# US 10,374,853 B2 · Method and apparatus for low complexity transmission and reception of constant or quasi-constant envelope continuous phase modulation waveforms > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,374,853 B2 - Document ID: US10374853B2 - Status: Granted - Kind: B2 - Application number: 15/907,494 - Filed: 2018-02-28 - Published: 2018-09-06 - Granted: 2019-08-06 - Earliest claimed: 2015-08-28 - Assignees: Marecomms Inc. - Applicants: Marecomms Inc. - Inventors: Ulaş Güntürkün - Web page: https://patentgrep.com/patent/US10374853B2 ## CPC - H04L 27/227: using coherent demodulation - H04L 25/03057: {with a recursive structure (H04L25/03031 takes precedence)} - H04L 25/03171: {Arrangements involving maximum a posteriori probability [MAP] detection} - H04L 25/03318: {Provision of soft decisions} - H04L 27/2003: {for continuous phase modulation (frequency shift keying H04L27/10)} - H04L 2025/03407: {Continuous phase} - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract To reflect advantages of continuous phase modulation (CPM), the invention provides a low complexity transmitter and receiver to transmit and receive CPM signals and addresses a significant reduction in the CPM demodulator complexity, and is especially well-suited for large values of L, e.g., L≥3. The invention utilizes a linear filter front end as an integral part of the CPM demodulation process to reduce the ISI inherent in CPM transmit signal, and minimizes the influence of L in the reception process. To that end, the invention renders the complexity of a CPM demodulator non-exponentially dependent on L, and L only has a weak impact on the number of coefficients of the linear front end filters. Moreover, the invention provides a simple way of forming CPM signals for a digital communication transmitter using parallel Time Invariant Phase Encoders, which simplifies the production of CPM waveforms on software or hardware. ## Claims ### Claim 1 A demodulator for performing low complexity coherent demodulation of constant or quasi-constant envelope Continuous Phase Modulation (CPM) signals for a baseband received signal to generate desired modulation symbol estimates, wherein the demodulator comprises: a noise reduction filter for filtering the baseband received signal to generate received samples which are sampled at symbol time; a bank of K linear Minimum Mean Square Error (MMSE) filters for producing CPM pseudosymbol estimates; and a symbol detector for performing symbol detection on the CPM pseudosymbol estimates to generate the desired modulation symbol estimates, wherein the demodulator is compatible with alphabet lengths (M>=2), all modulation indexes (h), all pulse lengths (L), and all pulse types, K=1 when M=2, and K>1 when M>2, and the K linear MMSE filters are in parallel and combat interference for M>2 or L>1. ### Claim 2 (depends on 1) The demodulator according to claim 1, wherein the symbol detector comprises a CPM symbol detector and the linear MMSE filters operate according to the MMSE criteria in which an optimum tap weight coefficient vector is represented by wi=[HiAiHiH+Vi]−1pi where Hi is a pulse convolution matrix, H denotes a complex conjugate operator, Ai is an autocorrelation matrix for the CPM pseudosymbol vectors, Vi is a noise covariance matrix, and pi is a cross-correlation vector between an input and a desired response of the linear MMSE filters. ### Claim 3 (depends on 2) The demodulator according to claim 2, wherein samples observed at the output of the linear MMSE filters are approximated according to a memoryless model of âi,n=μiai,n+ωi,n where μi is a scaling constant or a filter gain for the MMSE filters, ωi,n denotes a Gaussian distributed noise sample with zero mean and variance σωi2, and the use of linear MMSE filters result in a memoryless, signal plus noise model, in which the effect of ISI is reduced. ### Claim 4 (depends on 1) The demodulator according to claim 1, wherein the CPM symbol detector is a p-state CPM symbol detector that results in a reduction of demodulation complexity from pML−1 states to only p states for all selections of h, M and L, where p is the denominator of the modulation index. ### Claim 5 (depends on 4) The demodulator according to claim 4, wherein the e-state CPM symbol detector performs symbol detection based on a maximum likelihood (ML) criteria where the desired symbol estimates are obtained as α ^ n = arg ⁢ ⁢ max α ⁢ f ⁡ ( a ^ 0 , n , a ^ 1 , n , … ⁢ , a ^ K - 1 , n ❘ α n ) , or based on a maximum a posteriori probability (MAP) criteria where the