21 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
Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network
We consider the multi-user detection (MUD) problem in uplink grant-free
non-orthogonal multiple access (NOMA), where the access point has to identify
the total number and correct identity of the active Internet of Things (IoT)
devices and decode their transmitted data. We assume that IoT devices use
complex spreading sequences and transmit information in a random-access manner
following the burst-sparsity model, where some IoT devices transmit their data
in multiple adjacent time slots with a high probability, while others transmit
only once during a frame. Exploiting the temporal correlation, we propose an
attention-based bidirectional long short-term memory (BiLSTM) network to solve
the MUD problem. The BiLSTM network creates a pattern of the device activation
history using forward and reverse pass LSTMs, whereas the attention mechanism
provides essential context to the device activation points. By doing so, a
hierarchical pathway is followed for detecting active devices in a grant-free
scenario. Then, by utilising the complex spreading sequences, blind data
detection for the estimated active devices is performed. The proposed framework
does not require prior knowledge of device sparsity levels and channels for
performing MUD. The results show that the proposed network achieves better
performance compared to existing benchmark schemes
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry
Massive machine-type communications (mMTC) is a crucial scenario to support
booming Internet of Things (IoTs) applications. In mMTC, although a large
number of devices are registered to an access point (AP), very few of them are
active with uplink short packet transmission at the same time, which requires
novel design of protocols and receivers to enable efficient data transmission
and accurate multi-user detection (MUD). Aiming at this problem, grant-free
non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA,
active devices can directly transmit their preambles and data symbols
altogether within one time frame, without grant from the AP. Compressive
sensing (CS)-based receivers are adopted for non-orthogonal preambles
(NOP)-based MUD, and successive interference cancellation is exploited to
decode the superimposed data signals. In this paper, we model, analyze, and
optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an
aspect of network deployment. Based on the SG network model, we first analyze
the success probability as well as the channel estimation error of the CS-based
MUD in the preamble phase and then analyze the average aggregate data rate in
the data phase. As IoT applications highly demands low energy consumption, low
infrastructure cost, and flexible deployment, we optimize the energy efficiency
and AP coverage efficiency of GF-NOMA via numerical methods. The validity of
our analysis is verified via Monte Carlo simulations. Simulation results also
