The present disclosure relates to a demodulation method, apparatus, and device. Some embodiments of the present disclosure are beneficial to improving demodulation performance.

The present disclosure relates to an encoded signal demodulation method, apparatus, and device. Some embodiments of the present disclosure are beneficial to improving demodulation performance.

A relay device relaying, to a reception device, a signal stream transmitted by a transmission device through MIMO transmission, the relay device including a lattice base reduction processing unit transforming bases of the signal stream transmitted by the transmission device through MIMO transmission, to increase orthogonality of a lattice of the signal stream, a MIMO equalization unit detecting, by equalization, reception symbols in the signal stream with the bases transformed by the lattice base reduction processing unit, a symbol quantization unit performing quantization by mapping the reception symbols detected by the MIMO equalization unit, to a region on a complex plane delimited by quantization threshold values, and a transmission unit transmitting, to the reception device, at least a signal quantized by the symbol quantization unit.

A decoder for determining an estimate of a vector of information symbols carried by a signal received through a transmission channel represented by a channel matrix is provided. The decoder includes a block division unit configured to divide the vector of information symbols into two or more sub-vectors, each sub-vector being associated with a block level; two or more processors configured to determine, in parallel, candidate sub-vectors and to store the candidate sub-vectors in a first stack. Each processor is configured to determine at least a candidate sub-vector by applying a symbol estimation algorithm and to store each candidate sub-vector with a decoding metric and the block level associated with the candidate sub-vector. The decoding metric is lower than or equal to a decoding metric threshold. A processor among the two or more processors is configured to determine at least a candidate vector from candidate sub-vectors stored in the first stack, the candidate vector being associated with a cumulated decoding metric and to update the decoding metric threshold from the cumulated decoding metric.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums which optimize one or more metrics of a communication system by intentionally changing symbols in a bitstream after encoding by an error correction coder, but prior to transmission. The symbols may be changed to meet a communication metric optimization goal, such as decreasing a high PAPR, reducing an error rate, reducing an average power level (to save battery), or altering some other communication metric. The symbol that is intentionally changed is then detected by the receiver as an error and corrected by the receiver utilizing the error correction coding.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums which optimize one or more metrics of a communication system by intentionally changing symbols in a bitstream after encoding by, an error correction coder, but prior to transmission. The symbols may be changed to meet a communication metric optimization goal, such as decreasing a high PAPR, reducing an error rate, reducing an average power level (to save battery), or altering some other communication metric. The symbol that is intentionally changed is then detected by the receiver as an error and corrected by the receiver utilizing the error correction coding.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums which optimize one or more metrics of a communication system by intentionally changing symbols in a bitstream after encoding by, an error correction coder, but prior to transmission. The symbols may be changed to meet a communication metric optimization goal, such as decreasing a high PAPR, reducing an error rate, reducing an average power level (to save battery), or altering some other communication metric. The symbol that is intentionally changed is then detected by the receiver as an error and corrected by the receiver utilizing the error correction coding.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums which optimize one or more metrics of a communication system by intentionally changing symbols in a bitstream after encoding by an error correction coder, but prior to transmission. The symbols may be changed to meet a communication metric optimization goal, such as decreasing a high PAPR, reducing an error rate, reducing an average power level (to save battery), or altering some other communication metric. The symbol that is intentionally changed is then detected by the receiver as an error and corrected by the receiver utilizing the error correction coding.

Apparatus and method for symbol detection are disclosed. The solution comprises obtaining (400) multiple-input-multiple-output symbols received over a transmission channel, the symbols comprising a plurality of layers, each layer comprising a constellation point of multiple candidate constellation points, selecting (402) for each layer a precision, each layer having a precision smaller or equal than the precision of a previous layer and searching (404) for each layer, utilising the selected precision, the constellation point among the candidate constellation points by minimising a given cost function, utilising a plurality of Arithmetic and Logic Units, ALUs, comprising at least one real and imaginary part, the ALUs of the apparatus comprising real and imaginary part having different precisions by having different number of bits, the data memory and the plurality of ALUs being connected with each other by a data bus of a given width.

Apparatus and method for symbol detection are disclosed. The solution comprises obtaining (400) multiple-input-multiple-output symbols received over a transmission channel, the symbols comprising a plurality of layers, each layer comprising a constellation point of multiple candidate constellation points, selecting (402) for each layer a precision, each layer having a precision smaller or equal than the precision of a previous layer and searching (404) for each layer, utilising the selected precision, the constellation point among the candidate constellation points by minimising a given cost function, utilising a plurality of Arithmetic and Logic Units, ALUs, comprising at least one real and imaginary part, the ALUs of the apparatus comprising real and imaginary part having different precisions by having different number of bits, the data memory and the plurality of ALUs being connected with each other by a data bus of a given width.