This invention relates in general to the field of communications and more specifically to antenna interference cancellation.
Single-antenna interference cancellation (SAIC) has been become popular in Global System for Mobile communication (GSM) standardization efforts due to its potential in providing significant capacity increase for high frequency reuse GSM networks. In the United States, a frequency reuse factor of one-to-one is typically used in GSM networks. Such GSM networks can be severely limited by co-channel interference (CCI) issues. Compared to GSM systems, Enhanced Data rates for GSM Evolution (EDGE) systems employ 8 Phase Shift Keying (8PSK) modulation in addition to Gaussian Minimum Shift Keying (GMSK) modulation. Therefore, the SAIC algorithm must be adapted in response to the change in the data modulation of the desired user, as well as that of the dominant interferer for some algorithms.
One non-linear multi-user SAIC scheme for EDGE is the use of joint detection. However, this approach is highly complex even after employing reduced state sequence estimation (RSSE) techniques. Hence, it is hard to incorporate this technique in any current state-of-the-art digital signal processor (DSP). Other schemes such as successive/serial interference cancellation do not work well with 8PSK signals, since the 8PSK tentative decisions tend to be unreliable. Another drawback of any multi-user SAIC scheme for EDGE is that it requires detection of the dominant interferer, which is more complex and sensitive for systems that employ more than one modulation scheme such as EDGE systems. Given this, a need exists in the art for a system and method that can provide for interference cancellation of a signal such as an 8PSK signal for EDGE systems.
The features of the present invention, which are believed to be novel, are set forth with particularity in the appended claims. The invention may best be understood by reference to the following description, taken in conjunction with the accompanying drawings, in the several figures of which like reference numerals identify like elements, and in which:
In accordance with an embodiment of the invention, a low-complexity linear blind capable SAIC receiver algorithm and receiver for EDGE systems is disclosed that provides significant amount of gain in different conditions. In one embodiment, a single antenna is used at the receiver with “virtual antennas” being provided for interference suppression. The “virtual antennas” are provided for GMSK signals by exploiting the spectral redundancy property of GMSK-modulated signals for Inphase and Quadrature signals. This redundancy can not be exploited for 8PSK signals since the 8PSK signal is complex with the Inphase and Quadrature signals carrying different information. For 8PSK signals as well as for GMSK signals, oversampling is used. Oversampling is beneficial for an interference suppression receiver since the additional degrees of freedom contain some new information on the interference. In still another embodiment, Q-times oversampling is performed using baud rate sampling followed by a Q-times interpolator. In other embodiments, one can choose not to oversample, in this case Q=1.
Referring now to
The appropriate vector signal (rm) 115, 116, 117 is provided to an optional space-time interference suppression circuit 122 which performs interference suppression and provides a suppressed signal (Sm) to a spatial whitening circuit 124. The vector signal (rm) is also sent to a channel estimation circuit 134 in order to determine a channel estimate h(z). The channel estimate is then converted in block 136 using a predetermined training sequence code (TSC) or decision feedback (DF) provided at 138. A summation circuit provides as an output an estimate of the interference component v(z) which is sent to the first stage filter computation block 126 where the first stage F(z) of the filter is determined. Block 128 then calculates the residual interference component e(z). The residual interference component e(z) is then used by block 130 which determines a spatial whitener W which is used by the spatial whitening circuit 124. The space-time interference suppression and spatial whitening circuit block 150 receives the required modulation information to enable the proper real (2Q-dimensional) or complex (Q-dimensional) processing via line 120. More details of the receiver and its operation are provided below.
A GMSK-modulated signal allows the following linear approximation:
where T is one symbol in duration and C0(t) is the GMSK waveform of duration 4 T. Likewise, the 8PSK modulation per the EDGE standard employs the C0(t) waveform for partial response signaling. The 8PSK-modulated signal has the following form:
The baseband received signal can be written as:
where {tilde over (h)}(t)is the overall channel impulse response including C0(t) with delay spread of LT, ñ(t)is the thermal noise, {tilde over (v)}(t) is the total interference plus noise, and φ=π/2 for GMSK and 3π/8 for 8PSK. The continuous time received signal is then sampled at Q times the baud rate. Defining {tilde over (r)}m yields:
Then it can be shown that:
This shows that Q-times oversampling provides an additional (Q−1) degrees of freedom, acting as a set of virtual correlated antennas.
When a GMSK signal is detected by modulation detector 104, derotation by φ=π/2 and Inphase-Quadrature component extraction is performed by 112, 114 in
This results in a single-input 2Q-output real-valued channel. Essentially, spectral redundancy from the fact that ak is a real-valued symbol is exploited.
