Ultrawideband (UWB) technology is attractive for its multiple-access (MA) applications in wireless communication systems owing to its high ratio of the transmitted signal bandwidth to information signal bandwidth (or pulse repetition frequency) [1]. Similarly, power can spread, because of its information symbols transmitted by short pulses, over the wide frequency band [2]. There are mainly two standard schemes formulated by IEEE 802.15 Task Group 3a, i.e., the multi-band-based orthogonal frequency division multiplexing (MB-OFDM) and single-band-based direct-sequence UWB (DS-UWB) [3]. The former is a carrier-based system that divides the wide bandwidth of UWB into many sub-bands, while the latter is a baseband system modulating its input information symbols with nanosecond pulses, which is different from conventional code division multiple access (CDMA) systems [1, 4, 5]. Compared with MB-OFDM, DS-UWB scheme has many advantages, which stem from its UWB nature, such as low peak-to-average power ratio, wide bandwidth, good information hidden ability, and less sensitivity to multipath fading [6, 7]. Our focus is thus on investigating the detection algorithms in multiuser DS-UWB communication systems.

Actually, the idea of UWB MA systems dates back to the original proposal put forward by Scholtz [8], and with subsequent analyses in [9–12]. However, as in conventional CDMA systems, these proposed UWB MA systems also suffer from the multiple-access interference (MAI) problem, which severely restricts their performance and system capacity. This is due to the crude assumption that the MAI can be modeled as a zero-mean Gaussian random variable (called “Gaussian approximation”) [13] for the conventional single-user matched receiver. Moreover, MAI even causes the near–far effect (NFE) [14], the case that the user with lower received signal power will be swamped by users with higher power. In order to solve these problems, multiuser detection (MUD) technology that can eliminate or weaken the negative effects of MAI was studied in [15–27]. Among them, the optimum multiuser detection (OMD), proposed for CDMA systems by Verdu [15], has the optimal BER performance [16] and the perfect NFE resistant ability [17]. But its computational complexity growing exponentially with the number of active users makes it impractical to use [18]. Yoon and Kohno [19] introduced this OMD algorithm to the UWB MA system; its high computational complexity problem is yet to be solved.

In recent years, many different sub-optimal MUD algorithms have been studied in literatures. In [20], a multiuser frequency-domain (FD) turbo detector was proposed that combines FD turbo equalization schemes with soft interference cancelation, but its BER performance is unsatisfactory. A blind multiuser detector using support vector machine on a chaos-based code CDMA systems was presented in [21], which does not require the knowledge of spreading codes of other users at the cost of training procedure. In [22], a low-complexity approximate SISO multiuser detector based on soft interference cancellation and linear minimum mean square error (MMSE) filtering was developed, but the performance of this detector is unfavorable at low SNR. As the swarm intelligence is one of the latest methods in the field of signal processing [23] (especially for combinatorial optimization problems [24]), several swarm-intelligence-based MUD algorithms have been considered in [25–27]. However, the tradeoff problem between computational complexity and BER performance still exists.

To solve these issues, we investigate a complexity-performance-balanced multiuser detector based on the artificial fish swarm algorithm (AFSA-MUD) for DS-UWB systems. As a kind of swarm intelligence methods, AFSA is selected here for its significant ability to search for the global optimal value and to adapt its searching space automatically [28, 29]. And its basic motivation is to find the global optimum by simulating the fish’s behaviors, such as preying, swarming, and searching.

In this proposed AFSA-MUD algorithm, a simplified Euclidean solution searching space is constructed by the use of the solutions of sub-optimal multiuser detectors, which are MMSE detector, decorrelating (DEC) detector, and successive interference cancellation (SIC) detector. Specifically, the center of this space is the result judged in terms of the average value of all these sub-optimal solutions, while its radius is defined as the maximum distance between this center and these sub-optimal solutions. Then, AFSA is applied in this simplified solution space and these sub-optimal solutions are considered as the initial states for the artificial fishes (AFs). Simulation results show that the BER performance and the NFE resistance capability of this proposed algorithm are comparable to those of OMD, and significantly better than those of matched filter (MF), SIC, DEC, and MMSE detectors. Besides, its computational complexity is much lower than that of OMD, indicating a better efficiency.

The remainder of this article is organized as follows. In Section 2, the general multiuser DS-UWB system and some typical MUD algorithms are described, including OMD, MMSE, DEC, and SIC. And in Sections 3 and 4, the basic principles of AFSA and the proposed AFSA-MUD algorithm are discussed, respectively. In Section 5, simulation experiments that compare the performance of different MUD algorithms are made, followed by conclusions given in Section 6.