Wang, L., McGeehan, JP., Williams, C., & Doufexi, A. (2008). Radarspectrum opportunities for cognitive communications transmission. In3rd International Conference on Cognitive Radio Oriented WirelessNetworks and Communications, 2008 (CrownCom 2008), Singapore(pp. 1 - 6). Institute of Electrical and Electronics Engineers (IEEE).https://doi.org/10.1109/CROWNCOM.2008.4562496

Peer reviewed version

Link to published version (if available):10.1109/CROWNCOM.2008.4562496

Link to publication record in Explore Bristol ResearchPDF-document

University of Bristol - Explore Bristol ResearchGeneral rights

This document is made available in accordance with publisher policies. Please cite only thepublished version using the reference above. Full terms of use are available:http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/

Radar spectrum opportunities for cognitive communications transmission

Lingfeng (Stephen) Wang, Joe McGeehan, Chris Williams, Angela Doufexi

Contents• Overview• Simulation architecture• Impact on WiMAX performance for the cases of

preamble-collision and data symbol-collision by radar pulse

• Impact on WiMAX PER for different radar waveforms in terms of pulsewidth and pulse repetition time (PRT)

• Transmission opportunities from radar swept rotation rate & antenna radiation patterns

• Summary & future work

Impacts of radar on cognitive system transmission

• Modelling the impact of radar on a WiMAX system

• WiMAX BER/PER performance in the presence of swept pulse radar

• Model parameters investigated are,• Radar pulsewidth, pulse repetition time• Radar antenna radiation pattern, swept rotation period• WiMAX symbol structure, modulation types

Simulation architecture I

• WiMAX system• 2 preambles in downlink and 1 preamble in

uplink transmission• 8 data symbols for data transmission, Cyclic

Prefix inserted• Sample rate is 5.76MHz• Transmission mode considered QPSK ½ rate• Channel Model: SUI 3

Reference: IEEE 802.16-2004, “IEEE Standard for Local and metropolitan area networks, Part 16: Air Interface for Fixed Broadband Wireless Access Systems”, Oct. 2004

IEEE 802.16a-03/01, “Channel models for fixed wireless applications”, June 2003

Simulation architecture II• Radar system

Parameters Unit

Radar frequency 2.7 GHz

Pulse Repetition Time (PRT) 577(100), 3331(578), 5771(1002),3200(555) Samples (µs)

Pulsewidth 5(1), 57(10), 115(20) Samples (µs)

3dB main beamwidth 1.36 (Uniform aperture distribution)1.84 (Cosine aperture distribution ) Degree

Table. 1 Swept radar parameters

Reference: Rec.ITU-R.M.1464-1, “Characteristics of radiolocation radars, and characteristics and protection criteria for sharing studies for aeronautical

radionavigation and meteorological radars in the radiodetermination service operating in the frequency band 2700-2900 MHz”, 2003

M.I.Skolnik, Introduction to Radar Systems, 3rd ed., McGraw-Hill Higher Education, New York, Dec. 2000

Simulation architecture III

Fig. 1 WiMAX packet structure and radar pulse positioning

Fig. 2 Antenna power radiation patterns as a function of radar rotation angle Fig. 3 SIR values at WiMAX node as a function of radar swept rotation angle, 4.8s rotation time of radar

Impact on WiMAX performance for the cases of preamble-collision and data symbol-collision

Fig. 4 QPSK, SUI3 Channel Fig. 5 Transmission performance according to different radar pulse time offsets

• Radar signal collisions with the preamble is less disruptive than collision with the data payload.

• The performance when colliding with different parts of the packet depends on the variation of SIR values.

Impact on WiMAX PER from different radar waveforms in terms of pulsewidth

(a) Pulsewidth=1us (b) Pulsewidth =20us

Fig. 6 PER performance at fixed radar pulsewidth in a Non-fading channel

• Considering a fixed pulsewidth and power level, the radar with the highest PRT has the worst impact on the WiMAX system. The exception is when the radar pulses keep colliding with a WiMAX specific packet portion, e.g. guard band or preamble. In that case the PER can be improved substantially.

• Short-pulsewidth radar is preferred since PER drops faster than long-pulsewidthradar when SIR increases.

Impact on WiMAX PER for different radar waveforms in terms of PRT (Pulse Repetition Time)

Fig. 7 PER performance at fixed PRT in Non-fading Channel

(a) PRT=577 samples (b) PRT=5771 samples

Fig. 8 PRT=5771 samples, SUI3 Channel

• For non fading channels and low PRT, short pulsewidth results in lower PER than that of long pulsewidth radar as SIR increases.

• Using short pulsewidth has limited transmission improvement in high PRT cases.• Implementation of short pulse in high PRT can lead to better PER performance

in SUI3 channel.

Transmission opportunities from radar swept rotation rate & antenna radiation patterns

Fig. 9 Radar spectrum Access Denial Rate for different radiation patterns in Non-fading channel

Fig. 10 ADR performance for WiMAX node approaching/departing Radar

• Access Denial Rate (ADR): WiMAX node not allowed to transmit if PER >10-1.• ADR is increased with increasing PRT. Specific PRT values have lower ADR. SUI3

channel has similar performance but leads to higher ADR. The impact of pulsewidthchanges on ADR are not obvious, especially with high PRT case, compared to the impacts of PRTs on ADR.

• Cosine radar antenna radiation pattern leads to more radar spectrum-access opportunities than Uniform radar radiation pattern under the same radar swept rotation period.

Summary & future work

• Radar signal collisions with the communications preamble is less disruptive than collision with the data payload.

• Short pulse and lower PRT radar is preferred for better communication transmission performance.

• Cosine radar antenna radiation pattern leads to more radar spectrum-access opportunities than Uniform radar radiation pattern under the same radar swept rotation period.

• Radar swept rotation estimation is important to the feasibility of radar spectrum access, which determine the best access timeslots for WiMAX nodes to achieve minimized Packet Error Rate

• Future work focuses on the design of a weighted cooperative sensing algorithm to provide a better global detection performance and a better global false alarm performance than that of standard cooperative sensing algorithm. In addition, the algorithm of the sensing team nodes selection in the mobile scenario are also considered.