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On the Capacity of the Multiantenna Gaussian Cognitive Interference Channel

机译:多天线高斯认知干扰信道的容量

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The capacity of the multiantenna Gaussian cognitive interference channel is studied. The cognitive interference channel is a variation of the classical two-users interference channel in which one of the transmitters, the cognitive transmitter, is also provided with the message of the second transmitter, the primary transmitter. We study the capacity of the multiple-input multiple-output Gaussian model, that is the channel in which the inputs are vectors and the outputs are obtained as linear combinations of the channel inputs plus an additive complex Gaussian noise. This channel models a wireless scenario in which transmitters and receivers have multiple antennas. For this channel, we derive capacity to within an additive gap, that is we show that inner and outer bounds to capacity lie to within a constant distance of each other. The gap between the inner and outer bounds depends on the number of antennas at the cognitive receiver and both bounds can be easily evaluated by considering jointly Gaussian inputs. We also derive capacity to within a constant multiplicative factor of two, that is we show that the ratio between inner and outer bound is at most two. The additive gap well-characterizes the capacity at high SNR, while the multiplicative gap is useful at low SNR. We also derive the exact capacity for a subset of the "strong interference" regime: in this subset, the primary transmitter can decode the cognitive message without loss of optimality. This new capacity result extends and generalizes previously known capacity results, in particular, the capacity in the "very strong interference" and the "primary decodes cognitive" regimes.
机译:研究了多天线高斯认知干扰信道的容量。认知干扰信道是经典的两用户干扰信道的变体,其中,发射机之一,即认知发射机,也被提供了第二发射机,即主发射机的消息。我们研究了多输入多输出高斯模型的容量,该模型是输入为矢量的通道,输出是通道输入与加性复杂高斯噪声的线性组合而获得的。该信道模拟了无线场景,其中发射器和接收器具有多个天线。对于此通道,我们将容量推导到加法间隙之内,也就是说,我们表明容量的内边界和外边界在彼此之间的恒定距离内。内边界和外边界之间的间隙取决于认知接收器的天线数量,并且可以通过共同考虑高斯输入来轻松评估两个边界。我们还将容量推导到恒定的乘数2内,也就是说,我们表明内界与外界之比最大为2。加性间隙很好地表征了高SNR时的容量,而乘法间隙则在低SNR时有用。我们还得出“强干扰”机制的子集的精确容量:在该子集中,主要发送器可以解码认知消息而不会失去最优性。该新的容量结果扩展并概括了先前已知的容量结果,特别是“非常强烈的干扰”和“主要解码认知”方案中的容量。

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