Abstract: Certain embodiments herein relate to scheduling interference between wireless devices in a manner that reduced interference associated with full duplex communication. Signal strength and interference information associated with communication between wireless devices may be collected and forwarded to a processing device, such as a central controller. The central controller may utilize such information to generate a conflict graph that depicts interference relationships between the wireless devices. Using the conflict graph, the central controller may determine whether requested communications may be scheduled along with concurrently scheduled communications without resulting in interference affecting the communications. In one embodiment, a requested communication may be scheduled with other concurrently scheduled communications if the aggregated throughput is increased with the addition of the requested communication.

Abstract: A method for self-interference cancellation in a wireless communication device. The wireless communication device has an estimator, a transmitter and a cancellation device. The estimator is configured to estimate a known signal received from a second wireless communication device when the second wireless communication device is utilized less than a predetermined threshold. The transmitter is configured to transmit within a coherence time of the estimated known signal, a predetermined signal. The cancellation device is configured to cancel the estimated known signal from a received signal.

Abstract: A technology that is operable to schedule data transfer for a multiple user multiple-input and multiple-output (MU-MIMO) communications network is disclosed. In one embodiment, an enhanced node B (eNode B) is configured with circuitry configured to receive a sounding signal from each of a plurality of user equipment (UEs). One or more major paths of the sounding signals from each of the plurality of UEs are determined. An angle of arrival (AoA) is determined that is associated with each of the one or more major paths. The plurality of UEs are grouped into one or more candidate MU-MIMO sets using the AoAs associated with each of the one or more major paths. Data transmissions are scheduled for one or more of the candidate UEs of the candidate MU-MIMO set on one or more of the major paths of each of the candidate UEs.

Abstract: Certain embodiments herein relate to scheduling interference between wireless devices in a manner that reduced interference associated with full duplex communication. Signal strength and interference information associated with communication between wireless devices may be collected and forwarded to a processing device, such as a central controller. The central controller may utilize such information to generate a conflict graph that depicts interference relationships between the wireless devices. Using the conflict graph, the central controller may determine whether requested communications may be scheduled along with concurrently scheduled communications without resulting in interference affecting the communications. In one embodiment, a requested communication may be scheduled with other concurrently scheduled communications if the aggregated throughput is increased with the addition of the requested communication.

Abstract: A low-complexity optimal soft MIMO detector is provided for a general spatial multiplexing (SM) systems with two transmit and NR receive antennas. The computational complexity of the proposed scheme is independent from the operating signal-to-noise ratio (SNR) and grows linearly with the constellation order. It provides the optimal maximum likelihood (ML) solution through the introduction of an efficient Log-likelihood ratio (LLR) calculation method, avoiding the exhaustive search over all possible nodes. The intrinsic parallelism makes it an appropriate option for implementation on DSPs, FPGAs, or ASICs. In specific, this MIMO detection architecture is very suitable to be applied in WiMax receivers based on IEEE 802.16e/m in both downlink (subscriber station) and uplink (base station).

Abstract: A low-complexity optimal soft MIMO detector is provided for a general spatial multiplexing (SM) systems with two transmit and NR receive antennas. The computational complexity of the proposed scheme is independent from the operating signal-to-noise ratio (SNR) and grows linearly with the constellation order. It provides the optimal maximum likelihood (ML) solution through the introduction of an efficient Log-likelihood ratio (LLR) calculation method, avoiding the exhaustive search over all possible nodes. The intrinsic parallelism makes it an appropriate option for implementation on DSPs, FPGAs, or ASICs. In specific, this MIMO detection architecture is very suitable to be applied in WiMax receivers based on IEEE 802.16e/m in both downlink (subscriber station) and uplink (base station).

Abstract: A low-complexity optimal soft MIMO detector is provided for a general spatial multiplexing (SM) systems with two transmit and NR receive antennas. The computational complexity of the proposed scheme is independent from the operating signal-to-noise ratio (SNR) and grows linearly with the constellation order. It provides the optimal maximum likelihood (ML) solution through the introduction of an efficient Log-likelihood ratio (LLR) calculation method, avoiding the exhaustive search over all possible nodes. The intrinsic parallelism makes it an appropriate option for implementation on DSPs, FPGAs, or ASICs. In specific, this MIMO detection architecture is very suitable to be applied in WiMax receivers based on IEEE 802.16e/m in both downlink (subscriber station) and uplink (base station).

Abstract: A low-complexity optimal soft MIMO detector is provided for a general spatial multiplexing (SM) systems with two transmit and NR receive antennas. The computational complexity of the proposed scheme is independent from the operating signal-to-noise ratio (SNR) and grows linearly with the constellation order. It provides the optimal maximum likelihood (ML) solution through the introduction of an efficient Log-likelihood ratio (LLR) calculation method, avoiding the exhaustive search over all possible nodes. The intrinsic parallelism makes it an appropriate option for implementation on DSPs, FPGAs, or ASICs. In specific, this MIMO detection architecture is very suitable to be applied in WiMax receivers based on IEEE 802.16e/m in both downlink (subscriber station) and uplink (base station).