Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L25/00—Baseband systems
  • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
  • H04L25/0202—Channel estimation
  • H04L25/0238—Channel estimation using blind estimation
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L25/00—Baseband systems
  • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
  • H04L25/0202—Channel estimation
  • H04L25/024—Channel estimation channel estimation algorithms
  • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
  • H04L25/0248—Eigen-space methods
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L25/00—Baseband systems
  • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
  • H04L25/0202—Channel estimation
  • H04L25/0204—Channel estimation of multiple channels

Definitions

• the object of the invention relates to a method for blind demodulation of signals transmitted by several transmitters and received by a network composed of at least one sensor.
• the object of the invention relates in particular to the demodulation of signals, that is to say the extraction of the symbols ⁇ a ⁇ emitted by a linearly modulated transmitter.
• FIG. 1 shows an antenna processing system comprising several transmitters Ei and an antenna processing system T comprising several antennas Ri receiving radioelectric sources with different angles of incidence.
• the angles of incidence of the sources or transmitters can be configured either in 1D with the azimuth ⁇ m or in 2D with the azimuth angle ⁇ m and the elevation angle ⁇ m .
• Figure 3 shows schematically a principle of modulation and demodulation of the symbols ⁇ a k ⁇ emitted by a transmitter.
• the signal propagates through a multipath channel.
• the transmitter transmits the symbol a k at the instant kT, where T is the symbol period.
• Demodulation consists in estimating and detecting the symbols in order to obtain the symbols estimated â at the output of the demodulator.
• the symbol train ⁇ a ⁇ is filtered linearly on transmission by an emission filter H also called shaping filter h 0 (t).
• blind demodulation techniques which do not use a priori information on the transmitted signal: shaping filter, learning sequence, etc.
• SIMO blind demodulation techniques abbreviated single input multiple outputs, (in abbreviation Single Input Multiple Output) called subspace using second order statistics, as described in reference [7].
• These algorithms have the disadvantage of not being robust either to an underestimation or to an overestimation of the order of the propagation channel: temporal spreading dependent on the multipaths and the shaping filter.
• a linear prediction technique has been proposed, described in reference [11], which has the disadvantage of being less efficient when the length of the channel is known.
• the method described in [16] proposes a parametric technique which unfortunately requires the knowledge of the shaping filter.
• the object of the present invention relates to a method based in particular on techniques for separating blind sources known to those skilled in the art and described for example in references [4] [5] [15] [19] assuming that the symbols issued are statistically independent.
• the method constructs a spatio-temporal observation whose mixed sources are trains of symbols of the transmitter. Each symbol train is for example the same symbol train shifted by an integer of symbol period T.
• the invention relates to a method for blind demodulation of a source or transmitter of linear waveform in a system comprising one or more sources and an array of sensors and a propagation channel, characterized in that it comprises at least the steps following:
• the method according to the invention notably offers the following advantages: • It makes no assumption on the constellations of symbols unlike the methods described in the prior art,
• Figure 1 an example of architecture
• FIG. 2 the angles of incidence of the sources
• FIG. 3 the process of linear modulation and demodulation of a train of symbols
• FIG. 4 the diagram of a linear modulation transmitter
• FIG. 5 a summary of the general principle implemented in the invention
• FIG. 7 a first example of implementation of the method where the signal is received in baseband
• FIG. 8 a second example where the signal is received in baseband and the multi-paths are decorrelated
• FIG. 9 a third example where the signal is received in baseband and the multipaths are decorrelated by group.
• the following description relates to a method of demodulation blind to higher orders of a linear waveform transmitter in a network having a structure such as that described in FIG. 1, for example.
• Figures 3 and 4 show the process of linear modulation of a symbol train ⁇ a at rate T by a formatting filter ho (t).
• the comb of symbols c (t) is firstly filtered by the shaping filter ho (t) and then transposed to the carrier frequency f 0 .
• it is also possible to use the Nyquist filter whose Fourier transform h 0 (f) ⁇ IlB (fB / 2) approaches a window of band B, when the roll-off is zero then ho (f) IlB (fB / 2) (the roll-off defines the slope of the filter outside of band B).
• the parameter is the half-length of the emission filter which is spread over a period of (2L 0 +1) IT e .
• l_o 0.
• the expression of s (mlT ⁇ + jT ⁇ ) is according to (2):
• the signal s (t) (FIG. 3) transmitted passes through a propagation channel before being received on a network composed of N antennas.
• the propagation channel can be modeled by P multi-path incidence ⁇ p> delay ⁇ p and amplitude ⁇ p (1 ⁇ p ⁇ P).
