Abstract: Embodiments are disclosed for improved systems and methods for encoding and decoding information transmitted using multiple-input-multiple-output (MIMO) communication devices. Structurally sparse codes are implemented based on principles of compressive sampling. Improved detectors are also disclosed for implementing basis pursuit and basis pursuit with runner-up detection techniques.

Abstract: A multi-level coding and iterative decoding scheme using sparse space codes as the inner-code and codes amenable to belief propagation decoding methods (such as low-density parity-check (LDPC) codes, turbo codes, and trellis codes) as the outer-code is proposed for MIMO communication channels.

Abstract: Methods, devices and systems for sensor-based wireless communication systems using compressive sampling are provided. In one embodiment, the method for sampling signals comprises receiving, over a wireless channel, a user equipment transmission based on an S-sparse combination of a set of vectors; down converting and discretizing the received transmission to create a discretized signal; correlating the discretized signal with a set of sense waveforms to create a set of samples, wherein a total number of samples in the set is equal to a total number of sense waveforms in the set, wherein the set of sense waveforms does not match the set of vectors, and wherein the total number of sense waveforms in the set of sense waveforms is fewer than a total number of vectors in the set of vectors; and transmitting at least one sample of the set of samples to a remote central processor.

Abstract: Methods, devices and systems for sensor-based wireless communication systems using compressive sampling are provided. In one embodiment, the method for sampling signals comprises receiving, over a wireless channel, a user equipment transmission based on an S-sparse combination of a set of vectors; down converting and discretizing the received transmission to create a discretized signal; correlating the discretized signal with a set of sense waveforms to create a set of samples, wherein a total number of samples in the set is equal to a total number of sense waveforms in the set, wherein the set of sense waveforms does not match the set of vectors, and wherein the total number of sense waveforms in the set of sense waveforms is fewer than a total number of vectors in the set of vectors; and transmitting at least one sample of the set of samples to a remote central processor.

Abstract: A multi-level coding and iterative decoding scheme using sparse space codes as the inner-code and codes amenable to belief propagation decoding methods (such as low-density parity-check (LDPC) codes, turbo codes, and trellis codes) as the outer-code is proposed for MIMO communication channels.

Abstract: Methods, devices and systems for sensor-based wireless communication systems using compressive sampling are provided. L User Equipments (mobile stations) transmit signals with sparsity S and their signals are compressively sensed to M samples by Z remote samplers (a distributed antenna arrangement) and the uplink channel is estimated by a central processor (the “central brain”). For a given system signal to noise ratio, retained samples M and sparsity S, we approximate the loss in sum mutual information due to imperfect knowledge of the channel. The approximation is premised on a lower bound of the mutual information which accounts for the power in the channel estimation error. Also, throughput results are given for adaptively adjusting the sparsity of multiple users' transmit signals based on channel fading.

Abstract: Methods, devices and systems for sensor-based wireless communication systems using compressive sampling are provided. In one embodiment, the method for sampling signals comprises receiving, over a wireless channel, a user equipment transmission based on an S-sparse combination of a set of vectors; down converting and discretizing the received transmission to create a discretized signal; correlating the discretized signal with a set of sense waveforms to create a set of samples, wherein a total number of samples in the set is equal to a total number of sense waveforms in the set, wherein the set of sense waveforms does not match the set of vectors, and wherein the total number of sense waveforms in the set of sense waveforms is fewer than a total number of vectors in the set of vectors; and transmitting at least one sample of the set of samples to a remote central processor.

Abstract: Methods, devices and systems for sensor-based wireless communication systems using compressive sampling are provided. In one embodiment, the method for sampling signals comprises receiving, over a wireless channel, a user equipment transmission based on an S-sparse combination of a set of vectors; down converting and discretizing the received transmission to create a discretized signal; correlating the discretized signal with a set of sense waveforms to create a set of samples, wherein a total number of samples in the set is equal to a total number of sense waveforms in the set, wherein the set of sense waveforms does not match the set of vectors, and wherein the total number of sense waveforms in the set of sense waveforms is fewer than a total number of vectors in the set of vectors; and transmitting at least one sample of the set of samples to a remote central processor.

Abstract: Methods, devices and systems for sensor-based wireless communication systems using compressive sampling are provided. In one embodiment, the method for sampling signals comprises receiving, over a wireless channel, a user equipment transmission based on an S-sparse combination of a set of vectors; down converting and discretizing the received transmission to create a discretized signal; correlating the discretized signal with a set of sense waveforms to create a set of samples, wherein a total number of samples in the set is equal to a total number of sense waveforms in the set, wherein the set of sense waveforms does not match the set of vectors, and wherein the total number of sense waveforms in the set of sense waveforms is fewer than a total number of vectors in the set of vectors; and transmitting at least one sample of the set of samples to a remote central processor.

Abstract: In a messaging system having a plurality of simulcasting base transmitters (104), a plurality of pseudorandom sequence generators (216) are provided (402) for the plurality of base transmitters. The plurality of pseudorandom sequence generators are arranged (403) to ensure that they generate a plurality of pseudorandom sequences having sub-sequences that are different from one another during concurrent transmissions by the plurality of base transmitters. A cancellation-affecting parameter of the plurality of base transmitters is adjusted (404, 406, 408) in accordance with the plurality of pseudorandom sequences during the simulcast transmission from the plurality of base transmitters to limit intervals of carrier cancellation.

Abstract: A fixed portion (102) of a radio messaging system schedules (602) an inbound message to be transmitted at a first scheduled time on a channel from a portable subscriber unit (122). The channel is monitored (604) at the first scheduled time to receive the inbound message, utilizing a beamforming technique. In response to failing to achieve adequate reception of the inbound message at the first scheduled time, the fixed portion reschedules (608) the inbound message to be transmitted at a second scheduled time, and remonitors (610) the channel at the second scheduled time to receive the inbound message, utilizing an omnidirectional technique.