Methods, nodes and control modules are disclosed. In the method, circuitry of a first node in a mesh network converts an optical layer in a working path between the first node and a second node, to a data stream in a digital layer. The working path carries data traffic from the first node to the second node in the optical layer of the mesh network when there is no failure in the working path. Circuitry of the first node in the mesh network detects a failure in the working path due to detection of an error in the data stream in the digital layer. The circuitry of the first node establishes, through transmission of at least one signal from the first node to the second node, a restoration path in the optical layer based on, at least in part, detection of the error in the data stream in the digital layer.

An apparatus and method are provided for processing a received input signal comprising a sequence of data blocks. Counter circuitry within the apparatus is arranged to receive a digital representation of the input signal, and for each data block generates a count value indicative of occurrences of a property of the digital representation (for example a rising edge or a falling edge) during an associated data block transmission period. Quantization circuitry then maps each count value to a soft decision value from amongst a predetermined set of soft decision values, where the number of soft decision values in the predetermined set exceeds a number of possible data values of the data block. The output circuitry then generates a digital output signal in dependence on the soft decision values. Such an apparatus has been found to provide a low power technique for a receiver, whilst still enabling the improved sensitivity benefits of using soft decisions to be achieved, and allows the apparatus to be constructed using all digital components.

A wireless receiver including a front end circuit, an adaptive threshold circuit, and a correlator. The front end circuit converts a wireless signal into a series of digital symbols. The adaptive threshold circuit provides an adaptive correlation threshold that is adapted based on a sync word. The correlator correlates the digital symbols with the sync word using the adaptive correlation threshold. The adaptive correlation threshold may be based on amplitude attenuation of the digital symbols that correspond to transitions of the sync word. The adaptive threshold circuit may be a lookup table that stores different threshold values each corresponding to one of multiple different sync words. Alternatively, the adaptive threshold circuit may be implemented as an evaluation circuit that determines the adaptive correlation threshold based on expected amplitude attenuation of the digital symbols that correspond to transitions of the sync word.

Systems and methods are disclosed for constrained receiver parameter optimization. Two parameter optimization functions may be applied, with one function providing constraints on the results of the second function in order to determine a parameter set to apply in the receiver. A method may comprise determining a first parameter set based on a first function, determining a second parameter set based on a second function different from the first function, and determining a third parameter set by using the first parameter set to define a subset of a parameter space to which to limit values from the second parameter set. In certain embodiments, a least squares function may be used to constrain the results of a general cost function.

A system and method for receiving a quadrature amplitude modulation symbol. In some embodiments, the symbol has a plurality of bits and is associated with a point in a constellation of quadrature amplitude modulation points, each point of the constellation having associated with it a binary word. The method includes receiving a first analog signal carrying a modulation; performing initial estimation, to generate a first initial modulation estimate, for a portion of the first analog signal carrying a modulation; identifying, based on the first initial modulation estimate, a row of an initial candidate lookup table, the row corresponding to a region of the constellation; and reading from the row of the initial candidate lookup table a first plurality of initial candidate points of the constellation.

A determination is made that error-correcting code functionality detected a first number of erroneous bits within a memory device. Bits within the memory device are evaluated to identify a subset of the bits as candidate bits. The candidate bits are evaluated to determine whether the error-correcting code functionality returns a non-error state, where no error correction is performed, based upon one or more combinations of candidate bits being inverted. Responsive to the error-correcting code functionality returning the non-error state for only one combination of the one or more combinations of candidate bits being inverted, the one combination of candidate bits is corrected.

The telemetry system used in the measurement while drilling (MWD) or logging while drilling (LWD) is essentially a digital communication system. The fact of the special and hostile drilling environment limits the use of many advanced techniques and equipment, and thus results in a low data transmission rate. While increasing the data rate for the MWD/LWD telemetry system becomes a primary focus, maintaining the system reliability and the decoding quality at a high data rate is equally challenging. This invention presents digital signal processing solutions to a high performance telemetry system with the high data rate, high system reliability, and high decoding quality.

A method and system for training a neural network are herein provided. According to one embodiment, a method includes generating a first labelled dataset corresponding to a first modulation scheme and a second labelled dataset corresponding to a second modulation scheme, determining a first gradient of a cost function between a first neural network layer and a second neural network layer based on back-propagation using the first labelled dataset and the second labelled dataset, and determining a second gradient of the cost function between the second neural network layer and a first set of nodes of a third neural network layer based on back- propagation using the first labelled dataset. The first set of nodes of the third neural network layer correspond to a first plurality of detector classes associated with the first modulation scheme.

A method and system for multiple-input multiple-output (MIMO) detector selection using a neural network is herein disclosed. According to one embodiment, a method includes generating a labelled dataset of features and detector labels, training a multi-layer perceptron (MLP) network using the generated labelled dataset, and selecting a detector class from a plurality of detector classes based on outputs of the trained MLP network.

Transmission quality is improved in an environment in which direct waves dominate in a transmission method for transmitting a plurality of modulated signals from a plurality of antennas at the same time. All data symbols used in data transmission of a modulated signal are precoded by hopping between precoding matrices so that the precoding matrix used to precode each data symbol and the precoding matrices used to precode data symbols that are adjacent to the data symbol in the frequency domain and the time domain all differ. A modulated signal with such data symbols arranged therein is transmitted.