Various technical and economic factors have led to a desire to increase communication throughput within a given frequency bandwidth. Several techniques have been developed. One approach, for example, utilizes higher order signal modulation formats such as 8 Phase Shift Keying (8PSK) and Quadrature Amplitude Modulation (QAM) to obtain greater bandwidth efficiency. Such modulation formats maximize the data transmitted in a given bandwidth, resulting in increased bandwidth efficiency. These modulation formats are often characterized by the number of bits per Hertz, e.g., Binary Phase Shift Keying (BPSK) has ½ bit/Hz. Quadrature Phase Shift Keying (QPSK) has 1 bit/Hz, and 8PSK has 1.5 bits/Hz, and 16 QAM has 2 bits/Hz. For example, for a given bandwidth 16 QAM can communicate 4 times more bits than BPSK. One limitation of higher order modulation is increased stringency on transmitter linearity resulting in transmitter power backoff requirements that reduce signal power for receiver detection and prompt the development of linearizers to allow operation closer to transmitter saturated output levels.
Other techniques for maximizing bandwidth efficiency and increasing communication throughput focus on maintaining isolation of independent data, for example, by polarization, geographic location, or frequency sub-band. Polarization techniques utilize orthogonally polarized antennas. Each orthogonal polarization communicates independent data streams to double the throughput. Sufficient isolation afforded by orthogonal polarization allows communication without cochannel interference but imposes stringent antenna design requirements to achieve and maintain orthogonal polarizations. According to geographic or spatial techniques, coverage areas served by antennas are broken into spatially isolated regions. Different spatially isolated antenna regions may re-use the same bandwidth to communicate independent data streams, thus, increasing communication throughput. In this case, stringent requirements on antenna sidelobe levels are necessary to avoid mutual interference. One frequency-based technique is frequency division multiple access, or FDMA. Each individual user in a FDMA scheme is assigned one or more subbands, but sufficient frequency separation between adjacent subbands is required to avoid mutual interference. All of these isolation-based techniques increase data throughput, but impose stringent isolation requirements that are achieved by passive design techniques so that communication to individual users is not degraded, for example, due to co-channel interference (CCI) or other interference.
Other techniques for maximizing bandwidth efficiency and increasing communication throughput use adaptive subtraction to allow two users to superimpose data streams onto the same bandwidth resulting in a composite signal. Upon reception of the composite signal containing both data streams, each user adaptively subtracts a time delayed adjusted replica of their respectively transmitted signal to receive the other user's data stream. In this way, the communication throughput is doubled. Commercial embodiments of this technique exist including, for example, AST's DOUBLETALK and ViaSat's PCMA (Paired Carrier Multiple Access). Such techniques allow reuse of the same frequency bandwidth, but are limited in that they require that uplink signals originate and terminate at the same users' locations so that an adaptively subtracted time delay of one user's signal can be used to obtain the other user's signal. Further, the composite signal cannot be broadcast to multiple receiving sites because signal replicas must be available at all users' sites to perform the required adaptive subtraction. (See FIG. 8).
Yet other techniques for maximizing bandwidth efficiency and increasing communication throughput utilize signal separation to separate data streams whose spectral content (or bandwidth) partially or completely overlap (e.g., blind signal separation). These blind signal separation techniques separate superimposed signals based on, for example, spectral differences or statistical independence between data streams. Blind signal separation techniques have been applied to a variety of problems, including communications, geophysical exploration, image processing and biological applications. Examples of signal separation techniques include maximum likelihood techniques, maximum a priori techniques, and higher order statistical approaches. Such signal separation techniques can be very useful, but are most effective only when the signals or streams to be separated exhibit the statistical independence and/or spectral differences that a selected signal separation technique is configured to detect.