Abstract:
The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a block least squares function interference filter by generating aggressor kernel matrices from the aggressor signals, augmenting the aggressor kernel matrices by weight factors and executing a linear combination of the augmented output, at an intermediate layer to produce intermediate layer outputs. At an output layer, a linear filter function may be executed on the intermediate layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.
Abstract:
The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a multi-model radial basis function neural network with Hammerstein structure by executing a radial basis function on aggressor signals at a hidden layer of the radial basis function neural network with Hammerstein structure to obtain hidden layer outputs, augmenting aggressor signal(s) by weight factors, infusing the hidden layer outputs by infusion factors, and, executing a linear combination of the augmented output, at an intermediate layer to produce a combined hidden layer outputs. At an output layer, a linear filter function may be executed on the hidden layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.
Abstract:
The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a support vector regression interference filter by generating one or more aggressor kernels, augmenting the one or more kernels by weight factors, and executing a regression function of the augmented components, to produce an estimated jammer signals. At an output layer, estimated jammer signals may be linearly combined to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.
Abstract:
Certain aspects of the present disclosure propose a method for estimating a channel utilizing Sparse Bayesian Learning (SBL) algorithm. The proposed method employs a Basis expansion (e.g., polynomial) channel model, and iteratively performs SBL algorithm to adjust parameters of the channel model.
Abstract:
Systems and methods for interference cancellation at a receiver in a wireless communication system are provided. In one aspect, a method for interference cancellation is provided. The method comprises providing total received chips received from a plurality of cells (2810). The method also comprises successively estimating received chips for each of the plurality of cells in a plurality of iterations (2820), wherein each of the plurality of iterations after a first iteration comprises canceling previously estimated received chips for one or more of the plurality of cells from the total received chips, and estimating received chips for one of the plurality of cells using the total received chips with the previously estimated received chips for the one or more of the plurality of cells cancelled out (2830).
Abstract:
Systems and methods for multi-user detection are provided. In one aspect of the disclosure, an apparatus is provided. The apparatus comprises a processing unit configured to process a plurality of received chips into a plurality of received symbols for a plurality of users. The apparatus further comprises a computation unit configured to compute a multi-user matrix relating a plurality of user symbols for the plurality of users to the plurality of received symbols and a detection unit configured to detect the plurality of user symbols for the plurality of users using the computed multi-user matrix and the plurality of received symbols.
Abstract:
A method for estimating timing in a wireless communication comprises the steps of receiving a plurality of symbol bursts corresponding to a plurality of time slots and selecting a subset of symbols from a first symbol burst of the plurality of symbol bursts. The subset comprises a first midamble symbol. The method further comprises the steps of calculating, for each symbol in the subset, a corresponding midamble estimation error, and determining the lowest calculated midamble estimation error to determine a timing for the first symbol burst. The method further comprises the steps of processing the first symbol burst utilizing the timing determined for the first symbol burst, and processing a second symbol burst of the plurality of symbol bursts utilizing the timing determined for the first symbol burst.
Abstract:
A method for timing and frequency synchronization in a wireless system is provided. The method comprises the steps of receiving a burst of symbols, selecting a subset of the burst of symbols, iteratively adjusting the subset of the burst of symbols by a plurality of timing offsets and calculating, for each timing offset, a first performance metric corresponding to the adjusted subset. The method further comprises the steps of determining one of the plurality of timing offsets to be a preferred timing offset based upon the first performance metric thereof, iteratively rotating the subset of the burst of symbols by a plurality of frequency offsets and calculating, for each frequency offset, a second performance metric corresponding to the rotated subset, and determining one of the plurality of frequency offsets to be a preferred frequency offset based upon the second performance metric thereof.
Abstract:
Techniques for performing adaptive channel estimation are described. A receiver derives channel estimates for a wireless channel based on received pilot symbols and at least one estimation parameter. The receiver updates the at least one estimation parameter based on the received pilot symbols. The at least one estimation parameter may be for an innovations representation model of the wireless channel and may be updated based on a cost function with costs defined by prediction errors. In one design, the receiver derives predicted pilot symbols based on the received pilot symbols and the at least one estimation parameter, determines prediction errors based on the received pilot symbols and the predicted pilot symbols, and further derives error gradients based on the prediction errors. The receiver then updates the at least one estimation parameter based on the error gradients and the prediction errors, e.g., if a stability test is satisfied.
Abstract:
Techniques for recovering a desired transmission in the presence of interfering transmissions are described. For iterative detection and cancellation, multiple groups of code channels are formed for a plurality of code channels for at least one sector. Processing is performed for the multiple groups of code channels in multiple iterations. For each iteration, data detection and signal cancellation are performed for the multiple groups of code channels in multiple stages, e.g., in a sequential order starting with the strongest group to the weakest group. Each stage of each iteration may perform data detection, signal reconstruction, and signal cancellation. Each stage of each iteration may also perform equalization, data detection, signal reconstruction, and signal cancellation.