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Effective SINR Mapping for PHY Abstraction in Communication Systems

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10 diapositives · 232 Views · Jul 29, 2014

This paper by Yakun Sun et al. discusses effective SINR mapping techniques for physical (PHY) layer abstraction in communication systems. It outlines a process for calculating receiver-output SINR across frequency tones and OFDM symbols, incorporating factors like large scale fading and channel impulse response. The paper also compares ideal versus practical channel estimation methods and highlights the dependency of SINR calculations on receiver types and beamforming techniques. Additionally, it provides insights into the impacts of channel estimation errors and the modeling of transmitter-receiver interactions.

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  1. About SINR conversion for PHY Abstraction Authors: Date: 2013-11-11 Yakun Sun, et. Al.

  2. Introduction • Effective SINR mapping is proposed for PHY abstraction [1, 2, 3]. • The effective SINR mapping, no matter what specific mapping function is adopted, operates in the same procedure. • Starting by calculating receiver-output SINR for each tone. • We discuss how to calculate receiver-output SINR. Yakun Sun, et. Al.

  3. PHY Layer Model for System Simulation • Tx MAC layer informs PHY layer the number of bits and MCS. • Tx PHY layer does not encode anything  a virtual PHY packet. • Channel includes (1) large scale fading, and (2) instantaneous channel impulse response (CIR) • Generated for performance evaluation. • Multiple virtual PHY layers may contribute to the received “signals”. • Each with a CIR. • Rx calculates channel freq response. • Rx MIMO detector only calculates SINR but does not process any signals. • Rx decoder takes SINRs across frequency tones and OFDM symbols, and predict PER for this packet. • A random number is generated and compare with PER Yakun Sun, et. Al.

  4. Frequency Domain Received Power • Frequency domain equalization is done and the PER depends on the equalizer performance. • Frequency domain received signal power is calculated on top of the channel frequency response. • A scaling factor to compensate the guard tones. • A simple example (more factors can be added, such as cable loss…): Yakun Sun, et. Al.

  5. Frequency Domain Received Signal Model • The (virtually) received signal at the nth tone, lthOFDM symbols is • Instantaneous channel fading is separated from the received signal power. • The power of channel frequency response |H(f)|2 is normalized, either per packet or long-term normalized. • The received noise is modeled as AWGN with variance σ2. Yakun Sun, et. Al.

  6. SINR Calculation • Based on the receiver assumed, the equalizer output SINR can be calculated for each spatial stream. • SINR is precoding (beamforming) dependent. • Per-tone beamforming changes the effective channel fading rather than a constant/static receive signal power boost. • The beamforming method has to be aligned or specified in simulation. • SINR is receiver dependent. • Typically, linear MMSE (with or without interference whitening) or MRC for single-stream transmission is assumed. • “SINR” does not exist for ML detector, and equivalent SINR for ML has been proposed in literatures but with great computational complexity. • For example, if no interference is present (K=0), and a rank-1 transmission (x0 is a scalar). The MRC output SINR is: Yakun Sun, et. Al.

  7. Ideal vs. Practical Channel Estimation • Receiver can assume ideal channel estimation. • Textbook SINR equations • If practical channel estimation is assumed, the impact on SINR can be represented by an additional noise for channel estimation error. • For example, a good approximation for SISO channel with one-shot channel estimation is to scale noise power by 2, (i.e., σCE2=2σ2) • Better approximations for CE errors are open to discuss. Yakun Sun, et. Al.

  8. Comments on SINR Calculation • Given asynchronous transmission in OBSS, the transmit power and channel responses from each interfering BSS may vary (and disappear) across time. • K interfering transmitters are assumed to be frequency-selective. • To simplify the simulation, flat-fading channels can be assumed for transmitters far away (with low received power). • TxEVMcan be modeled by capping EVM-free SINR. Yakun Sun, et. Al.

  9. Summary • Briefly introduce how the receiver-output SINR can be calculated. • Receiver-output SINR is a function of channel frequency responses and received signal power from each transmitter (desired or not). • SINR depends on the receiver type and beamforming schemes. • SINR calculation can include a simple and efficient modeling of transmitter/receiver details, such as CE error, EVM, and etc. Yakun Sun, et. Al.

  10. References [1] IEEE 802.16m-08/004r5, Jan. 2009 [2] R1-050680, “Text Proposal: Simulation Assumptions and Evaluation for EUTRA”, 3GPP TSG RAN WG1 #41bis, June, 2005 [3] R1-061626, “LTE Downlink System Performance Evaluation Results”, 3GPP TSG RAN1 #45, May, 2006 Yakun Sun, et. Al.