Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A method and apparatus is disclosed herein for scheduling, load balancing and/or pilot assignment in MIMD cellular deployments (e.g., reciprority-based MIMO cellular deployments). In one embodiment, the method comprises receiving, by the central controller from at least one AP, for at least one user terminal, a size of a user set and user rate information indicative of a rate that can be provided to the user terminal when served in a group of one or more user terminals; and determining, by the central controller, based on user rate information and the user set size, an allocation of AP resources among the user terminals for the at least one AP.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A method and apparatus is disclosed herein for internal relative transceiver calibration.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A method and apparatus is disclosed herein for scheduling, load balancing and/or pilot assignment in MIMD cellular deployments (e.g., reciprority-based MIMO cellular deployments).

Abstract: A system for coordinating a WLAN including MIMO WAP nodes supporting WLAN communications with an associated set of station nodes on a shared OFDM communication channel. A primary beam coordinator aggregates channel state information (CSI) for communication links and crosslinks between each WAP node and both associated and non-associated station nodes and extrapolates therefrom spatially distinct primary beam pattern setup options for each of the WAP nodes to enable substantially non-interfering concurrent downlink communications between each WAP node and a corresponding subset of its associated station nodes. The WAP nodes select a corresponding one of the primary beam options together with its associated stations as provided by the primary beam coordinator and generate the corresponding selected primary beam option for subsequent downlink communications to the associated subset of station nodes.

Abstract: A method and apparatus is disclosed herein for performing wireless communication.

Abstract: A source channel encoder, source channel decoder and methods for implementing such devices are disclosed herein. The source channel encoder includes a linear transform encoder configured to generate a plurality of source components. A successive refinement quantizer is configured to generate a plurality of bit planes based on the source components. A systematic linear encoder configured to map the bit planes into channel-encoded symbols. The linear transform encoder may be configured to apply a Discrete Cosine Transform (DCT) or a Discrete Wavelet Transform (DWT). The linear transform encoder may be configured for differential encoding.

Abstract: A method and apparatus is disclosed herein for internal relative transceiver calibration.

Abstract: A system, a method and an apparatus are disclosed herein for receiver terminal driven joint encoder and decoder mode adaptation for SU-MIMO transmission. In one embodiment, the system comprises a transmitter that is able to be directed to transmit wireless signals over a multiple-input, multiple output (MIMO) system using any of a plurality of encoding modes; and a user terminal having a receiver comprising a decoding module which has a plurality of decoding modes, each of which can be used to decode communications from the transmitter received over a multiple-input, multiple output (MIMO) channel, wherein at least one encoding mode is operable with multiple of the decoding modes.

Abstract: Time and phase synchronization may enable distributed multiuser multiple-input multiple-output (MIMO) architectures and techniques, such as supported by the IEEE 802.11n standard, where several access points are connected to a central server and operate as a large distributed multi-antenna access point. The phase of all access points can be locked using a common reference (e.g., a synchronization tone) broadcasted over the air in conjunction with a predictive filter (e.g., a Kalman filter) which closely tracks the phase drift for each subcarrier channel.

Abstract: A method and apparatus is disclosed herein for performing wireless communication.

Abstract: A method and apparatus is disclosed herein for performing wireless communication.

Abstract: Each wireless access point in a network may transmit pilot bursts; receive pilot bursts; determine the timing, carrier frequency, and/or sampling frequency of each received pilot burst; determine an offset between the timing, carrier frequency, and/or sampling frequency of each received pilot burst and its own transmitted signals; and deliver one or more of these determined offsets to a computer server. The computer server may receive these offsets and, based on them, compute and deliver to each wireless access point a timing, carrier frequency, and/or sampling frequency correction factor. Each wireless access point may adjust its transmitted timing, carrier frequency, and/or sampling frequency in accordance with these correction factors.