121 research outputs found
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
New communication standards need to deal with machine-to-machine
communications, in which users may start or stop transmitting at any time in an
asynchronous manner. Thus, the number of users is an unknown and time-varying
parameter that needs to be accurately estimated in order to properly recover
the symbols transmitted by all users in the system. In this paper, we address
the problem of joint channel parameter and data estimation in a multiuser
communication channel in which the number of transmitters is not known. For
that purpose, we develop the infinite factorial finite state machine model, a
Bayesian nonparametric model based on the Markov Indian buffet that allows for
an unbounded number of transmitters with arbitrary channel length. We propose
an inference algorithm that makes use of slice sampling and particle Gibbs with
ancestor sampling. Our approach is fully blind as it does not require a prior
channel estimation step, prior knowledge of the number of transmitters, or any
signaling information. Our experimental results, loosely based on the LTE
random access channel, show that the proposed approach can effectively recover
the data-generating process for a wide range of scenarios, with varying number
of transmitters, number of receivers, constellation order, channel length, and
signal-to-noise ratio.Comment: 15 pages, 15 figure
Nonorthogonal Multiple Access for 5G and Beyond
This work was
supported in part by the U.K. Engineering and Physical Sciences Research Council
(EPSRC) under Grant EP/N029720/1 and Grant EP/N029720/2. The work of
L. Hanzo was supported by the ERC Advanced Fellow Grant Beam-me-up
The Road to Next-Generation Multiple Access: A 50-Year Tutorial Review
The evolution of wireless communications has been significantly influenced by
remarkable advancements in multiple access (MA) technologies over the past five
decades, shaping the landscape of modern connectivity. Within this context, a
comprehensive tutorial review is presented, focusing on representative MA
techniques developed over the past 50 years. The following areas are explored:
i) The foundational principles and information-theoretic capacity limits of
power-domain non-orthogonal multiple access (NOMA) are characterized, along
with its extension to multiple-input multiple-output (MIMO)-NOMA. ii) Several
MA transmission schemes exploiting the spatial domain are investigated,
encompassing both conventional space-division multiple access (SDMA)/MIMO-NOMA
systems and near-field MA systems utilizing spherical-wave propagation models.
iii) The application of NOMA to integrated sensing and communications (ISAC)
systems is studied. This includes an introduction to typical NOMA-based
downlink/uplink ISAC frameworks, followed by an evaluation of their performance
limits using a mutual information (MI)-based analytical framework. iv) Major
issues and research opportunities associated with the integration of MA with
other emerging technologies are identified to facilitate MA in next-generation
networks, i.e., next-generation multiple access (NGMA). Throughout the paper,
promising directions are highlighted to inspire future research endeavors in
the realm of MA and NGMA.Comment: 43 pages, 38 figures; Submitted to Proceedings of the IEE
Multiple Access for Massive Machine Type Communications
The internet we have known thus far has been an internet of people, as it has connected people with one another. However, these connections are forecasted to occupy only a minuscule of future communications. The internet of tomorrow is indeed: the internet of things. The Internet of Things (IoT) promises to improve all aspects of life by connecting everything to everything. An enormous amount of effort is being exerted to turn these visions into a reality. Sensors and actuators will communicate and operate in an automated fashion with no or minimal human intervention. In the current literature, these sensors and actuators are referred to as machines, and the communication amongst these machines is referred to as Machine to Machine (M2M) communication or Machine-Type Communication (MTC). As IoT requires a seamless mode of communication that is available anywhere and anytime, wireless communications will be one of the key enabling technologies for IoT. In existing wireless cellular networks, users with data to transmit first need to request channel access. All access requests are processed by a central unit that in return either grants or denies the access request. Once granted access, users' data transmissions are non-overlapping and interference free. However, as the number of IoT devices is forecasted to be in the order of hundreds of millions, if not billions, in the near future, the access channels of existing cellular networks are predicted to suffer from severe congestion and, thus, incur unpredictable latencies in the system. On the other hand, in random access, users with data to transmit will access the channel in an uncoordinated and probabilistic fashion, thus, requiring little or no signalling overhead. However, this reduction in overhead is at the expense of reliability and efficiency due to the interference caused by contending users. In most existing random access schemes, packets are lost when they experience interference from other packets transmitted over the same resources. Moreover, most existing random access schemes are best-effort schemes with almost no Quality of Service (QoS) guarantees. In this thesis, we investigate the performance of different random access schemes in different settings to resolve the problem of the massive access of IoT devices with diverse QoS guarantees. First, we take a step towards re-designing existing random access protocols such that they are more practical and more efficient. For many years, researchers have adopted the collision channel model in random access schemes: a collision is the event of two or more users transmitting over the same time-frequency resources. In the event of a collision, all the involved data is lost, and users need to retransmit their information. However, in practice, data can be recovered even in the presence of interference provided that the power of the signal is sufficiently larger than the power of the noise and the power of the interference. Based on this, we re-define the event of collision as the event of the interference power exceeding a pre-determined threshold. We propose a new analytical framework to compute the probability of packet recovery failure inspired by error control codes on graph. We optimize the random access parameters based on evolution strategies. Our results show a significant improvement in performance in terms of reliability and efficiency. Next, we focus on supporting the heterogeneous IoT applications and accommodating their diverse latency and reliability requirements in a unified access scheme. We propose a multi-stage approach where each group of applications transmits in different stages with different probabilities. We propose a new analytical framework to compute the probability of packet recovery failure for each group in each stage. We also optimize the random access parameters using evolution strategies. Our results show that our proposed scheme can outperform coordinated access schemes of existing cellular networks when the number of users is very large. Finally, we investigate random non-orthogonal multiple access schemes that are known to achieve a higher spectrum efficiency and are known to support higher loads. In our proposed scheme, user detection and channel estimation are carried out via pilot sequences that are transmitted simultaneously with the user's data. Here, a collision event is defined as the event of two or more users selecting the same pilot sequence. All collisions are regarded as interference to the remaining users. We first study the distribution of the interference power and derive its expression. Then, we use this expression to derive simple yet accurate analytical bounds on the throughput and outage probability of the proposed scheme. We consider both joint decoding as well as successive interference cancellation. We show that the proposed scheme is especially useful in the case of short packet transmission
Study of Robust Adaptive Beamforming Algorithms Based on Power Method Processing and Spatial Spectrum Matching
Robust adaptive beamforming (RAB) based on interference-plus-noise covariance
(INC) matrix reconstruction can experience performance degradation when model
mismatch errors exist, particularly when the input signal-to-noise ratio (SNR)
is large. In this work, we devise an efficient RAB technique for dealing with
covariance matrix reconstruction issues. The proposed method involves INC
matrix reconstruction using an idea in which the power and the steering vector
of the interferences are estimated based on the power method. Furthermore,
spatial match processing is computed to reconstruct the desired
signal-plus-noise covariance matrix. Then, the noise components are excluded to
retain the desired signal (DS) covariance matrix. A key feature of the proposed
technique is to avoid eigenvalue decomposition of the INC matrix to obtain the
dominant power of the interference-plus-noise region. Moreover, the INC
reconstruction is carried out according to the definition of the theoretical
INC matrix. Simulation results are shown and discussed to verify the
effectiveness of the proposed method against existing approaches.Comment: 7 pages, 2 figure
NON-ORTHOGONAL MULTIPLE ACCESS: A COMPREHENSIVE ANALYTICAL STUDY AND OPTIMISATION IN FADING CHANNELS
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