Dynamic spectrum management

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Dynamic spectrum management (DSM), also referred to as dynamic spectrum access (DSA), is a set of techniques based on theoretical concepts in network information theory and game theory that is being researched and developed to improve the performance of a communication network as a whole. [1] [2] The concept of DSM also draws principles from the fields of cross-layer optimization, artificial intelligence, machine learning etc. It has been recently made possible by the availability of software radio due to development of fast enough processors both at servers and at terminals. These are techniques for cooperative optimization. This can also be compared or related to optimization of one link in the network on the account of losing performance on many links negatively affected by this single optimization.

Contents

It is most commonly applied to optimize digital subscriber line (DSL) performance of a network. Another potential application of DSM is for cognitive radio.

Important and common principles of DSM include:

DSM in Digital Subscribers Loop

DSM can be achieved over ordinary copper phone lines' network by reducing or eliminating crosstalk, interference and near–far problem within a DSL network especially affecting the DSL phone lines that are close together in a binder. [3] [4]

The technique involves multiple methods:

DSM in Wireless Networks

An important application of dynamic spectrum access is in wireless networks. Spectrum, as the key resource for wireless communications, plays a major role in network key performance indicators like coverage, quality of service, energy efficiency, and reliability. Most wireless communication services are provided under a fixed spectrum allocation predefined by regulators and assigned by auctions to the operators. This spectrum allocation process is highly inefficient, leading to significant spectrum underutilization. Despite the increasing improvements in the spectral efficiency of wireless technologies, the demand for bandwidth exceeds the availability of spectrum for new communication services and networks. Paradoxically, several spectrum surveys demonstrate that the spatial and temporal use of the sub-3 GHz spectrum is less than 20% world wide [5] and less than 11% in rural areas [6] . In this context, Dynamic Spectrum Access (DSA) networks enable the opportunistic use of unused or underutilized spectrum in specific areas or at particular times. By leveraging licensed but unused spectrum or by better distributing spectrum according to the dynamic demand of services, higher spectrum efficiency can be achieved [7] .

Some dynamic spectrum access and management techniques and methods include:


See also

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References

  1. ↑ "Towards Dynamic Regulation of radio spectrum: technical dream or economic nightmare?" (PDF). Alcatel-lucent.com. Retrieved 2015-12-22.
  2. ↑ "Software Radio Enabling Dynamic Spectrum Management". Fcc.gov. Archived from the original on 2009-05-09. Retrieved 2015-12-22.
  3. ↑ "Dynamic Spectrum Management – A methodology for providing significantly higher broadband capacity to the users" (PDF). Telenor.com. Archived from the original (PDF) on 2010-01-08. Retrieved 2015-12-22.
  4. ↑ "Dynamic Spectrum Management for Digital Subscriber Lines" (PDF). Alcatel-lucent.com. Retrieved 2015-12-22.
  5. ↑ Martinez Alonso, Rodney; Plets, David; Deruyck, Margot; Martens, Luc; Guillen Nieto, Glauco; Joseph, Wout (2018-03-01). "TV White Space and LTE Network Optimization Toward Energy Efficiency in Suburban and Rural Scenarios". IEEE Transactions on Broadcasting. 64 (1): 164–171. doi:10.1109/TBC.2017.2731043. hdl:1854/LU-8556874. ISSN   0018-9316.
  6. ↑ Alonso, Rodney Martinez; Guerra, Arley Coto; Pupo, Ernesto Fontes; Plets, David; Nieto, Glauco Guillen; Martens, Luc; Joseph, Wout (2020-10-27). "Assessment of White Spaces Quality in Rural Areas: A large-scale spectrum survey". 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE. pp. 1–5. doi:10.1109/BMSB49480.2020.9379402. hdl:1854/LU-8733571. ISBN   978-1-7281-5784-9.
  7. ↑ Martinez Alonso, Rodney; Plets, David; Deruyck, Margot; Martens, Luc; Guillen Nieto, Glauco; Joseph, Wout (2020-05-09). "Dynamic Interference Optimization in Cognitive Radio Networks for Rural and Suburban Areas". Wireless Communications and Mobile Computing. 2020: 1–16. doi: 10.1155/2020/2850528 . ISSN   1530-8669.
  8. ↑ Martinez Alonso, Rodney; Plets, David; Deruyck, Margot; Martens, Luc; Guillen Nieto, Glauco; Joseph, Wout (2021-01-01). "Multi-objective optimization of cognitive radio networks". Computer Networks. 184: 107651. doi:10.1016/j.comnet.2020.107651. ISSN   1389-1286.
  9. ↑ Martinez Alonso, Rodney; Plets, David; Pollin, Sofie; Martens, Luc; Joseph, Wout (2023-05-28). "Outlier Detection and Spectrum Feature Extraction Based on Nearest-Neighbors Correlation and Random Forest Algorithm". ICC 2023 - IEEE International Conference on Communications. IEEE. pp. 4615–4620. doi:10.1109/ICC45041.2023.10279819. ISBN   978-1-5386-7462-8.
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