Constructive Interference Optimization for Data-Aided Precoding in MultiUser MISO Systems
Abstract: Unlike the general concept of eliminating or avoiding inter-user interference in the downlink multi-user multiple input single-output (MISO) system, the data-aided precoding scheme attempts to exploit the constructive interference at symbol level. Positive interference can be constructed to enhance the received signal gain by predicting the phase and magnitude of inter-user interference. In this paper, we formulate a constructive interference optimization problem that minimizes a sum of minimum mean square error (MMSE) for all users while ensuring the minimum required constructive interference gain with a fixed total power constraint. As opposed to the existing scheme, such as constructive zero-forcing precoding or minimum power precoding subject to the strict phase conservation for phase shift keying (PSK), our proposed scheme exploits a full range of relaxation for constructive interference region, while ensuring the link performance by minimizing the sum of mean-square error (MSE) for all users. In fact, the relaxed requirements lead to more degrees of freedom for improving the cumulative constructive interference gain (CCIG) under the varying channel conditions. Furthermore, our MMSE-based optimization approach allows for a semi-closed-
form optimal solution to the data-aided precoding scheme, providing a more CCIG without incurring unacceptable complexity than other state-of-the-art schemes. Existing system: As opposed to the existing scheme, such as constructive zero-forcing precoding or minimum power precoding subject to the strict phase conservation for PSK, our proposed MMSECIO scheme relaxes the constraint of constructive interference region that is specified in terms of target cumulative constructive interference gain (CCIG), while ensuring the link performance by minimizing the sum of meansquare error (MSE) for all users. From the relaxed requirements, minimizing MSE metric allows for improving the CCIG, which can enhance each user’s SINR under the varying channel conditions. Because minimizing a sum of MSE is closely connected with other important performance measures such as symbol error rate (SER) and system capacity, a MMSE problem plays an essential role in enhancement of the link performance . Proposed system: Specifically, it can greatly improve the detection performance of phase-shiftkeying (PSK)-modulated data by designing the phase and amplitude of inter-user interference, which can be estimated at the transmitter when both data and CSI are simultaneously exploited on a symbol by- symbol basis. As the symbol-level precoding scheme leverages the interference to improve the overall performance, it has been known to outperform the block-level scheme in the interference-limited environment. For example, modified forms of zero-forcing beam forming (ZFBF) are proposed as a data-aided precoding scheme in, where they used the phase information of inter-user interference to obtain constructive interference and showed that it achieved better performance than ZFBF. Furthermore, proposed a symbol-level precoding scheme that minimizes the transmit power while satisfying the per-user constraint of constructive interference gain by simultaneously designing the phase and magnitude of inter-user interference. Advantages: Specifically, it can greatly improve the detection performance of phase-shiftkeying (PSK)-modulated data by designing the phase and amplitude of inter-user
interference, which can be estimated at the transmitter when both data and CSI are simultaneously exploited on a symbol by- symbol basis. As the symbol-level precoding scheme leverages the interference to improve the overall performance, it has been known to outperform the block-level scheme in the interference-limited environment. For example, modified forms of zero-forcing beam forming (ZFBF) are proposed as a data-aided precoding scheme in, where they used the phase information of inter-user interference to obtain constructive interference and showed that it achieved better performance than ZFBF. Disadvantages: In general, however, solving any form of optimization problems to enhance the link performance for data-aided precoding, e.g., weighted sum-rate maximization, sum-rate maximization, or the worst-constructive gain maximization problem, is not straightforward, simply because they are non-convex problems, i.e., cannot be solved in any closed form. In this paper, we consider a new optimization framework for symbol-level link enhancement subject to the minimum required constructive interference gain, while allowing for a semi-closed-form solution. Modules: Channel state information: IN multi-user multi-input multi-output (MU-MIMO) networks, the spatial degrees of freedom offered by multiple antennas can be advantageously exploited to enhance the system capacity, i.e., scheduling multiple users to share the spatial channel. The general concept of precoding is to design the transmitted signal for delivering the desired data block efficiently by exploiting the multi-antenna spatial degrees of freedom and channel state information (CSI) while limiting the interuser interference. In recent years, symbol-by symbol data-aided precoding has been studied to exploit the constructive multi-user interference that pushes the detected constellation point deeper into detection region at the receiver side. More specifically, it can greatly improve the detection performance of phase-shift-keying (PSK)-modulated data by designing the phase and amplitude of inter-user interference, which can be estimated at the transmitter when both data and CSI are
simultaneously exploited on a symbol by- symbol basis. As the symbol-level precoding scheme leverages the interference to improve the overall performance, it has been known to outperform the block-level scheme in the interference-limited environment. Zero – forcing beam form: Modified forms of zero-forcing beam forming (ZFBF) are proposed as a data-aided precoding scheme in, where they used the phase information of inter-user interference to obtain constructive interference and showed that it achieved better performance than ZFBF. Furthermore, proposed a symbol-level precoding scheme that minimizes the transmit power while satisfying the per-user constraint of constructive interference gain by simultaneously designing the phase and magnitude of inter-user interference. In general, however, solving any form of optimization problems to enhance the link performance for data-aided precoding. Constructive interference optimization: In general, however, solving any form of optimization problems to enhance the link performance for data-aided precoding, e.g., weighted sum-rate maximization, sum-rate maximization, or the worst-constructive gain maximization problem, is not straightforward, simply because they are non-convex problems, cannot be solved in any closed form. In this paper, we consider a new optimization framework for symbol-level link enhancement subject to the minimum required constructive interference gain, while allowing for a semi-closed-form solution. Our trick is to formulate a constructive interference optimization (CIO) problem that minimizes a sum of minimum mean square error (MMSE) for all users while ensuring the minimum required constructive interference gain with a fixed total power constraint. Symbol error rate: As opposed to the existing scheme, such as constructive zero-forcing precoding or minimum power precoding subject to the strict phase conservation for PSK, our proposed MMSECIO scheme relaxes the constraint of constructive interference region that is specified in terms of target cumulative constructive interference gain (CCIG), while ensuring the link performance by minimizing the sum of mean-
square error (MSE) for all users. From the relaxed requirements, minimizing MSE metric allows for improving the CCIG, which can enhance each user’s SINR under the varying channel conditions. Because minimizing a sum of MSE is closely connected with other important performance measures such as symbol error rate (SER) and system capacity, a MMSE problem plays an essential role in enhancement of the link performance. Therefore, we formulate a symbol-level MMSE problem.