Adaptive differential interference cancellation multiuser detection method for DS-CDMA communications

Abstract

The multiuser detection is a signal processing tech- nique which enhances the performance and capacity in DS- CDMA technology. Since its complexity issue was too expensive to handle, there was focus on the suboptimal detectors. In this paper we propose a hybrid method called adaptive differential interference cancellation method (ADIC) which is a hybrid of the differential interference cancellation method and which is then combined in a adaptive way to determine a weight matrix which will map the outcome of the differential interference cancellation method to come out with a better performance. This paper also looks into the computational cost of different methods using the floating point operation (flops) which is a good estimation of the comparative computational cost if not the exact one. I. INTRODUCTION The conventional DS-CDMA match filter detector fails to combat these issues due three prominent drawbacks: unrealis- tic ucorrelated code, near-far problem (even with strict power control (1)), and tough multiple access interference (MAI). Many advanced signal processing approaches have been made to improve the performances of these kinds of detectors. In this paper we will study the different approaches that were made in this field and implement these methods to reach a conclusion about the detection methods. As the name of the topic suggests the focus of our work will be study and propose cost effective better performing multiuser detection method. We will explore Multiuser Detection Techniques in Synchronous CDMA environment. Multi-user detection deals with the development and ap- plication of joint demodulation and interference cancellation cancellation techniques for improved detection of a desired set of digital signals in the presence of multiple access interfer- ence, intersymbol interference and noise. Since veterbi forward dynamic algorithm (2) is used in case of Optimum multiuser detection (3) the complexity grows in with the increase in the number of users. Hence the complexity is immense which increases exponentially with the increase in the number of users. Looking ahead for lower complexity hence lesser cost of implementation, the paper proposes the use of sub-optimal linear detectors (4). The sub-optimal detector the paper next focuses on is Decorrelator type of Detector (5) which is optimal in least squares. This detector out performs the optimal detector in many respects. Then the Minimum Mean Square Error (MMSE) linear detector is proposed which tries to minimize the squared error from the required transformation. It is one of the most popular detectors in use due to its easiness to implement and its lower complexity. But then in the above detection methods the decisions made were based on certain linear transformation criterion and there are missing efforts to check the performance in between the detection process to improvise the transformation method better detection. So then the paper proposes Adaptive multiuser detection. Then the paper focuses on Interference cancellation methods which utilize the feedback to reduce Multiple Access Interference (MAI) in the detected bits. Under the Interference Cancellation umbrella the Successive Interference Cancellation (SIC) (6), (7) method is explored and simulated to observe the results. Then the next method which performs better than SIC and more commonly used in real life, called Parallel Interference Cancellation (PIC) (6), (8) is also implemented. Before going ahead to implement the final proposed detection method, this paper implements Differential Interference Cancellation (DIC) (9) method. And then this paper finally proposes an Inter- ference Cancellation method called the Adaptive Differential Interference Cancellation Multiuser Detection method which is cost effective in comparison to other Interference Cancellation methods as we will require lesser hardware (multipliers as in the SIC or Parallel Interference Cancellation (PIC)) for that matter. More ever since we are using adaptive method the desired BER can be further reduced. This proposed method will be discussed in detail in the following section.