Nxp Usa, Inc.

Marius Octavian Arvinte

Andrei Alexandru Enescu

Victor-Florin Crasmariu

Leo G Dehner

Wim Joseph Rouwet

A transmitter includes a plurality of user-specific channels, with each user specific channel associated with a different set of user equipment (UE) receive antennas. For precoding, the transmitter generates a baseline channel matrix reflecting the characteristics of the communication medium employed to transmit data to the different user equipment (UEs). For each user-specific channel, the transmitter generates a complementary channel matrix based on the baseline channel matrix, then performs matrix decomposition to eliminate selected terms of the complementary channel matrix that interfere from other communication channels of the transmitter. The transmitter can reuse portions of one rotational matrix set generated for one channel to generate the rotational matrix sets for one or more other channels.

Complexity reduction for transmitter precoding

A decoder decodes a set of data streams received at a receiver based on a tree search that employs a subset of decoding constellation points. The decoder can form a tree wherein each level of the tree corresponds to one of the set of data streams. Each level of the tree includes a plurality of nodes corresponding to a set of candidate constellation points, wherein the set of candidate constellation points indicating possible values of data received via the set of data streams. For tree levels beyond an initial tree level, the decoder expands each node (that is, calculates the metrics for nodes of the next tree level) for only a subset of candidate constellation points, wherein the subset of candidate constellation points is based on a sign value of the node.

MIMO decoding based on quadrant identification

A method, system, and apparatus are provided for computing soft bits in a non-linear MIMO detector which decodes a signal received at a plurality of receive antennas using channel estimate information and a decoding tree to produce output data for a bit estimation value which includes a maximum likelihood solution along with a naturally ordered vector identifying all explored node metrics and node indices, where soft bits are computed for each bit estimation value by determining a set of bit-masks through repetition and indexing operations applied on the explored node indices, masking the naturally ordered vector with the set of bit-masks to generate masked node metrics, determining candidate soft bit values by subtracting metrics of all nodes that form the maximum likelihood solution from the masked node metrics, and determining a final soft bit value by identifying which of the candidate soft bit values has a lowest value.

Soft bit computation unit for MIMO detection and error correction

A receiver decodes received data streams based on a subset of candidate decoding constellation points. A first stage of a decoder of the receiver selects a subset of candidate decoding constellation points by identifying a decoded value for an initial data stream of the set of data streams. A second stage then applies MMSE error detection to each of the constellation points in the selected subset, and calculates an error metric based on the MMSE error detection results. The decoder selects the constellation points having the lowest error metrics, and uses the selected constellation points as an initial set of points for decoding the next data stream to be decoded.

Complexity reduction for receiver decoding

A transmitter includes a plurality of user-specific channels, with each user specific channel associated with a different set of user equipment (UE) receive antennas. For precoding, the transmitter generates a baseline channel matrix reflecting the characteristics of the communication medium employed to transmit data to the different user equipment (UEs). For each user-specific channel, the transmitter generates a complementary channel matrix based on the baseline channel matrix, then performs matrix decomposition (e.g., QR-Givens decomposition) to eliminate selected terms of the complementary channel matrix that interfere from other communication channels of the transmitter.

A nonlinear MIMO-OFDM detector includes a vector arithmetic unit (VAU) that sequentially computes first metrics corresponding to a first current tree level of a first search tree and second metrics corresponding to a second current tree level of a second search tree. A sorting and indexing unit (SIU) that sorts the first metrics and the second metrics sequentially received from the VAU and that sequentially provides first indices of lowest first metrics and second indices of lowest second metrics to the vector arithmetic unit. The lowest first metrics are first inputs to the VAU for a first next tree level of the first search tree and the lowest second metrics are second inputs to the VAU for a second next tree level of the second search tree. The VAU and the SIU are pipelined to compute the second metrics concurrently with sorting and indexing of the first metrics.

Nonlinear MIMO-OFDM detector

A transmitter generates precoding matrices for communication channels of a transmitter, The transmitter includes a plurality of user-specific channels, with each user specific channel associated with a different set of user equipment (UE) receive antennas. For precoding, the transmitter generates a baseline channel matrix reflecting the characteristics of the communication medium employed to transmit data to the different user equipment (UEs). For each user-specific channel, the transmitter generates a set of null space vectors wherein only a subset of the generated null space vectors generated by the transmitter are used to precode the data. To identify the combination of null space vectors to be used for each channel, the transmitter calculates a quality score for each such combination of null space vectors. The transmitter uses the subset of null vectors that yields the highest score to precode the data.

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Focsani, Romania

Marius Octavian Arvinte