Dynamic spectrum sharing with other networks using optimized PHY/MAC layers GARY CHURAN, SANTANU DUTTA AND DUNMIN ZHENG, OCTOBER 29, 2019, VERSION 1.0
Agenda ● Problem Statement ● Potential Applications of CDMA-IA ● Key Features ● Concept of Operations (CONOPS) ● Transmitter Block Diagram ● Receiver Block Diagram ● Simulations ● Potential for enhancement via AI ● Summary & Conclusions ● Backup (detailed description) 2
Problem Statement ● Use Case Network #1 • Several independent services share a Network #3 common wideband channel Network #2 • The services have different access priorities • The lowest priority service can 10 MHz a utonomously sense the spectrum Spectrum near node-i of CDMA_IA network occupancy of the shared band and Network #5 Network #1 adaptively utilize unused segments . Network #3 ● Examples of potential applications • HF, CBRS-GAA 10 MHz Spectrum near node-j of CDMA_IA network Spectrum sharing in CDMA_IA (CDMA-IA spectrum shown as green) 3
Potential Applications for CDMA-IA: (1) HF Characteristics of HF Interference [1], [2] • HF interference spectrum occupancy changes from High at midnight to Low at midday • At midnight, the spectrum occupancy is often close to 100% when examined through a 3-kHz bandwidth filter but 50% when observed through a 100-Hz bandwidth filter • HF spectrum occupancy often remains constant over more than 30 minutes and hundreds of kms [1] Gott, G. F., Dutta, S., Doany , P., “Analysis of HF interference with application to digital communications”, IEE Proceedings, Vol. 130, Pt. F, No. 5, AUGU ST 1983 [2] Dutta, S., and Gott, G. F., “Correlation of HF interference spectra with range,” IEE Proceedings, Vol. 128, Pt. F, No. 4, AUGUST 1981 CDMA-IA could substantially improve utilization of the HF band 4
Potential Applications for CDMA-IA: (2) CBRS-GAA PAL: Priority Access License (Tier-2 priority) Environmental Incumbent: Military Radars, Fixed Satellite, Wireless ISP (temp.) (Tier-1 users) SAS Sensors GAA: General Authorized Access (Tier-3 priority) GAA Tx/RX GAA Tx/RX PAL Tx/RX PAL Tx/RX Incumbent Incumbent Tx/RX Tx/RX • GAA users are informed by the SAS of the Spectrum Occupancy of higher priority users. Hence, there are no issues in maintaining dynamic spectrum separation. • GAA users coexist on overlaid basis based on traditional, code-based orthogonality of CDMA CDMA-IA would be good air interface for GAA 5
CDMA-IA Key Features ● Topology: wireless, ad-hoc mesh (no central controller) ● Shares a common band, say 10 MHz wide, with other independent services employing arbitrary access protocols and spectrum occupancies ● Other networks expected to have higher channel access priority than CDMA-IA ● User traffic is transported by a multiplicity of simultaneous unicast links ● Control information is shared between nodes via unicast or broadcast links ● Duplexing mode: TDD 6
Concept of Operations (CONOPS) for CDMA-IA ● Each node measures the spectrum occupancy at its location and broadcasts a coded, low-bandwidth description of the spectrum occupancy, called Spectrum Usability Mask (SUM), to all other nodes of the CDMA-IA network. ● SUM broadcast is performed using traditional CDMA over the full band ● In the unicast links, each the transmit node’s signal’s spectrum is shaped to fit into the holes of the SUM at the destination node. • Referred to as ‘water filling’ in signal design. • Avoids causing interference to other networks and accepting interference from other networks (beyond ss processing gain of CDMA). ● Other innovations • Application of Fountain Codes in the frequency domain to excise unusable spectrum segments, as defined in the destination SUM → Efficient implementation of spectrum excision while maintaining link communication efficiency • In receiver, coherent integration of signal energy over discontinuous segments of spectrum → Not done in existing, interference-avoiding spread spectrum systems, such as Bluetooth. Improves communication efficiency. • Spectrum spreading is performed using OFDM signals distributed over the spread bandwidth → More DSP friendly than direct spreading 7
