towards forward-link interference cancellation tsg-c source: title: abstract: recommendation: ©2005...

Towards Forward-Link Interference Cancellation

TSG-C

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©2005 LG Electronics, Inc.LG Electronics, Inc. grants a free, irrevocable license to 3GPP2 and its Organizational Partners to incorporate text or other copyrightable material contained in the contribution and any modifications thereof in the creation of 3GPP2 publications; to copyright and sell in Organizational Partner's name any Organizational Partner's standards publication even though it may include all or portions of this contribution; and at the Organizational Partner's sole discretion to permit others to reproduce in whole or in part such contribution or the resulting Organizational Partner's standards publication. LG Electronics, Inc. is also willing to grant licenses under such contributor copyrights to third parties on reasonable, non-discriminatory terms and conditions for purpose of practicing an Organizational Partner’s standard which incorporates this contribution. This document has been prepared by LG Electronics, Inc. to assist the development of specifications by 3GPP2. It is proposed to the Committee as a basis for discussion and is not to be construed as a binding proposal on LG Electronics, Inc.. LG Electronics, Inc. specifically reserves the right to amend or modify the material contained herein and to any intellectual property of LG Electronics, Inc. other than provided in the copyright statement above.

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This contribution discusses the benefits of implementing advanced signal processing for forward link.

FYI

Sang G. Kim, Shu Wang, Soonyil Kwon and Hobin Kim

2 LGE Mobile Research, USA

Outline

• Introduction• Multiuser signal modeling and blind receiver design

» Conventional multiuser signal modeling and receiver design

» Subspace-based signal modeling and receiver design» New approach to blind multiuser signal modeling and

receiver design

• Multiuser receiver performance evaluation» Traditional evaluations» Link level simulation considerations» Radio network simulation considerations» Implementation complexity

• Conclusions and recommendations


Introduction (1/3)

• Through the past 20s-year academic and industrial research, it shows that interference cancellation techniques have the potentials to increase wireless network reliability and capacity.

• Narrowband interference cancellation techniques, named single-antenna interference cancellation, have been intensively investigated for GSM/EDGE network [Cingular 03].» Joint demodulation: accurate interference estimation and

mitigation, high complexity; ~70% voice capacity gain for synchronous network.

» Blind interference cancellation: low complexity; ~40% voice capacity gain for synchronous network.


Introduction (2/3)

• The feasibility of Common Pilot Channel (CPICH) interference cancellation at WCDMA user equipment has been investigated [3GPP-TR25.991].» For a Cancellation Set (CS) size of 6, the system level

simulation results reported up to 13.6% gain for voice and 20.6% gain for 144kbps data.

» Due to more receiver impairments/imperfections in practices, these capacity gain results will be reduced.

• There are also reports of using interference cancellation for EV-DO reverse-link.


Introduction (3/3)

• Though interference cancellation is widely regarded as an implementation-related issue, it is known to have nontrivial impacts on the whole system capacity.

• There is growing interest in the possibility of using advanced receiver techniques for enhancing system capacity.

• Therefore it should be important for us to investigate the possible benefits of interference cancellation.

• This presentation focuses on the forward-link interference cancellation techniques.


• Increased spectral efficiency : > 4 bps/Hz• Higher peak data rates and system throughput

capacity• Long-term targets for peak data rate

» Forward Link : 100 Mbps ~ 1 Gbps» Reverse Link : up to 50 Mbps

• To achieve these, the enabling techniques include» Interference cancellation» Smart antenna and MIMO techniques» Advanced scheduling techniques» Etc.

Future Wireless Cellular Requirements


Forward-Link Interference Cancellation

• Compared with AN, AT is known to have many limitations:» limited knowledge of interfering signals,» limited power,» limited computation capability,» and limited physical form factor.

• Given the limitations above, blind approaches are more attractive. » The incorporation of efficient and reliable blind interference

cancellation techniques in the AT design is important for designing the next stage mobile systems.

• Different interpretations of received multiuser signal can lead to different multiuser receiver design frameworks.» Conventional multiuser signal modeling and receiver design» Subspace-based signal modeling and receiver design» New approach to blind multiuser signal modeling and receiver

design


Conv. Multiuser Signal Model (1/3)

2 2 2C

11 1 2L ; , ; ; ;K K K Kb b Ab AA s nsr s

(K-1) unknown interfering signal amplitudes

(K-1) unknown interfering signal signatures or sequences

Unknown information carried by interfering signals

This is the information we want to know

The received signal vector.



r1(τ11)

r1(τ12)

r1(τ13)

r2(τ21)

r3(τ31)

r2(τ22)

r3(τ32)

r2(τ23)

r3(τ33)

r(τ11)

τ11 τ12 τ13t

the desired signal

the interference



• The conventional model is a natural and straightforward description of received signals.

• It is known to be critical in understanding multiuser communication and designing conventional interference cancellation receiver.