desired modulation symbol estimates are obtained as α ^ n = arg ⁢ ⁢ max α ⁢ P ⁡ ( α n ❘ a ^ 0 , n , a ^ 1 , n , … ⁢ , a ^ K - 1 , n ) where αn denotes an M-ary message symbol of a CPM alphabet of size M. ### Claim 6 (depends on 1) The demodulator according to claim 1, wherein the symbol detection is performed using soft interference cancellation and the bank of linear MMSE filters receive a soft decision feedback and subtract remodulated interference estimates from the received samples before performing filtering to produce the CPM pseudosymbol estimates, and the demodulator further comprises: a CPM Soft Input Soft Output (SISO) symbol detector for performing symbol detection on the CPM pseudosymbol estimates using a priori information to produce soft decisions on a joint CPM state and to generate extrinsic information; a remodulator which generates and feeds intersymbol interference estimates back to the linear filters with soft decision feedback as the remodulated interference estimates; and a SISO channel decoder which generates and feeds the a priori information back to the CPM SISO symbol detector, and produces a posteriori data estimates that are used as the desired modulation symbol estimates. ### Claim 7 (depends on 6) The demodulator according to claim 6, wherein the CPM SISO symbol detector and the SISO channel decoder operate such that demodulation and channel decoding are performed jointly in an iterative fashion by repeatedly producing and delivering the extrinsic information to the SISO channel decoder and producing and feeding the a priori information to the CPM SISO detector a number of times and at the end of a final iteration, the a posteriori data estimates are generated. ### Claim 8 (depends on 6) The demodulator according to claim 6, wherein the linear MMSE filters with soft decision feedback are realized using a minimum mean square error with soft interference cancellation (MMSE-SIC) criteria, according to which an optimum tap weight coefficient vector is represented by wif=[pif[pif]H+Vi]−1pif where pif is a cross-correlation vector between an input and a desired response of the linear MMSE filters with soft decision feedback, Vi is a noise covariance matrix, and H denotes a complex conjugate operator. ### Claim 9 (depends on 8) The demodulator according to claim 8, wherein samples observed at an output of the linear MMSE filters with soft decision feedback are approximated according to a memoryless model of âi,n=μifai,n+ωi,nf where âi,n is a sample observed at the output of the linear MMSE filters with soft decision feedback, μif is a scaling constant or a filter gain for the linear MMSE filters with soft decision feedback, ωi,nf is a noise sample with zero mean and variance σ ω i , n f 2 , and the linear MMSE filters with soft decision feedback results in a memoryless, signal plus noise model in which the effect of ISI is further reduced due to soft interference cancellation. ### Claim 10 (depends on 1) The demodulator according to claim 1, wherein the noise reduction filter is a bank of matched filters each having an impulse response that is matched to a CPM pulse which is obtained from a Laurent, Mengali-Morelli decomposition of the CPM pseudosymbols. ### Claim 11 (depends on 1) The demodulator according to claim 1, wherein the demodulator is implemented using various combinations of Field Programmable Logic arrays, Application Specific Integrated Circuits, Digital Signal Processing platforms and software. ### Claim 12 A method for low complexity coherent demodulation of constant or quasi-constant envelope Continuous Phase Modulation (CPM) signals for a baseband received signal to generate desired modulation symbol estimates, wherein the method comprises: filtering the baseband received signal using a bank of K noise reduction filters to generate received samples which are sampled at symbol time; performing linear filtering on the received samples using a bank of K linear Minimum Mean Square Error (MMSE) filters to produce CPM pseudosymbol estimates; performing symbol detection on the CPM pseudosymbol estimates to generate the desired modulation symbol estimates, wherein the method is compatible with alphabet lengths (M>=2), all modulation indexes (h), all pulse lengths (L), and all pulse types, K=1 when M=2, and K>1 when M>2, and the K linear MMSE filters are in parallel and combat interference for M>2 or L>1. ### Claim 13 (depends on 12) The method according to claim 12, wherein the symbol detection is performed using a CPM symbol detector and the linear MMSE filters are