• x (t) which corresponds to the sum of a linear mixture of P multi-paths and of a supposed white and Gaussian noise.
• p p is the amplitude of the p ièm ⁇ path
• b (t) is the supposed Gaussian noise vector
• a ( ⁇ ) is the response of the sensor network to a source of incidence ⁇
• A [a ( ⁇ ). ..
• equation (6) can be rewritten as follows:
• U k is a vector of dimension Mx1 received at time k
• nk is the noise vector
• G [gi ... gj.
• the number I L of components must be less than or equal to the dimension M of the observation vector.
• the methods of references [4] [5] and [15] use the statistics of order 2 and 4 of the observations u k .
• the first step uses the statistics of order 2 of the observations Uk (these observations can be functions of the signals received on the sensors) to obtain a new observation z k such that:
• the second step is to identify the base orthogonal of the 6 from the order 4 statistics of the whitened observations Zk. Under these conditions we can extract the signals Sk by performing: ê k ⁇ G ⁇ Z k ⁇ ⁇ ⁇ U k (15)
• the idea implemented in the method according to the invention is to construct a spatio-temporal observation whose mixed sources are trains of symbols of the transmitter. Each train of symbols is for example the same symbol train shifted by an integer of symbol period T.
• FIG. 7 represents a first example of an alternative embodiment of the method where the signal is received in baseband.
• the method comprises a step 1.1 of determining the symbol time Te by applying for example a cyclic detection algorithm, such as that described for example in [1] [10].
• h (k) is a vector whose n ièm8 component is the k iô ⁇ coefficient of the filter linearly filtering the symbol train ⁇ a m ⁇ on the n iè ⁇ sensor.
• the vector coefficient filter h (k) depends on both the formatting filter and the propagation channel.
• the observation vector z (t) being determined, the method applies an ICA type method to estimate the L c symbol trains
• the j th output of the ICA methods gives the symbol train ⁇ â m , j ⁇ associated with the channel vector i zJ .
• the symbol trains ⁇ â m , j ⁇ are estimated with the same amplitude because the symbol trains ⁇ a mi ⁇ are all of the same power by checking:
• the objective of the next step I.4 of the method is to order the L c outputs (â m , j, h zj ) in the same order as the inputs (a m -j, h 2 (i)) in order to get the channel vectors h zj .
• the method intercorrelates two by two the outputs â m ⁇ i and â mJ by calculating the following criterion Cy (k):
• â m , i ma ⁇ â m - k j, j.
• We also re-phase the channel vectors by performing: h z (k j ) h z] Cjm aX j (kj) * .
• h z (0) h z , / max .
• the symbol trains ⁇ â m . k ⁇ associated with channel vectors __. (£,). Knowing that the estimated symbols verify â m . k expQ ⁇ j a ⁇ ) a m -k, the last step of the process will consist in estimating this phase ⁇ j max . To do this, we first identify the constellation of the symbols ak from a database made up of all the possible constellations. This base consists of known constellations such as nPSK, n-QAM. Each time that we detect or learn of a new constellation, we will enrich the database.
• the method then comprises the following steps:
• Step n ° B.2 Determination of the type of the constellation by comparing the position of the states (û m , v m ) of the constellation of ⁇ â k ⁇ with a database composed of all the possible constellations.
• the nearest constellation is made up of the states (u m , v m ) for 1 ⁇ m ⁇ M.
• the method can include a step of estimating the parameters of the propagation channel at angle ⁇ p and delay ⁇ p of equation (8) by the algorithm proposed in [8].
• FIGS. 8 and 9 schematically show another variant embodiment which may include two variants corresponding respectively to the case of uncorrelated multipaths and to the case of correlated multipaths by group.
• > (2l_o + 1) T, have the advantage of being uncorrelated with each other by checking: E [s (t-Xi) s (t-Xj) *] 0.
• equation (4) we then observe that it suffices to apply an ICA type method when P ⁇ N on the observation x (t) to obtain the signals s (tx) of each of the multi-paths. .
• the method determines their powers to keep the signal s (tx pma ⁇ ) of the multi-path of greatest amplitude p P max-
• Step n ° ll.a.1 Determination of the symbol time T by applying a cyclic detection algorithm as in [1] [10].
• Step n ° ll.a.3 Application of an ICA method on the observations x (t) to obtain s, (t) and ai for 1 ⁇ i ⁇ P.
• Step n ° ll.a.5 Constitution of the observation vector z (t) of (22) from the signal S lm (t).
• Step n ° ll.a.6 Application of an ICA method to estimate the symbol trains ⁇ a mn ⁇ where -L 0 ⁇ n ⁇ L 0 .