• The problem in developing blind interference cancellation: many parameters in this model are unknown beforehand by AT.

s1

s2

SK

r

n

the noise subspace

the signal signature subspace


Example: Blind MMSE MUD

• Multiuser detection using the Wiener filtering techniques.» The detection concept simple and clear.» Their implementation techniques are pretty mature.» Basically treat a multiuser detection problem as a single-

user detection problem.

• Known shortcomings include» without explicitly exploring interference structure, the

convergence speed and tracking capabilities are knowingly not good.

» not suitable for a dynamic environment.


Subspace-Based Multiuser Signal Model (1/2)

1 1S

1 21 2 1L ; , ; , ; ,N NAb υ υ υr s n

N bases of the signal subspace

This is the data we want to know

The current received signal vector


Subspace-Based Multiuser Signal Model (2/2)

• The subspace-based model gives us in-depth understanding of the received signal structure.

• The performance of subspace-based detectors can be the exactly same to conventional detectors.

• The bad thing is the signal subspace separation or matrix inverse is non-trivial.

v1

v2

vN

r

the signal subspace

the noise subspace


Example: Subspace-Based Decorrelator

• The basic detection scheme includes» estimate the phases/delays and spreading sequences of

interfering signals.» calculate a set of orthonormal bases from the signal

signatures/sequences of interfering signals. For example, Gram-Schmidt processing or Gauss elimination.

» reconstruct the conventional decorrelating detector

• The problems with the decorrelators include» the channel estimation cannot be avoided.» the computation complexity still is more than O(N2) with

Gaussian elimination.» possible singularity problem and noise enhancement.» there is performance degradation when α=K/N is close or

greater than 1.


Blind Multiuser Signal Model (1/3)

1B

1 1 , 1L ; ; ; ;1 1b bn An n -M …b n -1 n M n r rr s n

M previously received signal vectors


This is the information we want to know

M previously detected correct information

The signal amplitude of desired users

the trick here is to find the function

LB(…)



• The blind multiuser signal model takes advantage of the correlation between consecutive symbol intervals for estimating interference or detecting the next symbol.

• The problem is there will be possible performance degradation when interference structure is changed, which is a common problem with other approaches.

• However, it can be good at cancelling unknown/unexpected interference.

r[n-1]r[n]

r[n-2]

r[n-M]



1 1H1 1

1 1

1

ˆˆ 1 1ˆˆ

ˆˆ

A n b nA n b n

A n M b n M

f

The interference cancellation can be realized in a simple adaptive filtering format.

n n r f S n

S[n] = [ s1 r[n-1] r[n-1] …

r[n-M] ] is termed a blind signal signature matrix

A new noise component


It is termed detection vector.


Example: A New Interference Cancellation Approach


Multiuser Receiver Design Strategies

• With different signal processing criteria, different blind receiver design scheme can be devised.» least squares based approaches» minimum mean-squared errors based approaches» maximum likelihood based approaches» etc.

• The good thing is that there is no channel estimation necessary.


Performance Evaluation

• Different interference cancellation receivers typically have different trade-offs between performance and complexity.

• Theoretically we consider the evaluation of» traditional parameters: bit-error rate, signal-to-noise ratio» interference-resistance: asymptotic multiuser efficiency,

near-far resistance» large-system performance: effective interference, user

capacity, and bandwidth.

• For the practical evaluations, we need consider» radio network simulations to evaluate capacity gains,» link-level simulations to evaluate feasible accuracy of

cancellation,» implementation complexity.


Computer Simulation (1/2)

group size G=3, total users K=10, blind spreading matrix size M=40, spreading gain N=64


Computer Simulation (2/2)

group size G=1, total users K=2, blind spreading matrix size M=40, spreading gain N=64, SNR=10dB


Network Level Simulation Considerations

• Many network level simulation assumptions should be made:» the cell network architecture, e.g. single cell or multiple-

cells, etc.» the definition of Cancellation Set. The Cancellation Set size

can be between the number of total received signals and the Active Set size.

» the modeling of IC-enabled/disabled power control and handover.

» the distribution of IC-enabled ATs and IC-disable ATs.


Link Level Simulation Considerations

• In link level simulation of interference cancellation receiver, we may consider» the spreading sequence allocation for interfering signals» in a multi-cell link level simulation, the power spectral

densities of other cells will be modeled, whether or not they are in the Cancellation Set.

» sensitivity performance with estimation imperfection and/or DSP errors.

» the delays of different multipath and interfering signals.


Implementation Complexity

• The evaluation of major receiver DSP components should include» the calculation of the cross-correlation terms between

different spreading codes,» the tracking of timing,» the channel estimation,» the regeneration of interference terms,» the cancellation of interference terms,» etc.


Conclusions and Recommendations

• With the investigation of existing interference cancellation applications, it is suggested that the employment of interference cancellation techniques at AT is important for enhancing the capacity of future mobile systems.

• With different understandings of received signal, three interference cancellation frameworks are discussed.

• For the evaluation purpose, several considerations for network level, link level and complexity evaluations are recommended.

towards forward-link interference cancellation tsg-c source: title: abstract: recommendation: ©2005...

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