show that CS-based GF-NOMA with NOP yields better MUD and data rate
performances than contention-based GF-NOMA with orthogonal preambles and
CS-based grant-free orthogonal multiple access.Comment: This paper is submitted to IEEE Internet Of Things Journa
μ¬λ¬Ό ν΅μ μμ μμΆ μΌμ±μ μ΄μ©ν λκ·λͺ¨ μ°κ²°λ°©λ²μ λν μ°κ΅¬
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ 보곡νλΆ, 2019. 2. μ΄κ΄λ³΅.Massive machine-type communication (mMTC) has become one of the most important requirements for next generation (5G) communication systems with the advent of the Internet-of-Things (IoT). In the mMTC scenarios, grant-free non-orthogonal multiple access (NOMA) on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are promising because many users sporadically transmit small data packets at low rates. In this dissertation, we propose a novel CS-MUD algorithm for active user and data detection for the mMTC systems. The proposed scheme consists of a MAP-based active user detector (MAP-AUD) and a MAP-based data detector (MAP-DD). By exchanging extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the performance of the active user detection and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm demonstrates dramatically improved performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms.μ¬λ¬Ό μΈν°λ· (Internet of Things, IoT) μλμ λλμ ν¨κ», λκ·λͺ¨ μ¬λ¬Ό ν΅μ (massive machine-type communications, mMTC)μ μ°¨μΈλ 무μ ν΅μ νμ€μ μ£Όμ μꡬ μ¬νλ€ μ€μ νλκ° λμλ€. λκ·λͺ¨ μ¬λ¬Ό ν΅μ νκ²½μμλ λ§μ μμ μ¬λ¬Ό κΈ°κΈ°(machine-type device)λ€μ΄ λλΆλΆ λΉνμ± μνλ‘ λ°μ΄ν°λ₯Ό μ μ‘νμ§ μλ€κ° κ°λμ© νμ± μνλ‘ μ νλμ΄ μμ ν¬κΈ°μ λ°μ΄ν°λ₯Ό μ μ‘νλ€. κ·Έλ¬λ―λ‘, κΈ°μ§κ΅(base station, BS)μΌλ‘λΆν°μ μ€μΌμ₯΄λ§μ κΈ°λ°μΌλ‘ μ§κ΅(orthogonal) μκ°/μ£Όνμ μμμ ν λΉλ°μ ν λ°μ΄ν° μ‘μμ μ΄ μ΄λ£¨μ΄μ§λ κΈ°μ‘΄μ ν΅μ λ°©μμ, μ€μ μ μ‘νλ €λ λ°μ΄ν° λλΉ λ§μ λΆκ°μ μΈ μ μ΄ μ 보λ₯Ό νμλ‘ νκ³ λν λ°μ΄ν°μ μ§μ°μ μ λ°μν€λ―λ‘ λκ·λͺ¨ μ¬λ¬Ό ν΅μ μ μ ν©νμ§ μλ€. λμ , μ μ‘λ¨μμλ μ€μΌμ₯΄λ§ μμ΄, μ¦ κΈ°μ§κ΅μΌλ‘ λΆν°μ μΉμΈ μμ΄(grant-free), λΉμ§κ΅ μμμ λ€μ€ μ μνκ³ (non-orthogonal multiple access, NOMA), μμ λ¨μμλ λ€μ€ μ¬μ©μ κ²μΆ(multi-user detection, MUD)μ μ΄μ©νμ¬ λ°μ΄ν°μ μΆ©λμ λ³΅μ‘°ν΄ λ΄λ λ°©μμ΄ λκ·λͺ¨ μ¬λ¬Ό ν΅μ μ μ ν©νλ€. μ΄ λ, μ¬λ¬Ό κΈ°κΈ°λ€μ΄ μ μ‘νλ λ°μ΄ν°μ ν¬μ νΉμ±μ κ°μνλ©΄, μμΆ μΌμ± κΈ°λ°μ λ€μ€ μ¬μ©μ κ²μΆ λ°©λ²(compressive sensing-based multi-user detection, CS-MUD)μ΄ μΌλ°μ μΈ λ€μ€ μ¬μ©μ κ²μΆ λ°©λ²λ³΄λ€ λ μ’μ μ±λ₯μ λ°νν μ μλ€.
λ³Έ λ
Όλ¬Έμμλ, κΈ°μ‘΄ λ°©μλ³΄λ€ λ μ’μ μ±λ₯μ κ°μ§ μλ‘μ΄ μμΆ μΌμ± κΈ°λ°μ λ€μ€ μ¬μ©μ κ²μΆ λ°©λ²μ μ μνλ€. λΆμ° μ€λͺ
νλ©΄, κ°μ₯ ν° μ¬ν νμ± νλ₯ μ κ°μ§ μ¬μ©μλ₯Ό μ°Ύκ³ (maximum a posteriori probability-based active user detection, MAP-AUD), μμ μ¬ν νλ₯ κ΄μ μμ κ°μ₯ νλ₯ μ΄ λμ λ°μ΄ν°λ₯Ό μΆμ νλ€(maximum a posteriori probability-based data detection, MAP-DD). μ΄ λ, MAP-AUDμ MAP-DD λΈλ‘μ μλ‘ μΈμ¬μ μ 보(extrinsic information)λ§μ μ£Όκ³ λ°λλ°, μ΄ μΈμ¬μ μ 보λ μλλ°©μ μ¬μ μ λ³΄κ° λκ³ , μ΄ μ¬μ μ 보λ₯Ό μ΄μ©νμ¬ λ€μ μ¬ν νλ₯ κ΄μ μμ μ΅μ μ ν΄λ₯Ό ꡬνλ€. μ΄λ¬ν λ°λ³΅ μνμ ν΅ν΄ κ° λΈλ‘μ κ²μΆμ μ νλμ μ±λ₯μ λμ¬ λκ°λ€.
