14 research outputs found
Downlink Precoding for Massive MIMO Systems Exploiting Virtual Channel Model Sparsity
In this paper, the problem of designing a forward link linear precoder for
Massive Multiple-Input Multiple-Output (MIMO) systems in conjunction with
Quadrature Amplitude Modulation (QAM) is addressed. First, we employ a novel
and efficient methodology that allows for a sparse representation of multiple
users and groups in a fashion similar to Joint Spatial Division and
Multiplexing. Then, the method is generalized to include Orthogonal Frequency
Division Multiplexing (OFDM) for frequency selective channels, resulting in
Combined Frequency and Spatial Division and Multiplexing, a configuration that
offers high flexibility in Massive MIMO systems. A challenge in such system
design is to consider finite alphabet inputs, especially with larger
constellation sizes such as . The proposed methodology is next
applied jointly with the complexity-reducing Per-Group Processing (PGP)
technique, on a per user group basis, in conjunction with QAM modulation and in
simulations, for constellation size up to . We show by numerical results
that the precoders developed offer significantly better performance than the
configuration with no precoder or the plain beamformer and with
Linear Precoding for MIMO Channels with QAM Constellations and Reduced Complexity
In this paper, the problem of designing a linear precoder for Multiple-Input
Multiple-Output (MIMO) systems in conjunction with Quadrature Amplitude
Modulation (QAM) is addressed. First, a novel and efficient methodology to
evaluate the input-output mutual information for a general Multiple-Input
Multiple-Output (MIMO) system as well as its corresponding gradients is
presented, based on the Gauss-Hermite quadrature rule. Then, the method is
exploited in a block coordinate gradient ascent optimization process to
determine the globally optimal linear precoder with respect to the MIMO
input-output mutual information for QAM systems with relatively moderate MIMO
channel sizes. The proposed methodology is next applied in conjunction with the
complexity-reducing per-group processing (PGP) technique, which is
semi-optimal, to both perfect channel state information at the transmitter
(CSIT) as well as statistical channel state information (SCSI) scenarios, with
high transmitting and receiving antenna size, and for constellation size up to
. We show by numerical results that the precoders developed offer
significantly better performance than the configuration with no precoder, and
the maximum diversity precoder for QAM with constellation sizes , and
and for MIMO channel size
Optimized Iterative (Turbo) Reception for QAM OFDM with CFO over Unknown Double-Selective Channels
A novel iterative (turbo) receiver is introduced, suitable for orthogonal frequency division multiplexing (OFDM) employing quadrature amplitude modulation (QAM) and receiver diversity. The system operates over a double-selective channel and includes a carrier frequency offset (CFO). We propose a maximum a posteriori probability expectation-maximization (MAP-EM) receiver with a different EM parameter division than standard methods. In such standard MAP-EM receivers, the E-step parameters correspond to the channel, while the M-step parameters correspond to the CFO and data symbols. This standard receiver parameter division results into a highly complex receiver for QAM, due to the large modulated symbol alphabet size, and the non-constant constellation symbol amplitude. In this paper, a new receiver framework introduces a different parameter division that leads to reduced complexity turbo receivers for QAM signaling, while still achieving close to optimal system performance. The new approach adapts the sum-product algorithm (SPA) parameter framework to the MAP-EM receiver. Thus, in the new receiver framework, the E-step parameters are data symbols, while the M-step parameters are the channel and the CFO. We evaluate the performance of the proposed receiver with and without automatic repeat request (ARQ), where in the former case packet combining applies to further improve performance
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Linear Precoding for MIMO Channels with QAM Constellations and Reduced Complexity
In this paper, the problem of designing a linear precoder for Multiple-Input
Multiple-Output (MIMO) systems in conjunction with Quadrature Amplitude
Modulation (QAM) is addressed. First, a novel and efficient methodology to
evaluate the input-output mutual information for a general Multiple-Input
Multiple-Output (MIMO) system as well as its corresponding gradients is
presented, based on the Gauss-Hermite quadrature rule. Then, the method is
exploited in a block coordinate gradient ascent optimization process to
determine the globally optimal linear precoder with respect to the MIMO
input-output mutual information for QAM systems with relatively moderate MIMO
channel sizes. The proposed methodology is next applied in conjunction with the
complexity-reducing per-group processing (PGP) technique, which is
semi-optimal, to both perfect channel state information at the transmitter
(CSIT) as well as statistical channel state information (SCSI) scenarios, with
high transmitting and receiving antenna size, and for constellation size up to
. We show by numerical results that the precoders developed offer
significantly better performance than the configuration with no precoder, and
the maximum diversity precoder for QAM with constellation sizes , and
and for MIMO channel size