# Position Accuracy and Distributed Beamforming Performance in WSNs: A Simulation Study
## 7 Conclusions  
This work investigated the performance of distributed beamforming in WSNs with a focus on the impact of position errors by developing a comprehensive simulation testbed. Real-world WSN deployments present numerous challenges, making software simulations essential for gaining insights into system behaviour and optimising implementation strategies. Our simulations provided a valuable tool for modelling individual components and complex processes, particularly those involving multidimensional arrays, allowing us to isolate and analyse the impact of key parameters.  
Our results underscore the critical importance of accurate beamforming for maximising received signal power. While conventional beamforming in uniformly spaced arrays inherently mitigates localisation challenges, distributed beamforming exhibits a greater sensitivity to sensor placement. We demonstrated that accurate sensor location information is fundamental for successful distributed beamforming, as imprecise implementations can lead to destructive interference and significant signal degradation.  
In conclusion, our simulation platform effectively demonstrates the functionality of distributed beamforming in WSNs and confirms that the number of sensors, position accuracy, and receiver localisation are critical factors for maintaining a desired signal quality. These findings highlight the need for localisation algorithms that minimise position error variance to achieve robust signal quality. Ultimately, this work contributes to optimising WSN performance and enabling reliable deployment in challenging environments.  
**Author Contributions:** Conceptualization, J.C., P.G., M.G., V.S. and R.D.; methodology, J.C., P.G., M.G., V.S. and R.D.; software, J.C. and P.G.; validation, J.C., P.G., M.G., V.S. and R.D.; formal analysis, J.C., P.G., M.G., V.S. and R.D.; investigation, J.C., P.G., M.G., V.S. and R.D.; resources, M.G., V.S. and R.D.; data curation, J.C. and P.G.; writing--original draft preparation, J.C. and P.G.; writing--review and editing, J.C., P.G., M.G., V.S. and R.D.; visualization, J.C., P.G., M.G., V.S. and R.D.; supervision, M.G., V.S. and R.D.; project administration, M.G., V.S. and R.D.; funding acquisition, J.C., P.G., M.G., V.S. and R.D. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Cramer–Rao Lower Bound for different SNR values.  
**Funding:** This work was supported by the European Regional Development Fund (FEDER) through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 framework, Regional OP Centro (POCI-01-0145-FEDER030588), the Regional Operational Program of Lisbon (Lisboa-01-0145-FEDER-030588), and by FCT/MEC, through national funds, under the projects UIDB/50008/2020 ([[https://doi.org/10.54499/UIDB/50008/2020](https://doi.org/10.54499/UIDB/50008/2020)]([https://doi.org/10.54499/UIDB/50008/2020](https://doi.org/10.54499/UIDB/50008/2020))) and UIDP/50008/2020, and within the MiFuture project, which has received funding from the European Union's Horizon Europe (HE) Marie Sklodowska-Curie Actions Doctoral Networks (MiFuture HORIZON-MSCA-2022-DN-01 and YAHYA/6G HORIZON-MSCA-2022-PF-01) under Grant Agreement number 101119643.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.