Subject: [NEXTEL-1] On Interference Rejection in Wireless Multichannel Systems

From: [email-masked]
Date: Wed, 11 Feb 2004 19:31:48 -0500

Excellent points on reducing interference through:

   * Optimization
   * Diversity
   * Spatial Diversity
   * Interference rejection techniques
   * Adaptive equalization techniques

===============================================================
On Interference Rejection in Wireless Multichannel Systems
Goran Klang
2003
TRITA{S3{SB{0328
ISSN 1103{8039
ISRN KTH/SB/R - - 03/28 - - SE
Signal Processing
Department of Signals, Sensors and Systems
Royal Institute of Technology
Stockholm, Sweden

 "combinations of diversity and interference rejection techniques are usually
considered to make a receiver robust against interference.  Whereas diversity
methods aim at optimizing the reception of the desired user's signal,
interference rejection techniques incorporate known or assumed characteristics
of the interference into the optimization."

<SNIP>

Interference Rejection

In most cellular systems the service and capacity demands are far from
uniformly distributed. Neither over the geographical service area nor in
time. To match such local variations, cellular network architectures employing
cells with di_erent reuse distances, coverage areas, and number
of assigned physical channels per cell are often used in practice. From
a downlink communication perspective, i.e., in communication scenarios
where signals are conveyed from a network access point to a mobile
terminal, this poses no problem as long as the physical channel reuse
distances are su_ciently large since the level of cochannel interference in
the di_erent cells will then be low, cf. Figure 1.1a. For the uplink case
however, i.e., communication from a mobile terminal to a network access
point, the situation is di_erent. As mobiles on the rim of large cells must
transmit with high output power in order to be detected by their assigned
1.2 Interference Rejection 5
c) d) b) a)

Figure 1.1: Examples of interference scenarios in cellular systems. a) Downlink
communication with sparse physical channel reuse: no or small cochannel
interference problems. b) Uplink communication in small cell close to large
cell, c) downlink communication with dense physical channel reuse, d) uplink
communication with dense physical channel reuse: severe cochannel interference
problems may occur.  network receivers, network receivers operating in small
cells close to large cells may experience severe cochannel interference, cf.
Figure 1.1b. Similar, considering short physical channel reuse distances, strong
interference may occur in both links as the coverage areas of di_erent cells
using the same set of physical channels may partly or in the worst case
scenarios even completely overlap, cf. Figure 1.1c{d. Thus, in order to achieve
both e_cient spectrum utilization and avoid local capacity shortages due to the
emergence of cochannel interference e.g., in scenarios as the one discussed
above, it is of fundamental importance that the generation of interference from
the transmitters is minimized but even more important that the receivers are
inherently robust to interference.

1.2.1 Interference Rejection Techniques

Much of the original interest in interference rejection and the use of
techniques for mitigating the impact of interference has its roots in military
applications. Primarily to protect signals of interest from being intentionally
or unintentionally jammed, but also to _nd e_ective methods 6 1 Introduction to
achieve jamming. However, concurrent with the rapid growth seen in mobile
communications that has lead to capacity problems in many networks, the interest
for using interference rejection techniques in nonmilitary applications has
gradually increased.

Di_erent combinations of diversity and interference rejection techniques are
usually considered to make a receiver robust against interference.  Whereas
diversity methods aim at optimizing the reception of the desired user's signal,
interference rejection techniques incorporate known or assumed characteristics
of the interference into the optimization. Although it is often of subordinate
importance what type of diversity that is exploited from an interference
rejection point-of-view, in the literature, interference rejection algorithms
are often classi_ed according to whether they exploit spatial diversity at the
receiver or not. That is, whether the algorithm is a single or multi-antenna
technique.  Interference rejection techniques for single antenna receivers have
for practical reasons attracted much attention in the literature. Although
optimal detection in the presence of cochannel interferers in general requires
joint multiuser detection [Van76, Ver86, Wal95], many suboptimal detectors based
on interference rejection have been proposed as a response to the fact that many
receivers lack both su_cient information and computational complexity in order
to implement an optimal detector. In for example [NV91, NAZ93, PF94, LFS95,
YFS97] di_erent adaptive equalization techniques are discussed for handling both
adjacent as well as cochannel interference. For signals having constant modulus
and signals with known envelope, examples of adaptive _lters trying to achieve
interference rejection by restoring the envelope of the signal can be found in
[TA83, TL85b, TL85a, MRHA91, FTT97, JSE+98].  A third approach is to consider
neural networks based _lters [Hay96].  In [CM92, CK92, Arn93, HSD97, XLY01] the
use of radial basis function networks are addressed and in [Bi92, SS94]
feed-forward networks with back propagation are considered. Techniques
exploiting periodic structures in the signals, such as cyclostationarity, are
discussed e.g., in [RH90, Gar93, RYH95].

