Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications

A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a block sparsity adaptive matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions.Comment: submitted to IEEE Transactions on Vehicular Technology [Major Revision

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

Low-Complexity Block Coordinate Descend Based Multiuser Detection for Uplink Grant-Free NOMA

Grant-free non-orthogonal multiple access (NOMA) scheme is considered as a promising candidate for the enabling of massive connectivity and reduced signalling overhead for Internet of Things (IoT) applications in massive machine-type communication (mMTC) networks. Exploiting the inherent nature of sporadic transmissions in the grant-free NOMA systems, compressed sensing based multiuser detection (CS-MUD) has been deemed as a powerful solution to user activity detection (UAD) and data detection (DD). In this paper, block coordinate descend (BCD) method is employed in CS-MUD to reduce the computational complexity. We propose two modified BCD based algorithms, called enhanced BCD (EBCD) and complexity reduction enhanced BCD (CR-EBCD), respectively. To be specific, by incorporating a novel candidate set pruning mechanism into the original BCD framework, our proposed EBCD algorithm achieves remarkable CS-MUD performance improvement. In addition, the proposed CR-EBCD algorithm further ameliorates the proposed EBCD by eliminating the redundant matrix multiplications during the iteration process. As a consequence, compared with the proposed EBCD algorithm, our proposed CR-EBCD algorithm enjoys two orders of magnitude complexity saving without any CS-MUD performance degradation, rendering it a viable solution for future mMTC scenarios. Extensive simulation results demonstrate the bound-approaching performance as well as ultra-low computational complexity

사물 통신에서 압축 센싱을 이용한 대규모 연결방법에 대한 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 이광복.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.사물 인터넷 (Internet of Things, IoT) 시대의 도래와 함께, 대규모 사물 통신(massive machine-type communications, mMTC)은 차세대 무선 통신 표준의 주요 요구 사항들 중의 하나가 되었다. 대규모 사물 통신 환경에서는 많은 수의 사물 기기(machine-type device)들이 대부분 비활성 상태로 데이터를 전송하지 않다가 가끔씩 활성 상태로 전환되어 작은 크기의 데이터를 전송한다. 그러므로, 기지국(base station, BS)으로부터의 스케쥴링을 기반으로 직교(orthogonal) 시간/주파수 자원을 할당받은 후 데이터 송수신이 이루어지는 기존의 통신 방식은, 실제 전송하려는 데이터 대비 많은 부가적인 제어 정보를 필요로 하고 또한 데이터의 지연을 유발시키므로 대규모 사물 통신에 적합하지 않다. 대신, 전송단에서는 스케쥴링 없이, 즉 기지국으로 부터의 승인 없이(grant-free), 비직교 자원에 다중 접속하고(non-orthogonal multiple access, NOMA), 수신단에서는 다중 사용자 검출(multi-user detection, MUD)을 이용하여 데이터의 충돌을 복조해 내는 방식이 대규모 사물 통신에 적합하다. 이 때, 사물 기기들이 전송하는 데이터의 희소 특성을 감안하면, 압축 센싱 기반의 다중 사용자 검출 방법(compressive sensing-based multi-user detection, CS-MUD)이 일반적인 다중 사용자 검출 방법보다 더 좋은 성능을 발휘할 수 있다. 본 논문에서는, 기존 방식보다 더 좋은 성능을 가진 새로운 압축 센싱 기반의 다중 사용자 검출 방법을 제안한다. 부연 설명하면, 가장 큰 사후 활성 확률을 가진 사용자를 찾고(maximum a posteriori probability-based active user detection, MAP-AUD), 역시 사후 확률 관점에서 가장 확률이 높은 데이터를 추정한다(maximum a posteriori probability-based data detection, MAP-DD). 이 때, MAP-AUD와 MAP-DD 블록은 서로 외재적 정보(extrinsic information)만을 주고 받는데, 이 외재적 정보는 상대방의 사전 정보가 되고, 이 사전 정보를 이용하여 다시 사후 확률 관점에서 최적의 해를 구한다. 이러한 반복 수행을 통해 각 블록은 검출의 정확도와 성능을 높여 나간다. 이 방법은 패킷 단위로 확장될 수 있다. 각각의 사물 기기들이 전송하려는 데이터는 여러개의 심볼로 구성된 패킷이며, 한 패킷 내의 각각의 심볼은 공통된 활성도를 가지게 된다. 여기서, 이 공통된 활성도를 이용하면, 활성 사물 기기와 이들의 전송 데이터 추정의 정확도를 높일 수 있다. 하지만, 이는 공동 최적화(joint optimization) 문제로 매우 복잡한 연산을 필요로 한다. 본 논문에서는, 패킷 내의 임의의 하나의 심볼에서 추정된 사물 기기의 활성도는 다른 심볼의 활성도를 추정하는데 사전 정보(a priori information)로 이용될 수 있다는 점에 착안하여, 복잡한 공동 최적화 문제를 비교적 연산량이 적은 부분 최적화(subproblem optimization) 문제로 단순화 시키고, 이들 간에 메시지 전달 (massage-passing) 기법을 통해 공동 최적화의 해에 근접한 해를 구하는 방법을 제안한다. 이 때, 부분 최적화 문제의 해법이 바로 앞에 설명한 MAP-AUD/MAP-DD 방법이다. 마지막으로, 모의 실험을 통해 제안하는 방법이 기존 방법과 비교했을 때 매우 크게 성능이 향상됨을 보였다. 특히, 제안하는 방법은 전체 사용자 수 대비 이용 가능한 자원이 적을 때일수록 더 큰 성능 향상이 있는데, 이는 차세대 무선 통신에서 사물 통신이 고려하는 단위 면적당 사물 기기의 수(10^6개/km^2)를 고려했을 때, 제안하는 방법이 대규모 사물 통신에 아주 효용성이 큼을 보여준다.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

Massive Access in Media Modulation Based Massive Machine-Type Communications

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive MIMO base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit a doubly structured sparsity in both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed algorithms in both uncoded and coded cases. Also, our results verify the validity of the proposed data-aided CSI update strategy.Comment: Accepted by IEEE Transactions on Wireless Communications. The codes and some other materials about this work may be available at https://gaozhen16.github.i

Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing

Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing with SE problem are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e.g., Compressively Sensed Measurements). This study proposes a novel approach for learning such a mapping from a training set. To accomplish this objective, the Convolutional Support Estimator Networks (CSENs), each with a compact configuration, are designed. The proposed CSEN can be a crucial tool for the following scenarios: (i) Real-time and low-cost support estimation can be applied in any mobile and low-power edge device for anomaly localization, simultaneous face recognition, etc. (ii) CSEN's output can directly be used as "prior information" which improves the performance of sparse signal recovery algorithms. The results over the benchmark datasets show that state-of-the-art performance levels can be achieved by the proposed approach with a significantly reduced computational complexity

Active Terminal Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA-OTFS in LEO Satellite Internet-of-Things

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial-satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.Comment: 20 pages, 9 figures, accepted by IEEE Transactions on Wireless Communication