This invention is method of pre-computing the optimal pairing of antennas to RF/ analog frontends in wireless MIMO systems without the need for computationally complex and power consuming searches. Other MIMO antenna selection schemes demand an exponential computational order for an exhaustive search, and slightly less for sub-optimal selection.
This new technique requires zero real-time computations and minimal memory for near-optimal anenna selection.
When mathematically expressing the computation requirements for MIMO systems, the relationship of the number of antennas in the device (N) to number of RF frontends (M) is exponential (NM) for exhaustive searches and semi-linear [(N2M) or (N4M)] for iterative/ sub-optimal searches. An antenna selection algorithm that eliminates significant computational requirements, and requires memory of a linear order, defined by the product of the number of antennas and the number of frontends. Estimating the angle spread concisely encapsulates the sources of distortion and interference as clusters. The algorithm then selects the optimal frontend-antenna pair to compensate for those clusters.
Home/ Office networking and Wi-Fi: Wireless LANs and WANs will benefit from increased range with fewer dropped connections and faster connection speeds, while users of related technologies like wireless voice over internet protocol (VoIP) would enjoy greater mobility and signal clarity.
Bluetooth and other M2M systems: MIMO antenna arrays will be the backbone of countless machine-to-machine devices that rely on intercommunication in order to function. This algorithm would decrease the size of such devices while ensuring continued reliability.
Handheld devices: The market demands connectivity in handheld electronics. Portable gaming devices, for instance, require access to community forums, and pocket PCs are equipped with internet browsers and multimedia software to communicate with desktop computers and wireless routers.
GPS systems: A constant signal is imperative for high-accuracy GPS devices. This suite of improved antenna technologies could provide for the incorporation of GPS systems into a range of portable devices, which are currently inhibited by size, power, or portability.
Cellular communications: These technologies will boost cell phone signal and range, while simultaneously minimizing power consumption and miniaturizing device size.
Large or phased arrays of smart antennas: Antennas can be constructed on a large scale to improve long distance communication in cell phone broadcast towers and government/ military base stations.
Increase number of built-in antennas: Use of angle spread estimation allows wireless devices to receive more antenna elements without a corresponding increase in RF frontends.
No real-time computational requirements: Wireless devices using this technology would benefit from added signal without forfeiting battery life or device size. The algorithm pre-determines the optimal frontend-antenna pair based on total angle spread, avoiding the cumbersome computational bottleneck of exhaustive or iterative searches for the strongest signal. The exponential computational complexity, present in current antenna selection technologies, quickly becomes prohibitive for moderate-to-large antenna arrays in small devices.
Reduce heat load: This algorithm can increase active antenna elements without increasing heat load by eliminating the need for extra processing without increasing number of RF frontends.
Reduce signal distortion and system complexity and increase computational efficiency in digital communications systems with an algorithm that combines the equalization and decoding tasks into one process.
The greatest challenge facing digital communications is the ability to accurately transfer information faster. When dealing with sub-optimal communication channels, digital signals are subject to significant signal degradation (through dispersion and noise) and are often laden with errors. While receiver based equalizers often compensate for dispersion errors, most systems also require additional corrective steps. The addition of a forward error correction step can often help compensate for the limits of equalization, but this requires an additional computation at the receiver which slows the flow of information. This algorithm combines the equalization and error correction coding, used in all digital communication systems, into one process with very low complexity. Combining these two steps results in greater computational efficiency and reduces overall complexity allowing for more efficient Soft-Input, Soft-Output (SISO) transfers. While the concept of joint equalization is well-known, implementations are often highly complicated. Traditional techniques require an exponential increase in complexity to recover from dispersion or signal smearing (2x complexity, where x is the number of periods of smearing).
This method scales linearly with complexity (2x complexity, where x is the number of periods of smearing), leading to a drastic reduction in complexity for error prone applications. In addition to the decrease in complexity, the combination of equalization and error correction utilize computational resources more efficiently. Computational efficiency lowers the total costs associated with digital signal reconstruction in all applications. Specifically this invention uses a Minimum Mean Squared Error (MMSE) equalizer which receives and outputs soft information, i.e. it is a soft-input soft-output (SISO) equalizer. This soft information is exchanged with a SISO decoder, opportune for error correction decoders. The nature of the equalizer permits solutions beyond one-dimensional data streams and for both channels of arbitrary length and for signal constellations of arbitrary size. A SISO MMSE-based iterative equalizer/decoder has been tested for one- and two-dimensional data recording systems, has been successfully tested on real data for wireless communications links, and can be applied to any digital communication system to enhance the speed and accuracy of information transfer.
Wireless Communications: Cellular Phones, Base Stations
Cable Systems: Television, Broadband Digital Microwave Communications: Digital Radio-Relay Links
Low Complexity: Where multi-step equalization and decoding systems tend to scale exponentially, this single step system combines the equalization and decoding tasks into one process allowing it to scale linearly, resulting in less complexity for large systems.
Efficient: Offers greater performance than systems that have separate equalization and decoding steps without sacrificing cost and complexity.
Cost Effective: Implementation is low-cost.
Flexible: Can be used in a wide variety of digital communication systems.