μ΄ λ°©λ²μ ν¨ν· λ¨μλ‘ νμ₯λ μ μλ€. κ°κ°μ μ¬λ¬Ό κΈ°κΈ°λ€μ΄ μ μ‘νλ €λ λ°μ΄ν°λ μ¬λ¬κ°μ μ¬λ³Όλ‘ ꡬμ±λ ν¨ν·μ΄λ©°, ν ν¨ν· λ΄μ κ°κ°μ μ¬λ³Όμ 곡ν΅λ νμ±λλ₯Ό κ°μ§κ² λλ€. μ¬κΈ°μ, μ΄ κ³΅ν΅λ νμ±λλ₯Ό μ΄μ©νλ©΄, νμ± μ¬λ¬Ό κΈ°κΈ°μ μ΄λ€μ μ μ‘ λ°μ΄ν° μΆμ μ μ νλλ₯Ό λμΌ μ μλ€. νμ§λ§, μ΄λ 곡λ μ΅μ ν(joint optimization) λ¬Έμ λ‘ λ§€μ° λ³΅μ‘ν μ°μ°μ νμλ‘ νλ€. λ³Έ λ
Όλ¬Έμμλ, ν¨ν· λ΄μ μμμ νλμ μ¬λ³Όμμ μΆμ λ μ¬λ¬Ό κΈ°κΈ°μ νμ±λλ λ€λ₯Έ μ¬λ³Όμ νμ±λλ₯Ό μΆμ νλλ° μ¬μ μ 보(a priori information)λ‘ μ΄μ©λ μ μλ€λ μ μ μ°©μνμ¬, 볡μ‘ν 곡λ μ΅μ ν λ¬Έμ λ₯Ό λΉκ΅μ μ°μ°λμ΄ μ μ λΆλΆ μ΅μ ν(subproblem optimization) λ¬Έμ λ‘ λ¨μν μν€κ³ , μ΄λ€ κ°μ λ©μμ§ μ λ¬ (massage-passing) κΈ°λ²μ ν΅ν΄ 곡λ μ΅μ νμ ν΄μ κ·Όμ ν ν΄λ₯Ό ꡬνλ λ°©λ²μ μ μνλ€. μ΄ λ, λΆλΆ μ΅μ ν λ¬Έμ μ ν΄λ²μ΄ λ°λ‘ μμ μ€λͺ
ν MAP-AUD/MAP-DD λ°©λ²μ΄λ€.
λ§μ§λ§μΌλ‘, λͺ¨μ μ€νμ ν΅ν΄ μ μνλ λ°©λ²μ΄ κΈ°μ‘΄ λ°©λ²κ³Ό λΉκ΅νμ λ λ§€μ° ν¬κ² μ±λ₯μ΄ ν₯μλ¨μ 보μλ€. νΉν, μ μνλ λ°©λ²μ μ 체 μ¬μ©μ μ λλΉ μ΄μ© κ°λ₯ν μμμ΄ μ μ λμΌμλ‘ λ ν° μ±λ₯ ν₯μμ΄ μλλ°, μ΄λ μ°¨μΈλ 무μ ν΅μ μμ μ¬λ¬Ό ν΅μ μ΄ κ³ λ €νλ λ¨μ λ©΄μ λΉ μ¬λ¬Ό κΈ°κΈ°μ μ(10^6κ°/km^2)λ₯Ό κ³ λ €νμ λ, μ μνλ λ°©λ²μ΄ λκ·λͺ¨ μ¬λ¬Ό ν΅μ μ μμ£Ό ν¨μ©μ±μ΄ νΌμ 보μ¬μ€λ€.Contents
Abstract i
Contents ii
List of Tables iv
List of Figures v
1 Introduction 1
2 MAP-based Active User and Data Detection 7
2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 MAP-based Active User and Data Detection . . . . . . . . . . . . . . 9
2.2.1 Activity Log-Likelihood Ratios . . . . . . . . . . . . . . . . 11
2.2.2 MAP-based Active User Detection . . . . . . . . . . . . . . . 12
2.2.3 MAP-based Data Detection . . . . . . . . . . . . . . . . . . 16
2.2.4 Inversion of Covariance Matrices . . . . . . . . . . . . . . . 20
2.2.5 Comments on Complexity . . . . . . . . . . . . . . . . . . . 23
3 Group Sparsity-Aware Active User and Data Detection 28
3.1 Extraction of Extrinsic User Activity Information . . . . . . . . . . . 28
3.2 Modified Active User and Data Detection . . . . . . . . . . . . . . . 32
4 Numerical Results 35
4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5 Conclusion 55
Abstract (In Korean) 61
Acknowlegement 63Docto