40 research outputs found
Advanced wireless communications using large numbers of transmit antennas and receive nodes
The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. First, we propose practical open-loop and closed-loop training frameworks to reduce the overhead of the downlink training phase. We then discuss efficient CSI quantization techniques using a trellis search. The proposed CSI quantization techniques can be implemented with a complexity that only grows linearly with the number of transmit antennas while the performance is close to the optimal case. We also analyze distributed reception using a large number of geographically separated nodes, a scenario that may become popular with the emergence of the Internet of Things. For distributed reception, we first propose coded distributed diversity to minimize the symbol error probability at the fusion center when the transmitter is equipped with a single antenna. Then we develop efficient receivers at the fusion center using minimal processing overhead at the receive nodes when the transmitter with multiple transmit antennas sends multiple symbols simultaneously using spatial multiplexing
Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays
Massive multiple-input multiple-output (MIMO) systems, which utilize a large
number of antennas at the base station, are expected to enhance network
throughput by enabling improved multiuser MIMO techniques. To deploy many
antennas in reasonable form factors, base stations are expected to employ
antenna arrays in both horizontal and vertical dimensions, which is known as
full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform
planar array (UPA), where antennas are placed in a grid pattern. To exploit the
full benefit of massive MIMO in frequency division duplexing (FDD), the
downlink channel state information (CSI) should be estimated, quantized, and
fed back from the receiver to the transmitter. However, it is difficult to
accurately quantize the channel in a computationally efficient manner due to
the high dimensionality of the massive MIMO channel. In this paper, we develop
both narrowband and wideband CSI quantizers for FD-MIMO taking the properties
of realistic channels and the UPA into consideration. To improve quantization
quality, we focus on not only quantizing dominant radio paths in the channel,
but also combining the quantized beams. We also develop a hierarchical beam
search approach, which scans both vertical and horizontal domains jointly with
moderate computational complexity. Numerical simulations verify that the
performance of the proposed quantizers is better than that of previous CSI
quantization techniques.Comment: 15 pages, 6 figure
On the MISO Channel with Feedback: Can Infinitely Massive Antennas Achieve Infinite Capacity?
We consider communication over a multiple-input single-output (MISO) block
fading channel in the presence of an independent noiseless feedback link. We
assume that the transmitter and receiver have no prior knowledge of the channel
state realizations, but the transmitter and receiver can acquire the channel
state information (CSIT/CSIR) via downlink training and feedback. For this
channel, we show that increasing the number of transmit antennas to infinity
will not achieve an infinite capacity, for a finite channel coherence length
and a finite input constraint on the second or fourth moment. This insight
follows from our new capacity bounds that hold for any linear and nonlinear
coding strategies, and any channel training schemes. In addition to the channel
capacity bounds, we also provide a characterization on the beamforming gain
that is also known as array gain or power gain, at the regime with a large
number of antennas.Comment: This work has been submitted to the IEEE Transactions on Information
Theory. It was presented in part at ISIT201