For additional references, the interested reader is referred to e.g. [LR97].
Due to the trends toward less expensive hardware and communication
using wavelengths in the order of centimeters rather than meters, the use
of multi-antenna receivers has become increasingly popular during the
last decade as a viable approach to achieve diversity in many practical
applications. Not only in the network access points of a system but also in
the terminals. Using multiple sensors, linear combinations can be formed
1.3 Parameter Estimation 7

by the receiver to help improve the estimate of the signal transmitted.  An
operation known as spatial _ltering or beamforming [MM80, VB88, KV96, God97,
Win98, Tso99, BO01, Tre02]. In contrast to the use of only one receive antenna,
having access to observations collected in the spatial domain, both spatial and
spatio-temporal processing approaches can be considered to improve the
interference robustness of a receiver. Depending on the known or assumed
characteristics and properties of the interfering signals many di_erent
detection schemes have been proposed.  In [ME86, VPPF94, BJ95, KH96, DP97,
Kar98, MB98] the cochannel signals are modeled as temporally white but spatially
correlated Gaussian noise in order to arrive at a maximum likelihood sequence
estimator (MLSE) [Pro95] for the signal of interest. The Gaussian assumption is
also considered in [Slo96, LCP97, AO98b, AO98a, Ast99, BMC99] to take the
presence of both spatial and temporal correlations in the cochannel interference
into account.

As a complement to the use of diversity at the receivers, exploiting spatial
diversity at the transmitters combined with coding| commonly referred to as
transmit diversity|has been proposed as a viable method to help improve the
performance of receivers operating in fading environments [Wit91, SW93, TSC98,
Ala98, TJC99]. However, in considering transmit diversity schemes based on
space-time codes [TSC98, Ala98, TJC99], new dimensions to the interference
rejection problem are added. Since transmitters employing space-time codes
periodically transmit temporally correlated sequences in parallel to providing
diversity at the receivers, cochannel interference observed from such
transmitters will typically be cyclostationary. A fact that, if not taken into
account, may severely degrade the performance of conventional interference
rejection methods [KO03]. Di_erent approaches to handle cochannel interference
due to space-time block codes may be found in e.g.,[NS99, NSC00, KN00, LSL02,
LLC02].

Parameter Estimation

(Optimize, Optimize, Optimize, Interferemce away)

Signal processing algorithms in general and interference rejection algorithms
in particular are inherently model based. A fundamental problem
of many applications is however that the exact parameter values of these
models will not be a priori available. Instead they need to be estimated
prior to applying the signal processing algorithm. To handle this problem,
most wireless communication systems have known training data period-
ically inserted into the transmission protocol to support synchronization
and parameter estimation. As the throughput over a given communication
link is often _x, in practice, the number of training symbols sent at
each occasion is usually determined by a tradeo_ between the minimum
required estimate accuracy and the maximum acceptable level of system
overhead.

For receivers to be robust against interference, they need accurate
models describing the fundamental characteristics of the interfering signal.
As the characteristics of the physical propagation channel and the
interference scenario may change, the available models need to be continuously
re-estimated. Hence, the receivers ability to support e_cient
spectrum utilization in a wireless system will in practice strongly depend
on the accuracy of their di_erent signal and model parameter estimates.
To maximize spectrum e_ciency and minimize system overhead, it is
thus of major importance that the estimators providing these estimates
are e_cient in their use of the available training data. That is, that the
estimators take advantage of all known structures present in the underlying
physical problem in order to improve the accuracy of the estimates.
Estimators designed to meet this requirement are usually refereed to as
structured estimators.

1.3.1 Blind Parameter Estimation Techniques

For interference rejection purposes, having access to models describing
the propagation channels of the interfering users is often very useful. Despite
the fact that most cochannel interference in cellular networks is
generated by transmitters incorporating training data into their transmission
protocols, as it is often too complicated to keep all receivers
in a network updated about the training sequences used by all active
transmitters, the receivers must in general resort to blind or semi-blind
parameter estimation techniques to gain this information.
Blind parameter estimation amounts to retrieving parameter estimates
from the outputs of a system where no explicit knowledge
about the system input data is available. For channel identi_cation,
this was _rst shown possible by Sato [Sat75] taking advantage of the
fact that communication signals are in fact highly structured. In
the early era of blind channel identi_cation, use of high-order statistics
was frequently considered to identify non-minimum phase systems
[Sat75, God80, TA83, BG84, SW90, Men91]. A major drawback working
with high-order statistic based estimators is their slow convergence rate.

1.4 Outline and Contributions 9

Often very large sets of output data must be available to obtain accurate
estimates. Under time varying conditions this fact limits the usefulness
of these high-order statistics based estimators and implies that to ensure
fast convergence, only estimators working with second order statistics
or _nite alphabet properties are suitable from a practical point-of-view.
Unfortunately, considering a single-input single-output (SISO) system, it
can be veri_ed that unique identi_cation of the channel is impossible
considering
only the second order statistics of the output data. However, as
was _rst discovered and exploited by Tong et al. in [TXK91, TXK93] and
later by several other researchers (for example [MDCM95]), by transforming
the SISO system into a single-input multiple-output (SIMO) system
considering for example temporal oversampling, it can be shown that
unique identi_cation up to a complex scalar multiplication of the channels
is indeed possible studying only the second order statistics of the
observed data. For thorough discussions on blind estimation, the interested
reader is referred to the overviews given in [LXTK96, TP98] and
the references therein.

In contrast to the standard blind parameter identi_cation problem
addressed in the references above, using blind parameter estimation techniques
in a receiver to extract information about interfering users, a complicating
factor exist. That is, the desired user's signal will act as interference
while estimating the parameters of the interfering users. In this
thesis, a semi-blind parameter estimation approach is suggested where
the known training data of the desired user is taken into account to handle
this problem.

http://www.lib.kth.se/Fulltext/KLANG030613.PDF

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