Pulse or digital communications – Receivers – Interference or noise reduction
Reexamination Certificate
1999-11-02
2002-04-23
Chin, Stephen (Department: 2634)
Pulse or digital communications
Receivers
Interference or noise reduction
C375S260000, C375S347000, C370S342000
Reexamination Certificate
active
06377636
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to wireless communication systems and methods of operating such systems to mitigate interference with the aid of coordinated transmission and training.
BACKGROUND OF THE INVENTION
Wireless communication systems serving stationary and mobile wireless subscribers are rapidly gaining popularity. Numerous system layouts and communications protocols have been developed to provide coverage in such wireless communication systems.
Currently, most wireless systems are broken up into separate coverage areas or cells. Typically, each cell has a base station equipped with an antenna for communicating with mobile or stationary wireless devices located in that cell. A cellular network consists of a number of such cells spanning the entire coverage area. The network has an assigned frequency spectrum for supporting communications between the wireless devices of subscribers and base stations in its cells. One of the constraints on a wireless communication systems is the availability of frequency spectrum. Hence, any wireless system has to be efficient in using its available frequency spectrum.
It is well-known that attenuation suffered by electromagnetic wave propagation allows wireless systems to re-use the same frequency channel in different cells. The allowable interference level between signals transmitted in the same frequency channel determines the minimum separation between cells which can be assigned the same frequency channel. In other words, frequency channel re-use patterns are dictated by the amount of Co-Channel Interference (CCI) seen by the receiving unit (either the base station or the wireless subscriber device).
As an example of frequency re-use,
FIG. 1
shows a portion or a cluster
10
of a typical wireless cellular system with a 7*3 re-use schedule, i.e., spatial channel re-use factor 7 and 3 sectors using different frequency channels in each cell
12
. In the 7*3 case the available frequency spectrum is divided into 21 channels or sub-channels labeled by f
1
, f
2
, . . . , f
21
. Frequencies f
1
, f
2
, f
3
are used in cell
12
A, frequencies f
4
, f
5
, f
6
are used in cell
12
B and so on. There is no frequency re-use within cluster
10
.
FIG. 1B
shows a system
14
built up of clusters
10
. As can be seen, the closest cell which re-uses the same frequency channel is at least three cells away. This separation ensures that sufficient attenuation is experienced by the signals emitted in the cells of one cluster before reaching cells of the next cluster re-using the same frequencies in its cells to not impair communications. The capacity of system
14
is dictated by the bandwidth of the channels and the carrier-to-interference (C/I) ratio. The sustainable re-use structure, therefore, decides the spectral efficiency of the system which is measured in the amount of information transmitted per unit frequency per cell, commonly measured in bps/Hz/cell.
Clearly, high spectral efficiency is a desirable system characteristic. By reducing CCI the C/I ratio can be improved and the spectral efficiency increased. Specifically, improved C/I ratio yields higher per link bit rates, enables more aggressive frequency re-use structures (closer spacing between cells re-using the same frequency channels) and increases the coverage of the system.
It is known in the communication art that receiving stations equipped with antenna arrays, rather than single antennas, can improve receiver performance. Antenna arrays can both reduce multipath fading of the desired signal and suppress interfering signals or CCI. Such arrays can consequently increase both the range and capacity of wireless systems. This is true for instance of wireless cellular telephone and other mobile systems.
In mobile systems, a variety of factors cause signal corruption. These include interference from other cellular users within or near a given cell. Another source of signal degradation is multipath fading, in which the received amplitude and phase of a source varies over time. The fading rate can reach as much as 200 Hz for a mobile user traveling at 60 mph at PCS frequencies of about 1.9 GHz. In such environments, the problem is to cleanly extract the signal of the user being tracked from the collection of received noise, CCI, and desired signal portions summed at the antennas of the array.
In Fixed Wireless Access (FWA) systems, e.g., where the receiver remains stationary, signal fading rate is less than in mobile systems. In this case, the channel coherence time or the time during which the channel estimate remains stable is longer since the receiver does not move. Still, over time, channel coherence will be lost in FWA systems as well.
Antenna arrays enable the system designer to increase the total received signal power, which makes the extraction of the desired signal easier. Signal recovery techniques using adaptive antenna arrays are described in detail, e.g., in the handbook of Theodore S. Rappaport,
Smart Antennas, Adaptive Arrays, Algorithms, & Wireless Position Location
; and Paulraj, A. J et al., “Space-Time Processing for Wireless Communications”, IEEE Signal Processing Magazine, November 1997, pp. 49-83.
Some of the techniques for increasing total received signal power use weighting factors to multiply the signal recovered at each antenna of the array prior to summing the weighted signals. Given that antenna arrays offer recognized, advantages including greater total received signal power, a key issue is the optimal calculation of the weighting factors used in the array. Different approaches to weight generation have been presented in the art.
If the channels of the desired and interfering signals are known, the weight generation technique that maximizes the signal-to-interference-plus-noise ratio (SINR), as well as minimizes the mean squared error (MMSE) between the output signal and the desired output signal, is the well-known Weiner-Hopf equation:
w=[R
xx
]
−1
r
xd
,
where r
xd
denotes the crosscorrelation of the received signal vector x with the desired signal, given by:
r
xd
=E[x*d],
where d is the desired signal, and R
xx
is the received signal correlation matrix, which in turn is defined as:
R
xx
=E[x*x
T
],
where the superscript * denotes complex conjugate and T denotes transpose.
Of course, this technique, also known as the beamforming approach, is only one of many. Other prior art techniques include joint detection of signal and interferers, successive interference canceling as well as space-time or space-frequency filtering and other techniques. More information about these techniques can be found in the above-cited references by Theodore Rappaport and Paulraj, A. J., as well as other publications.
Interference mitigation including CCI reduction for the purpose of increasing spectral efficiency of cellular wireless systems particularly adapted to a system using adaptive antenna arrays has been addressed in the prior art. For example, U.S. Pat. No. 5,819,168 to Golden et al. examines the problem of insufficient estimation of CCI and noise in communication channels which leads to an inability to suppress interference. In particular, Golden teaches to solve the problems associated with correct estimation of the R
xx
correlation matrix by an improved strategy for determining the weighting coefficients to modify R
xx
based on the ratio of interference to noise.
U.S. Pat. No. 5,933,768 to Sköld et al. addresses the problem of interference suppression with little knowledge of the interfering signal. This is done by detecting a training sequence or other portion of the interfering signal, estimating the interferer channel and using this information in a joint demodulation receiver. The training sequences come from a finite set of known training sequences. Furthermore, the training sequences of the interferers arrive at the receiver at undetermined times. The channel estimation is performed user by user and results in poor channel estimates of the interferers since their traini
Heath, Jr. Robert W.
Paulraj Arogyaswami J.
Sebastian Peroor K.
Tellado Jose
Chin Stephen
Iospan Wirless, Inc.
Liu Shuwang
Lumen Intellectual Property Services
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