St-ericsson Sa

Nissan Motor Company Limited

Ampere S.a.s.

Renault S.a.s.

Sébastien Aubert

Andrea Ancora

Philippe Weingertner

Jean-Pierre Giacalone

A device for planning a path and/or a trajectory for a motor vehicle includes a module for receiving a sequence of input variables and hardware and software for determining a control law corresponding to the path and/or the trajectory according to the sequence of input variables received. The device includes a temporal classification unit in a set of compact representations.

Device for planning a path and/or trajectory for a motor vehicle

A detection process for a receiver of a wireless communication system based on Multiple-Input Multiple-Output antennas (n, n), said receiver processing observations symbols y derived from symbols x transmitted by an emitter through a channel H; characterized in that it involves: &#x2014;a preprocessing which only depends on the channel H, said preprocessing involving: &#x2014;a first QRD decomposition () for the purpose of decomposing said channel H into two Qand Rmatrices, with QH=/and Rbeing upper triangular; &#x2014;a lattice reduction () for the purpose of generating Q, Rand a transformation matrix T; &#x2014;a second QRD decomposition () applied on the matrix RTfor the purpose of generating two matrixes Q'and R&#x2032;, &#x2014;a loading phase () comprising a linear detection process of the observations y for the purpose of generating a value x; &#x2014;a neighborhood search (-) performed in the Original Domain Neighborhood (ODN) with a search center being equal to the result xof said loading phase, said neighborhood search determining a limited number of symbols (K-best).

MIMO receiver using lattic reduction and K-Best detection

A process selects a Precoding Matrix Index (PMI) in a Multiple In Multiple Out (MIMO) receiver used in a wireless communications system including a base station communicating with User Equipments (UE) through a downlink and uplink channel. The base station applies a precoding on the transmit symbol vector based on a matrix selected from a set of predefined matrices and identified by a PMI index computed by the UE and forwarded to the base station via the uplink. The process includes estimating the MIMO channel matrix H of a given set of resources blocks comprising received symbol vectors, estimating the variance &#x3c3;of the additive noise (AWGN), and computing for each particular matrix comprised within the set of predefined matrices a cost function representative of the orthogonality of the matrix MIMO channel matrix H. The process further includes comparing the values of the cost function and transmitting to the base station the index corresponding to the matrix corresponding to the best conditioned MIMO channel matrix according to the comparison of the values.

Precoding matrix index selection process for a MIMO receiver based on a near-ML detection, and apparatus for doing the same

A detection process for a receiver of a wireless communication system based on Multiple-In-Multiple-Out antennas, the process involving: &#x2014;a preprocessing which only depends on the channel H, said preprocessing involving: &#x2014;A QRD decomposition () for the purpose of decomposing said channel H into two Q and R matrices, with QQ=I and R being upper triangular; &#x2014;a lattice reduction () for the purpose of generating (formula AA, formula BB) and a permutation matrix T; &#x2014;a loading phase () comprising a linear LRA-Minimum-Mean-Square-Error equalization applied on said symbols y in accordance with the result of said lattice reduction for the purpose of generating a value (formula CC). The process is characterized by the fact that it further involves the steps of: &#x2014;Performing a neighborhood search with a search center being equal to the result (formula CC) of said lattice reduction; &#x2014;Determining the -BKest symbols in accordance with a Partial Euclidean Distance (PED) defined in accordance with the following formula (formula DD)&#x2014;detecting each layer and with the result of said detection performing an update of the search center so as to perform detection of the next layer; &#x2014;multiplying the estimated value (formula EE) by said matrix T plus quantizing it onto the original constellation so as to generate the estimated value (formula FF).

Detection process for a receiver of a wireless MIMO communication system

A process for performing a QR Decomposition of a channel matrix of a wireless communication channel system, said process involving the steps of: performing a full QR Decomposition of one channel matrix for the purpose of deriving a first QR representation of the channel; performing a second iterative QR Decomposition of an adjacent channel matrix, said second iterative QR Decomposition using the results of said first QR representation as well as the difference of the two adjacent channel matrices &#x394;H. More particularly, the process is characterized in that the iterative QR Decomposition is performed in response to the detection of a high level of workload of the processor.

Process for performing a QR decomposition of a channel matrix in a MIMO wireless communication system, and receiver for doing the same

A process for computing Log-Likelihood-ratios (LLRs) in a detector of a wireless communication receiver is disclosed, with the, LLRs being used by a channel decoder. A signal is received from a telecom front end, the signal corresponding to data belonging to a finite set of constellation symbols, each constellation symbol being arranged in a lattice constellation impaired by additive noise and a multiplicative channel. A limited set of distances representative of Euclidian distances between the received signal and a finite set of predetermined constellation symbols are computed, possibly multiplied by the channel. A set of soft decision LLRs are derived from the computed set of distances under the constraint of a limited length of the list of distances. The derived LLRs are completed by clipping values read from a look-up table which is simultaneously addressed by the values of the SNR and a bit index.

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Nice, France

Sébastien Aubert