# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 1 Introduction  
Wireless sensor network (WSN) is a special kind of ad-hoc network which can be applied in military, home industrial and other monitoring scenarios [1]. High throughput and low latency sensor networks ubiquitously provide connections around us. Most wireless sensor networks focus on narrowband physical layers such as IEEE 802.15.4 on 2.4 GHz. However, ultra-wideband (UWB) technology provides high data transmission rates, strong multipath resolution and high interference resistance ability which is more suitable for communication links or WSNs in specific scenarios such as multimedia transmission, dense multipath scenarios and high reliability communication. In recent years, WSN based on UWB has been explored in various scenarios including wireless monitoring sensor networks in underground mine tunnels [2], smart buildings [3], marine engine telemetry sensor networks [4], aircraft sensor networks [5] and wireless body sensor networks [6]. For star topology wireless sensor networks based on UWB, direct sequence UWB (DS-UWB) is a suitable technology to provide reliable multiple access for each sensor node. In a sense, the multiple access scheme of DS-UWB is similar to code division multiple access (CDMA) systems, as both of them use pseudorandom codes to distinguish different users. For such a DS-UWB system,the multiple access interference and noise are serious limiting conditions for improving its bit error ratio (BER) performance. Multiuser detection is an effective method to eliminate the bad effect of multiple access interference. The optimal multiuser detection (OMUD) method was first proposed by [PERSON] in 1986 [PERSON] (1986). With a bank of matched filters followed by a maximum likelihood sequence estimator, optimal multiuser detection achieves optimal BER and near-far effect resistance performances. However, optimal multiuser detection has a computational complexity that grows exponentially with the number of users. This feature makes optimal multiuser detection impractical to implement in actual communication systems. Consequently, many studies have been performed on suboptimal multiuser to obtain less complexity and good BER performance. Of them, linear multiuser detection has been widely studied as low-complexity integrated mathematical analysis ([PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018). Matched filter (MF), decorrelating (DEC) and minimum mean square error (MMSE) are three representative linear multiuser detection methods. Linear multiuser detectors form the output based on various linear filters. In recent years, considerable attention has been focused on blind multiuser detection, which requires the prior knowledge of only the signature sequence and timing of the desired user ([PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018). In ([PERSON] and [PERSON], 2018), a novel subspace tracking algorithm by orthonormalizing the eigenvectors using an approximation of Gram-Schmidt procedure was applied to blind multiuser detection. This scheme demonstrates the performance improvement for non-stationary cellular CDMA environment. Besides, suboptimal multiuser detection algorithms based on SINR Lower Bound ([PERSON] and [PERSON], 2018), box-constrained deregularization ([PERSON] and [PERSON], 2018), compressive sensing ([PERSON] and [PERSON], 2018), restricted search space ([PERSON] and [PERSON], 2018), decision-level data fusion technologies ([PERSON] and [PERSON], 2018) and opportunistic multiuser detection ([PERSON] and [PERSON], 2018; [PERSON], 2018) were analyzed in recent years. As for coded DS-UWB systems, iterative multiuser detection methods were also widely analyzed. Usually these suboptimal multiuser detection algorithms seek tradeoff between computation complexity and BER performance. On the basis of the Turbo decoding algorithm, various iterative multiuser detection algorithm were proposed ([PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018).