G=64, L=4, Ai2/A12=400, SNR=6 dB.

  • Why Does It Work Better?

    § It is still based on the second order statistics, but it outperforms the blind MMSE

    § Estimation Rr=E(rrT) ~ r(i) rT(i) /n

    Introduces specific distribution of eigenvector and eigenvalue estimation errors

    § BIC-MME receiver deals with the estimation errors more efficiently than other well known blind receivers

    ∑ =



  • Conclusions

    • A blind SIC receiver was derived using the MME criterion

    • Performance gains over the blind MMSE scheme are greater with smaller number of samples (for estimation of the covariance matrix)

    • Particularly effective for a system with a few very strong interferers

    • This may be a very viable solution for implementation on the downlink

    • Future study: fading channels, packet traffic

  • Ongoing Research: Implementation of the Advanced Receivers

    Blind SIC Implementation Issues

    a. Iterative solutions b. Suitable for the pipelining (i.e., parallel

    executions) c. Number of the SIC stages directly

    corresponds to the performance, i.e., it is easy to control tradeoff between performance vs. complexity

  • Ongoing Research: Implementation of the Advanced Receivers

    WINLAB Wireless Testbed: Core Technologies 1. DSP

    a. Multiprocessor systems (parallelism, pipelining…)

    § Processor communication: comm. ports vs. global access memory

    b. DSP as a controller for reprogrammable hardware 2. FPGA

    a. Parallelism, pipelining…

    b. Adoption of DSP functionalities (MPY+ADD):

    § Implementation of vector and matrix operations (vector multiplications, matrix inverse, eigen-decomposition)

    § Performance vs. complexity evaluations

  • Winlab Testbed-SW

    System Level Simulations ( SPW, Matlab…)

    HW/SW partitioning

    DSP Simulation (SPW-CGS)

    Partitioning (SPW-Muliprox)

    DSP Compiler

    DSP Boards

    VHDL Description (SPW-HDS)

    Synthesis (VSS)

    FPGA Place and Route (Xilinx Foundation)

    APTIX/FPGA Board

  • Winlab Testbed-HW

    VME Bus Adapter

    MIX Baseboard CPU (MC 68020)

    Quad DSP TMS320C40 Processor

    M IX

    B u


    V M

    E B

    u s

    Dual A/D & Multiband receiver (Pentek 6472 &




    Dual A/D (AD9042ST)

    Dual D/A (AD9742XR)

    RF Frontend

  • Model 4270 - Quad TMS320C40 Processor

  • n Problems that have not been efficiently solved in the case of conventional linear detectors

    a. Presence of highly correlated and/or linearly non- independent interferer signature sequences (case of highly loaded system)

    b. Multipath c. Mismatch

    § Our solution: Nonlinear blind interference cancellation using higher order statistics.

    a. Blind equivalent to nonlinear centralized SIC b. Related to blind source separation

    Ongoing Research: Nonlinear Blind SIC schemes