When an 8PSK signal is detected by the modulation detector 104, derotation by φ=3π/8 results in the following:
which gives a single-input Q-output complex-valued channel.
The oversampled received vector signal rm is then processed by a space-time interference suppression matrix filter as follows:
where Gn (εR2Q2Q for GMSK and εCQxQ for 8PSK) is the n-th tap of the matrix filter. In the z-domain, y(z)=G(z)r(z)=G(z)(h(z)α(z)+v(z)). The processed 2Q-vector signal ym 140 serves as the input of the desired user equalizer such as a MLSE, DFE or other type of equalizer. The effective ISI channel for the equalizer is now equal to heq(z)=G(z)h(z).
The above proposed algorithm is not limited to co-channel interference suppression. The algorithm can also suppress adjacent channel interference. The algorithm can also be extended to the case of multiple antennas at the receiver. With P>1 antennas at the receiver, the received signal vectors are stacked from the P receive antennas into one vector with P times the length. This results in a 2PQ-dimensional real-valued rm in equation (4) for GMSK, which is associated with a single-input 2PQ-output real-valued channel. For 8PSK, this results in PQ-dimensional complex-valued rm in equation (5) for 8PSK, which is associated with single-input PQ-output complex-valued channel. The design technique is the same as that for single-antenna receiver (P=1). The difference is simply in dimensionality as a result of having additional receive antennas.
Receiver Filter Design
In one embodiment a space-time matrix filer
is decomposed into two parts as follows:
The filter design in accordance with one embodiment is designed with the following criteria:
The first stage is optional, since N can be set to 0. Setting N=0 results in better performance in some scenarios. F(z) increases the effective channel constraint length before equalization by N. The spatial whitener W 124, does not affect the effective channel memory.
It can be assumed that only the channel estimate of the desired user is available via some kind of channel estimation algorithm, for example, using a single-user correlator, a single-user least square technique or a joint least square technique. The algorithm is said to be blind to the interference parameters. Given the received signal r(z) and the desired user channel estimate {tilde over (h)}(z), the interference component v(z) can be estimated as follows:
v(z)=r(z)−ĥ(z){circumflex over (α)}(z) (8)
where {circumflex over (α)}(z) is an estimate of the desired user data. This can be obtained as follows:
The interference estimate v(z) is then used to compute F(z) in block 126 according to an optimization criterion such as:
where e(z)=F(z)v(z) is the residual interference after interference suppression and B is the index set depending on where v(z) is computed within a burst. The optimization problem in equation (9) can be viewed as a linear prediction problem. The solution can be obtained using any adaptive filtering algorithm or analytically as follows:
Then, the solution to equation (9) is given as:
From fopt;
can be obtained.
The spatial whitening transformation W can be obtained from the residual interference estimate e(z)=Fopt(z){circumflex over (v)}(z). First, an estimate of the spatial covariance matrix is obtained as follows:
which is then used for deriving the spatial whitening transformation:
W=Re−1/2 (12)
It should be noted that when N=0, e(z)=v(z). For asynchronous systems where the interference may be present only within a part of a burst, some decision-directed algorithm can be used to adapt the matrix filter G(z) to changes in the interference structure. The algorithm can start from the mid-amble since the desired user training sequence code (TSC) is known and then adapt from the center to the beginning and end of each burst. In this case, an efficient algorithm to update matrix inverses can also be used. The decision-directed adaptive algorithm can be based on a host of standard adaptive filtering algorithms such as NLMS and RLS (Kalman filtering).
Taking the square-root of a matrix is needed to compute the spatial whitening transformation (see equation 12). This may increase the receiver complexity significantly since it involves computing a symmetric matrix factorization. However, when an equalizer that uses a matched filtering as a front-end is used, the square-root operation can be circumvented. In this case, the equalizer requires only the channel correlation estimates. The channel correlation polynomial is given as:
p(z)=||WF(z)h(z)||2=h(z)r F(z)T Re−1 F(z)h(z) (13)
which does not require computing the square-root of Re−1. Such simplification can also be done for MLSE equalizer when a front-end matched filter is used. In this case, the branch metric definition needs to be modified to take into account the noise correlation after matched filtering. The effective channel for the equalizer is the channel correlation polynomial in equation (13).
Referring to
In
The Configuration 3 results are given in
The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Numerous variations and modifications will become apparent to those skilled in the art. It is intended that the following claims be interpreted to embrace all such variations and modifications.
This application claims priority to U.S. Provisional Application No. 60/563,742 filed Apr. 19, 2004, and entitled “Linear Single Antenna Interference Cancellation Receiver for Edge Systems,” by Eko N. Onggosanusi et al, incorporated herein by reference.
Number | Date | Country | |
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60563742 | Apr 2004 | US |