237 research outputs found
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
Interference-Mitigating Waveform Design for Next-Generation Wireless Systems
A brief historical perspective of the evolution of waveform designs employed in consecutive generations of wireless communications systems is provided, highlighting the range of often conflicting demands on the various waveform characteristics. As the culmination of recent advances in the field the underlying benefits of various Multiple Input Multiple Output (MIMO) schemes are highlighted and exemplified. As an integral part of the appropriate waveform design, cognizance is given to the particular choice of the duplexing scheme used for supporting full-duplex communications and it is demonstrated that Time Division Duplexing (TDD) is substantially outperformed by Frequency Division Duplexing (FDD), unless the TDD scheme is combined with further sophisticated scheduling, MIMOs and/or adaptive modulation/coding. It is also argued that the specific choice of the Direct-Sequence (DS) spreading codes invoked in DS-CDMA predetermines the properties of the system. It is demonstrated that a specifically designed family of spreading codes exhibits a so-called interference-free window (IFW) and hence the resultant system is capable of outperforming its standardised counterpart employing classic Orthogonal Variable Spreading Factor (OVSF) codes under realistic dispersive channel conditions, provided that the interfering multi-user and multipath components arrive within this IFW. This condition may be ensured with the aid of quasisynchronous adaptive timing advance control. However, a limitation of the system is that the number of spreading codes exhibiting a certain IFW is limited, although this problem may be mitigated with the aid of novel code design principles, employing a combination of several spreading sequences in the time-frequency and spatial-domain. The paper is concluded by quantifying the achievable user load of a UTRA-like TDD Code Division Multiple Access (CDMA) system employing Loosely Synchronized (LS) spreading codes exhibiting an IFW in comparison to that of its counterpart using OVSF codes. Both system's performance is enhanced using beamforming MIMOs
Compressive Sensing Based Channel Feedback Protocols for Spatially-Correlated Massive Antenna Arrays
Incorporating wireless transceivers with numerous antennas (such as Massive-MIMO) is a prospective way to increase the link capacity or enhance the energy efficiency of future communication systems. However, the benefits of such approach can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large with so many antennas, the feedback is arduous in practice, especially for frequency division duplexing (FDD) systems. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing, which permits the transmitter to obtain channel information with acceptable accuracy under substantially reduced feedback load. Furthermore, by leveraging properties of compressive sensing, we present two adaptive feedback protocols, in which the feedback content can be dynamically configured based on channel conditions to improve the efficiency.Engineering and Applied Science
A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback
Channel state information (CSI) plays a critical role in achieving the
potential benefits of massive multiple input multiple output (MIMO) systems. In
frequency division duplex (FDD) massive MIMO systems, the base station (BS)
relies on sustained and accurate CSI feedback from the users. However, due to
the large number of antennas and users being served in massive MIMO systems,
feedback overhead can become a bottleneck. In this paper, we propose a
model-driven deep learning method for CSI feedback, called learnable
optimization and regularization algorithm (LORA). Instead of using l1-norm as
the regularization term, a learnable regularization module is introduced in
LORA to automatically adapt to the characteristics of CSI. We unfold the
conventional iterative shrinkage-thresholding algorithm (ISTA) to a neural
network and learn both the optimization process and regularization term by
end-toend training. We show that LORA improves the CSI feedback accuracy and
speed. Besides, a novel learnable quantization method and the corresponding
training scheme are proposed, and it is shown that LORA can operate
successfully at different bit rates, providing flexibility in terms of the CSI
feedback overhead. Various realistic scenarios are considered to demonstrate
the effectiveness and robustness of LORA through numerical simulations
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