realized using the MMSE criteria, according to which an optimum tap weight coefficient vector is represented by wi=[HiAiHiH+Vi]−1pi where Hi is a pulse convolution matrix, H denotes a complex conjugate operator, Ai is an autocorrelation matrix for the CPM pseudosymbol vectors, Vi is a noise covariance matrix, and pi is a cross-correlation vector between an input and a desired response of the linear MMSE filters. ### Claim 14 (depends on 13) The method according to claim 13, wherein samples observed at the output of the linear MMSE filters are approximated according to a memoryless model of âi,n=μiai,n+ωi,n where μi is a scaling constant or a filter gain for the MMSE filters, ωi,n denotes a Gaussian distributed noise sample with zero mean and variance σωi2, and the use of linear MMSE filters result in a memoryless, signal plus noise model, in which the effect of ISI is reduced. ### Claim 15 (depends on 12) The method according to claim 12, wherein the performing of symbol detection further comprises using a Maximum A posteriori Probability (MAP) detection criteria where the desired modulation symbol estimates are obtained as α ^ n = arg ⁢ ⁢ max α ⁢ f ⁡ ( a ^ 0 , n , a ^ 1 , n , … ⁢ , a ^ K - 1 , n ❘ α n ) , or based on a maximum a posteriori probability (MAP) criteria where the desired modulation symbol estimates are obtained as α ^ n = arg ⁢ ⁢ max α ⁢ P ⁡ ( α n ❘ a ^ 0 , n , a ^ 1 , n , … ⁢ , a ^ K - 1 , n ) where an denotes an M-ary message symbol of a CPM alphabet of size M. ### Claim 16 (depends on 12) The method according to claim 12, wherein the symbol detection is performed using soft interference cancellation comprising: performing linear filtering by subtracting remodulated interference estimates from the received samples before performing filtering using K linear MMSE filters with soft decision feedback to produce the CPM pseudosymbol estimates; performing the symbol detection on the CPM pseudosymbol estimates using a CPM Soft Input Soft Output (SISO) detector and a priori information to produce soft decisions on a joint CPM state and to produce extrinsic information; remodulating the CPM pseudosymbol estimates using a remodulator to obtain mean interference estimates that are used as the remodulated interference estimates; and using an SISO channel decoder to generate the a priori information and to generate a posteriori data estimates that are used as the desired modulation symbol estimates, wherein the linear MMSE filters with soft decision feedback provides for further reduction of ISI that is inherent in the CPM signals. ### Claim 17 (depends on 16) The method according to claim 16, wherein the CPM SISO symbol detector is used when a channel code is used, such that demodulation and channel decoding are performed jointly in an iterative fashion by repeatedly producing and delivering the extrinsic information to the SISO channel decoder and producing and feeding the a priori information to the CPM SISO detector a number of times and at the end of a final iteration, the a posteriori data estimates are generated. ### Claim 18 (depends on 16) The method according to claim 16, wherein the method comprises realizing the linear MMSE filters with soft decision feedback using a Minimum Mean Square Error with Soft Interference Cancellation (MMSE-SIC) criteria, according to which an optimum tap weight coefficient vector is represented by wif=[pif[pif]H+Vi]−1pif where pif is a cross-correlation vector between an input and a desired response of the linear MMSE filters with soft decision feedback, Vi is a noise covariance matrix, and H denotes a complex conjugate operator. ### Claim 19 (depends on 18) The method according to claim 18, wherein samples observed at an output of the linear MMSE filters with soft decision feedback are approximated according to a memoryless model of âi,n=μifai,n+ωi,nf where âi,n is a sample observed at the output of the linear MMSE filters with soft decision feedback, μif is a scaling constant or a filter gain for the linear MMSE filters with soft decision feedback, ωi,nf is a noise sample with zero mean and variance σ ω i , n f 2 , and the linear niters with soft decision feedback results in a memoryless, signal plus noise model in which the effect of ISI is further reduced due to soft interference cancellation. ### Claim 20 (depends on 12) The demodulator according to claim 12, wherein the noise reduction filter is a bank of matched filters each having an impulse response that is matched to a CPM pulse which is obtained from a Laurent, Mengali-Morelli decomposition of the CPM.