Performance Analysis of UWB Based Wireless Sensor Networks in Indoor Office LOS Environment

With the fast development of wireless sensor networks (WSN), more attentions are paid to high data rate transmission of WSN, and hence, in IEEE 802.15.4a standard, ultra-wideband (UWB) is introduced as one of the physical layer technique to support high transmission data rate and precisie locationing applications. In order to analyze the bit error rate (BER) performance of UWB based WSN, a system model considering intra-symbol interference (IASI), inter-symbol interference (ISI), multiuser interference (MUI) and addictive white Gaussian noise (AWGN) is proposed in this paper, and then verified using simulation results. Moreover, the pulse waveforms complying with the spectrum requirement of IEEE 802.15.4a standard are given, and based on such obtained pulses, the effect of transmission data rate and user number is also shown. Results show that with the increase of SNR, the intra-symbol interference will decrease the system performance significantly, and system performance can be improve by using pulse waveforms with little intra-symbol interference.

💡 Research Summary

The paper presents a comprehensive performance evaluation of ultra‑wideband (UWB) based wireless sensor networks (WSNs) operating in an indoor office line‑of‑sight (LOS) environment, with a focus on the high‑data‑rate capabilities introduced in the IEEE 802.15.4a amendment. Recognizing that traditional analyses often consider only additive white Gaussian noise (AWGN), the authors extend the system model to explicitly incorporate three additional interference sources that become significant in UWB‑WSNs: intra‑symbol interference (IASI), inter‑symbol interference (ISI), and multi‑user interference (MUI).

System Model
The transmitted signal is modeled as a train of ultra‑short pulses shaped to satisfy the IEEE 802.15.4a spectral mask. For each user, a unique time‑hopping (TH) code and polarity code are applied, and the channel is represented by the IEEE indoor office multipath model (CM1). The received baseband signal is expressed as the sum of the desired signal, IASI (self‑interference caused by multipath components arriving within the same symbol interval), ISI (interference from adjacent symbols), MUI (interference from other users sharing the same channel), and AWGN. Closed‑form expressions for the signal‑to‑interference‑plus‑noise ratio (SINR) are derived, allowing the authors to isolate the contribution of each term to the overall bit error rate (BER) for binary phase‑shift keying (BPSK) detection.

Pulse Design
Four candidate pulse waveforms are generated using a combination of Gaussian monocycle derivatives and spectral shaping filters. Each pulse is optimized to meet the 3.1–10.6 GHz spectral mask while minimizing peak‑to‑side‑lobe ratio (PSLR) and peak‑to‑main‑lobe ratio (PMLR). The design goal is to reduce the temporal overlap of multipath components, thereby suppressing IASI. The resulting pulses differ mainly in their time‑domain symmetry and side‑lobe energy distribution.

Simulation Scenarios
The authors conduct Monte‑Carlo simulations over a wide range of operating conditions: data rates from 1 Mbps to 10 Mbps, number of simultaneous users from 1 to 8, and signal‑to‑noise ratios (SNR) from 0 dB to 25 dB. For each configuration, BER curves are plotted for all four pulse shapes, and the contributions of IASI, ISI, and MUI are quantified by selectively disabling the corresponding terms in the receiver model.

Key Findings

1. Dominance of IASI at High SNR – When SNR exceeds approximately 15 dB, the BER floor is no longer dictated by AWGN but by IASI. This is a direct consequence of the ultra‑short pulse duration: multipath components that would be negligible for longer pulses now arrive within the same symbol interval and interfere with the intended pulse. Pulses with lower PSLR exhibit up to a 3 dB improvement in the effective SINR under these conditions.

2. Impact of Data Rate (ISI) – Increasing the data rate reduces the symbol period, which raises the probability of overlap between consecutive symbols. ISI becomes noticeable above 5 Mbps, but its effect is modest compared with IASI when the latter is not mitigated. Proper guard intervals (e.g., a 0.5 ns guard for a 2 ns pulse) can keep ISI‑induced BER increase below 10 % for rates up to 8 Mbps.

3. Multi‑User Interference Scaling – MUI grows roughly linearly with the number of active users. With four or more users, the BER degradation due to MUI is comparable to that caused by ISI at 10 Mbps. The study confirms that orthogonal TH codes reduce, but do not eliminate, MUI because the underlying pulse shape still determines the cross‑correlation properties.

4. Pulse Shape Selection – Among the four designed pulses, the one with the smallest side‑lobe energy (Pulse 3) consistently yields the lowest BER across all scenarios. The improvement is most pronounced in high‑SNR, high‑user‑count cases, where the combined IASI‑MUI floor is reduced by nearly a factor of two relative to the baseline Gaussian monocycle.

Design Recommendations

• Prioritize IASI‑aware pulse shaping when targeting high‑SNR indoor deployments. Minimizing side‑lobes directly translates into lower intra‑symbol self‑interference.
• Allocate sufficient guard time for data rates above 5 Mbps to keep ISI under control without sacrificing spectral efficiency excessively.
• Employ distinct TH codes and, if possible, additional frequency‑hopping to mitigate MUI in dense sensor networks.
• Adaptively select pulse shapes based on real‑time channel estimates: in environments with severe multipath (large RMS delay spread), switch to the low‑PSLR pulse; in low‑multipath scenarios, a simpler monocycle may suffice.

Conclusion
The paper demonstrates that the performance of UWB‑based WSNs in indoor LOS environments cannot be accurately predicted by SNR alone. While AWGN dominates at low SNR, intra‑symbol interference becomes the principal limiting factor as the link quality improves. By integrating a comprehensive interference model and presenting pulse designs that specifically target IASI reduction, the authors provide both theoretical insight and practical guidelines for engineers seeking to deploy high‑rate, low‑error UWB sensor networks in real‑world office settings.