• Step n ° ll.a.7 Determination of the phase ⁇ ima ⁇ of the output associated with the vector h z (n) with the highest modulus by applying steps B.1, B.2 and B.3.
• the symbol train ⁇ m ⁇ constitutes the output of the demodulator of this sub-process.
• Step n ° ll.a.9 Estimation of the parameters of the propagation channel at angle ⁇ p and delay x p by maximizing for 1 ⁇ i ⁇ P the criteria
• the method considers that part of the multipaths are correlated.
• the signal vector received by the sensors of equation (4) becomes:
• the threshold ⁇ is determined in [3] with respect to a chi-2 law with 2 degrees of freedom.
• the incidences ⁇ p , q are determined from Aq for (1 ⁇ q ⁇ Q) by applying the MUSIC [1] algorithm to the matrix A, ⁇ q H. From these goniometries we deduce the matrices Aq. Knowing that Aq ⁇ q s (t, x q ) . we deduce s (t, x q ) to within a diagonal matrix by performing Aq Xq (t).
• Stage n ° ll.b.1 Determination of the symbol time T by applying a cyclic detection algorithm as in [1] [10].
• Step n ° ll.b.3 Application of an ICA method on the observations x (t) to obtain $ (t) and ⁇ from equation (24).
• Step n ° ll.b.6 Constitution of the observation vector z (t) of (29) from the signal s gmax (t).
• Step n ° ll.b.7 Application of an ICA method to estimate the symbol trains ⁇ a m - n ⁇ where - ⁇ n ⁇ Lo.
• Step n ° ll.b.8 Determination of the phase ⁇ i max of the output associated with the vector h z (i) of higher modulus by applying steps B.1, B.2 and B.3.
• Step n ° ll.b.9 Resetting the symbol train ⁇ â m ⁇ by performing at m - â m exp (-j ⁇ jma ⁇ ) -
• the symbol train ⁇ â m ⁇ constitutes the output of the demodulator of this sub- process.
• Step n c ll.b.10 Estimation of the parameters of the propagation channel at angle ⁇ q , p and delay x q , p .
• Step n ° lll.a.1 Steps 1.1 to I.4 described above to obtain the symbol trains ⁇ b m _ k ⁇ associated with the fi ⁇ & channel vectors.
• Step n ° lll.a.2 Construction of the vector w of equation (32) from Step n ° lll.a.3: Maximization of the Carrier criterion (fo) of equation (33) to obtain fo.
• Step n ° lll.a.4 Application of equation (30) to deduce the symbols ⁇ a m ⁇ from the symbols ⁇ b m ⁇ .
• Step n ° lll.a.5 Steps I.5 to I.7 previously described.
• the steps are as follows:
• Step n ° lll.b.1 Steps ll.a.1 to ll.a.4 described above to obtain the vector z (t) of equation (22).
• Step n ° lll.b.2 Application of ICA methods [4] [5] [15] [19] to estimate the L c symbol trains ⁇ b mj ⁇ associated with the channel vectors h zj .
• Step n ° lll.b.4 Construction of the vector w of equation (32) from h z (k j ).
• Step n ° lll.b.5 Maximization of the Carrier criterion (fo) of equation (33) to obtain f 0 .
• Step n ° lll.b.6 Application of equation (30) to deduce the symbols ⁇ a m ⁇ from the symbols ⁇ b ⁇ .
• Step n ° lll.b.7 Choice of the symbol train associated with the vector h z (i) of higher modulus: ⁇ â m -i ⁇ .
• Step n ° lll.b.8 Steps ll.a.7 to ll.a.9 described above.
• the steps are for example the following:
• Step n ° lll.c.1 Steps ll.b.1 to ll.b.6 n ° 2.2 to obtain the vector z (t) of equation (29).
• Step n ° lll.c.2 Application of ICA methods [4] [5] [15] [19] to estimate the L c symbol trains ⁇ b mj ⁇ associated with the channel vectors h z .
• Step n ° lll.c.4 Construction of the vector w of equation (32) from h z (k j ).
• Step n ° lll.c.5 Maximization of the Carrier criterion (fo) of equation (33) to obtain f 0 .
• Step n ° lll.c.6 Application of equation (30) to deduce the symbols ⁇ a m ⁇ from the symbols ⁇ b m ⁇ - Step n ° lll.c.7: Choice among the symbol trains which one is associated with the vector h z (i) of higher modulus: ⁇ â m -i ⁇ - Step n ° lll.c.8: Steps ll.b.8 to ll.b.10 previously described.

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• Computer Networks & Wireless Communication (AREA)
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