An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna Quantization
The widespread deployment of small cells (SCs) plays a crucial role in enhancing system capacity, coverage, and quality of service (QoS) for smart applications. However, due to the dynamic nature of user demands and the limited resources available, SCs cannot support large quantization codebooks, wh...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11039791/ |
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| author | Sanjeeb Shrestha Xiaoying Kong Paul Kwan Xiaojing Huang |
| author_facet | Sanjeeb Shrestha Xiaoying Kong Paul Kwan Xiaojing Huang |
| author_sort | Sanjeeb Shrestha |
| collection | DOAJ |
| description | The widespread deployment of small cells (SCs) plays a crucial role in enhancing system capacity, coverage, and quality of service (QoS) for smart applications. However, due to the dynamic nature of user demands and the limited resources available, SCs cannot support large quantization codebooks, which are typically more suitable for macro cells (MCs) in finite rate feedback (FRF)-based multiple input single output (MISO) systems. In this paper, we propose an adaptive quantization approach for SCs that adjusts the codebook size based on the number of receiver antennas. Additionally, we address the issue of code quantization error (CQE), which arises when two distinct channels are quantized using the same code, as well as the average system error (AvgSysErr), which can increase due to elevated CQE. Our analysis shows that for SCs to achieve convergence of AvgSysErr with FRF-based MISO systems, the probability of non-unique codes in the quantization codebook must be less than <inline-formula> <tex-math notation="LaTeX">$\frac {1}{N}$ </tex-math></inline-formula>, where N is the number of antennas at the transmitter. Similarly, the lower bound for the non-unique code probability must be less than or equal to <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> represents the difference between the non-unique code probabilities of <inline-formula> <tex-math notation="LaTeX">$\frac {1}{N_{1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\frac {1}{N_{2}}$ </tex-math></inline-formula>, given <inline-formula> <tex-math notation="LaTeX">$\frac {1}{N_{1}}\gt \frac {1}{N_{2}}$ </tex-math></inline-formula> (where <inline-formula> <tex-math notation="LaTeX">$N_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N_{2}$ </tex-math></inline-formula> denote the number of antennas at the transmitter). |
| format | Article |
| id | doaj-art-3faa17f50d8341a496d536712b9e44b7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3faa17f50d8341a496d536712b9e44b72025-08-20T03:29:34ZengIEEEIEEE Access2169-35362025-01-011310701010702110.1109/ACCESS.2025.358069811039791An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna QuantizationSanjeeb Shrestha0https://orcid.org/0000-0001-6634-7427Xiaoying Kong1Paul Kwan2https://orcid.org/0000-0002-4959-5274Xiaojing Huang3https://orcid.org/0000-0001-6869-5732Melbourne Institute of Technology, Sydney, NSW, AustraliaMelbourne Institute of Technology, Sydney, NSW, AustraliaCollege of Information and Communications Technology (ICT), Central Queensland University, Brisbane, QLD, AustraliaSchool of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, AustraliaThe widespread deployment of small cells (SCs) plays a crucial role in enhancing system capacity, coverage, and quality of service (QoS) for smart applications. However, due to the dynamic nature of user demands and the limited resources available, SCs cannot support large quantization codebooks, which are typically more suitable for macro cells (MCs) in finite rate feedback (FRF)-based multiple input single output (MISO) systems. In this paper, we propose an adaptive quantization approach for SCs that adjusts the codebook size based on the number of receiver antennas. Additionally, we address the issue of code quantization error (CQE), which arises when two distinct channels are quantized using the same code, as well as the average system error (AvgSysErr), which can increase due to elevated CQE. Our analysis shows that for SCs to achieve convergence of AvgSysErr with FRF-based MISO systems, the probability of non-unique codes in the quantization codebook must be less than <inline-formula> <tex-math notation="LaTeX">$\frac {1}{N}$ </tex-math></inline-formula>, where N is the number of antennas at the transmitter. Similarly, the lower bound for the non-unique code probability must be less than or equal to <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> represents the difference between the non-unique code probabilities of <inline-formula> <tex-math notation="LaTeX">$\frac {1}{N_{1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\frac {1}{N_{2}}$ </tex-math></inline-formula>, given <inline-formula> <tex-math notation="LaTeX">$\frac {1}{N_{1}}\gt \frac {1}{N_{2}}$ </tex-math></inline-formula> (where <inline-formula> <tex-math notation="LaTeX">$N_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N_{2}$ </tex-math></inline-formula> denote the number of antennas at the transmitter).https://ieeexplore.ieee.org/document/11039791/Small cellsadaptive quantizationcode quantization errorfinite rate feedbackaverage system error |
| spellingShingle | Sanjeeb Shrestha Xiaoying Kong Paul Kwan Xiaojing Huang An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna Quantization IEEE Access Small cells adaptive quantization code quantization error finite rate feedback average system error |
| title | An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna Quantization |
| title_full | An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna Quantization |
| title_fullStr | An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna Quantization |
| title_full_unstemmed | An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna Quantization |
| title_short | An Adaptive Approach in Channel Quantization for Small Cells Based on Per-Receiver Antenna Quantization |
| title_sort | adaptive approach in channel quantization for small cells based on per receiver antenna quantization |
| topic | Small cells adaptive quantization code quantization error finite rate feedback average system error |
| url | https://ieeexplore.ieee.org/document/11039791/ |
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