Transmitter Block Diagram The PSD data consists of “ N ” discrete measurement points out-of-network across the wideband channel Incoming info bit stream is transmissions bandwidth. split into equal-size blocks: maximum Spectrum . . . . . . interference B 1 B 2 B 3 setpoint Usability Mask (from Freq f c receiving node Each block contains b 1 Wideband channel bandwidth via control K info bits: b 2 Spectrum Usability Mask channel) b : (“ N ” discrete points across chnl. BW) . . . 1 (usable) Time domain Element- Frequency domain symbol block Pseudo-random b K Freq by-element masked (complex elements) spreading matrix multiply cos( w t) symbol block x : G : Rate R FEC g n,m → ±1, N >> M d 1 1 d 1 x 1 . . . d 2 0 0 x 2 Re{∙} g 1,1 g 1,2 g 1,M AGC Transmit d 3 1 d 3 x 3 N- signal: c 1 S Tx (t) c 2 point . . . . . . . . . . . . . . . . . . Matrix IFFT multiply . . . Im{∙} d N-1 1 d N-1 x N-1 . . . M = c M d N 0 0 x N g N,1 g N,2 g N,M K/R sin( w t) N x M M x 1 N x 1 N x 1 8
Receiver Block Diagram G’ : PN spreading matrix (elements = ±1) cos( w t) . . . Matrix G’ is formed by sample g 1,1 g 1,2 g 1,M Received signal removing rows from G that + noise correspond to the positions + interference: . . . Complex of the zeros in the Spectrum S Rx (t) L elements Demod. Usability Mask. “ L” denotes + n(t) M the number of rows sample . . . g L,1 g L,2 g L,M remaining in G’ . Spectrum Usability Decoded FEC Mask info. encoded Form pseudo-inverse jsin( w t) bits bits Element - d” 1 d” : c’ : b’ : by-element c’ 1 g -11,1 g -11,L G’ -1 : b’ 1 multiply d” 2 c’ 2 g -12,1 g -12,L . . . b’ 2 FEC d” 3 Received decoder x’ 1 d’ 1 1 d’ : Matrix time domain . . . . . . . . . x’ 2 d’ 2 0 . . . g -1M,L g -1M,1 multiply . . . b’ K d” L-1 c’ M symbol block x’ 3 d’ 3 1 M x L M x 1 K x 1 d” L x’ : N - . . . . . . Remove zeroed-out point . . . . . . L x 1 elements FFT x’ N-1 d’ N-1 1 from d’ . x’ N d’ N 0 N x 1 N x 1 9
Simulation Scenario Transmit Signal Envelope Transmit Signal PSD 1.4 Signal PSD (dB) Usability mask Note: Tx power is independent 50.0 6 Symbol magnitude 1.2 of the fraction of the band that 40.0 5 1 is open PSD (dB) 30.0 4 0.8 20.0 3 0.6 10.0 2 0.4 0.0 1 0.2 -10.0 0 8% of the band is open 0 0 500 1000 1500 2000 0 500 1000 1500 2000 Channel symbol frequency Channel symbol time Transmit Signal Envelope Transmit Signal PSD 1.2 Signal PSD (dB) Usability mask 50.0 6 Symbol magnitude 1 40.0 5 0.8 PSD (dB) 30.0 4 0.6 20.0 3 0.4 10.0 2 0.0 1 0.2 63% of the spectrum open -10.0 0 0 0 500 1000 1500 2000 0 500 1000 1500 2000 Channel symbol frequency Channel symbol time 10
Simulation Results Plot of Demodulated Data Bit + Noise Amplitude 2 Demodulated data bit + noise amplitude 1.5 L/N = 8% 1 L/N = 19% Channel Demod. Transmit Usability Output Signal 0.5 L/N = 29% L/N SNR (dB) PAPR (dB) 8% 5.0 9.3 0 L/N = 46% 19% 8.8 7.6 -0.5 L/N = 63% 29% 7.5 9.1 46% 8.1 7.8 -1 L/N = 90% 63% 8.8 7.9 90% 8.2 8.7 -1.5 L/N = 100% 100% 8.6 8.8 -2 Xmt. data -2.5 1 8 15 22 29 36 43 50 57 64 Data bit position No systematic variation in received SNR with Channel Usability reducing from 100% to 19% 2.3 dB reduction in received SNR with Channel Usability reducing from 100% to 8% 11
Potential for AI to improve CDMA-IA (based on literature review [3]) State of the Art General Conclusion from [1] ● Spectrum Sensing , Decision Making and Mobility take time to execute. We call the net of the above: Spectrum Adaptation Time . ● If the Spectrum Adaptation Time is long relative to Spectrum Correlation Time , then the performance of CDMA-IA may suffer. ● In many practical applications, such as HF and CBRS, this problem does not exist. • HF Spectrum Correlation Time often exceeds 30 minutes PU: Primary User • In CBRS, the SAS informs Spectrum Occupancy in the band to all transmitters CR: Cognitive Radio ● Nevertheless, we reviewed of AI/Cognitive Radio literature to see if CDMA-IA could be improved. Typical prediction models include • Hidden Markov Models • Multilayer neural network • Bayesian interference-based prediction • Moving average model • Autoregressive model • Static neighbor graph ● An overview of the art is provided in [3]. • Dynamic spectrum sharing based on occupancy prediction is still in the Research Phase • Prediction accuracy may depend on the application • Does not appear to be ready for mainstream deployment [3] XIAOSHUANG XING, et. al, “Spectrum Prediction in Cognitive Radio Networks”, IEEE Wireless Communications, April 2013. 12
Recommend
More recommend
