9,549 research outputs found
Generalized Spatial Modulation in Large-Scale Multiuser MIMO Systems
Generalized spatial modulation (GSM) uses transmit antenna elements but
fewer transmit radio frequency (RF) chains, . Spatial modulation (SM)
and spatial multiplexing are special cases of GSM with and
, respectively. In GSM, in addition to conveying information bits
through conventional modulation symbols (for example, QAM), the
indices of the active transmit antennas also convey information bits.
In this paper, we investigate {\em GSM for large-scale multiuser MIMO
communications on the uplink}. Our contributions in this paper include: ()
an average bit error probability (ABEP) analysis for maximum-likelihood
detection in multiuser GSM-MIMO on the uplink, where we derive an upper bound
on the ABEP, and () low-complexity algorithms for GSM-MIMO signal detection
and channel estimation at the base station receiver based on message passing.
The analytical upper bounds on the ABEP are found to be tight at moderate to
high signal-to-noise ratios (SNR). The proposed receiver algorithms are found
to scale very well in complexity while achieving near-optimal performance in
large dimensions. Simulation results show that, for the same spectral
efficiency, multiuser GSM-MIMO can outperform multiuser SM-MIMO as well as
conventional multiuser MIMO, by about 2 to 9 dB at a bit error rate of
. Such SNR gains in GSM-MIMO compared to SM-MIMO and conventional MIMO
can be attributed to the fact that, because of a larger number of spatial index
bits, GSM-MIMO can use a lower-order QAM alphabet which is more power
efficient.Comment: IEEE Trans. on Wireless Communications, accepte
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
Asynchronous Channel Training in Multi-Cell Massive MIMO
Pilot contamination has been regarded as the main bottleneck in time division
duplexing (TDD) multi-cell massive multiple-input multiple-output (MIMO)
systems. The pilot contamination problem cannot be addressed with large-scale
antenna arrays. We provide a novel asynchronous channel training scheme to
obtain precise channel matrices without the cooperation of base stations. The
scheme takes advantage of sampling diversity by inducing intentional timing
mismatch. Then, the linear minimum mean square error (LMMSE) estimator and the
zero-forcing (ZF) estimator are designed. Moreover, we derive the minimum
square error (MSE) upper bound of the ZF estimator. In addition, we propose the
equally-divided delay scheme which under certain conditions is the optimal
solution to minimize the MSE of the ZF estimator employing the identity matrix
as pilot matrix. We calculate the uplink achievable rate using maximum ratio
combining (MRC) to compare asynchronous and synchronous channel training
schemes. Finally, simulation results demonstrate that the asynchronous channel
estimation scheme can greatly reduce the harmful effect of pilot contamination
Efficient space-frequency block coded pilot-aided channel estimation method for multiple-input-multiple-output orthogonal frequency division multiplexing systems over mobile frequency-selective fading channels
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.An iterative pilot-aided channel estimation technique for space-frequency block coded (SFBC) multiple-input multiple-output orthogonal frequency division multiplexing systems is proposed. Traditionally, when channel estimation techniques are utilised, the SFBC information signals are decoded one block at a time. In the proposed algorithm, multiple blocks of SFBC information signals are decoded simultaneously. The proposed channel estimation method can thus significantly reduce the amount of time required to decode information signals compared to similar channel estimation methods proposed in the literature. The proposed method is based on the maximum likelihood approach that offers linearity and simplicity of implementation. An expression for the pairwise error probability (PEP) is derived based on the estimated channel. The derived PEP is then used to determine the optimal power allocation for the pilot sequence. The performance of the proposed algorithm is demonstrated in high frequency selective channels, for different number of pilot symbols, using different modulation schemes. The algorithm is also tested under different levels of Doppler shift and for different number of transmit and receive antennas. The results show that the proposed scheme minimises the error margin between slow and high speed receivers compared to similar channel estimation methods in the literature.Peer reviewe
Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach
A key challenge of massive MTC (mMTC), is the joint detection of device
activity and decoding of data. The sparse characteristics of mMTC makes
compressed sensing (CS) approaches a promising solution to the device detection
problem. However, utilizing CS-based approaches for device detection along with
channel estimation, and using the acquired estimates for coherent data
transmission is suboptimal, especially when the goal is to convey only a few
bits of data.
First, we focus on the coherent transmission and demonstrate that it is
possible to obtain more accurate channel state information by combining
conventional estimators with CS-based techniques. Moreover, we illustrate that
even simple power control techniques can enhance the device detection
performance in mMTC setups.
Second, we devise a new non-coherent transmission scheme for mMTC and
specifically for grant-free random access. We design an algorithm that jointly
detects device activity along with embedded information bits. The approach
leverages elements from the approximate message passing (AMP) algorithm, and
exploits the structured sparsity introduced by the non-coherent transmission
scheme. Our analysis reveals that the proposed approach has superior
performance compared to application of the original AMP approach.Comment: Submitted to IEEE Transactions on Communication
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