Matrix Factorization Based Blind Bayesian Receiver for Grant-Free Random Access in mmWave MIMO mMTC

Grant-free random access is promising for massive connectivity with sporadic transmissions in massive machine type communications (mMTC), where the hand-shaking between the access point (AP) and users is skipped, leading to high access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this paper, to further reduce the overhead and latency, we explore the problem of grant-free random access without the use of pilot signals in a millimeter wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation and signal detection (UACESD). We show that the blind joint UACESD can be formulated as a constrained composite matrix factorization problem, which can be solved by exploiting the structures of the channel matrix and signal matrix. Leveraging our recently developed unitary approximate message passing based matrix factorization (UAMP-MF) algorithm, we design a message passing based Bayesian algorithm to solve the blind joint UACESD problem. Extensive simulation results demonstrate the effectiveness of the blind grant-free random access scheme

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

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

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Sparse Message Passing Based Preamble Estimation for Crowded M2M Communications

Due to the massive number of devices in the M2M communication era, new challenges have been brought to the existing random-access (RA) mechanism, such as severe preamble collisions and resource block (RB) wastes. To address these problems, a novel sparse message passing (SMP) algorithm is proposed, based on a factor graph on which Bernoulli messages are updated. The SMP enables an accurate estimation on the activity of the devices and the identity of the preamble chosen by each active device. Aided by the estimation, the RB efficiency for the uplink data transmission can be improved, especially among the collided devices. In addition, an analytical tool is derived to analyze the iterative evolution and convergence of the SMP algorithm. Finally, numerical simulations are provided to verify the validity of our analytical results and the significant improvement of the proposed SMP on estimation error rate even when preamble collision occurs.Comment: submitted to ICC 2018 with 6 pages and 4 figure

대규모 사물 통신을 위한 압축센싱 기반 다중 사용자 검출

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 이광복.Massive machine-type communication (mMTC) is a newly introduced service category in 5G wireless communication systems to support a variety of Internet-of-Things (IoT) applications. In the mMTC network, a large portion of devices is inactive and hence does not transmit data. Thus, the transmit vector consisting of data symbols of both active and inactive devices can be readily modeled as a sparse vector. In recovering sparsely represented multi-user vectors, compressed sensing based multi-user detection (CS-MUD) can be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal multiple access (NOMA) environments. In this dissertation, two novel techniques regarding CS-MUD for mMTC networks are proposed. In the first part of the dissertation, the sparsity-aware ordered successive interference cancellation (SA-OSIC) technique is proposed. In CS-MUD, multi-user vectors are detected based on a sparsity-aware maximum a posteriori probability (S-MAP) criterion. To reduce the computational complexity of S-MAP detection, sparsity-aware successive interference cancellation (SA-SIC) can be used. SA-SIC is a simple low-complexity scheme that recovers transmit symbols in a sequential manner. However, SA-SIC does not perform well without proper layer sorting due to error propagation. When multi-user vectors are sparse and each device is active with a distinct probability, the detection order determined solely by channel gains might not be optimal. In this dissertation, to reduce the error propagation and enhance the performance of SA-SIC, an activity-aware sorted QR decomposition (A-SQRD) algorithm that finds the optimal detection order is proposed. The proposed technique finds the optimal detection order based on the activity probabilities and channel gains of machine-type devices. Numerical results verify that the proposed technique greatly improves the performance of SA-SIC. In the second part of the dissertation, the expectation propagation based joint AUD and CE (EP-AUD/CE) technique is proposed. In several studies regarding CS-MUD, the uplink channel state information (CSI) from the MTD to the BS is assumed to be perfectly known to the BS. In practice, however, the uplink CSI from the devices to the BS should be estimated before data detection. To address this issue, various joint active user detection (AUD) and channel estimation (CE) schemes have been proposed. Since only a few devices are active at one time, an element-wise (i.e., Hadamard) product of the binary activity pattern and the channel vector is also a sparse vector and thus compressed sensing (CS)-based technique is a good fit for the problem at hand. One potential shortcoming in these studies is that a prior distribution of the sparse vector is not exploited. In fact, these studies are based on the non-Bayesian greedy algorithms such as the orthogonal matching pursuit (OMP) and approximate message passing (AMP) algorithms, which do not require a prior distribution of the sparse vector. In essence, these algorithms find out non-zero values based on the instantaneous correlation between the sensing matrix and the observation vector so that they might not be effective in the situation where the prior distribution is available. In this case, clearly, by exploiting the statistical distribution of the sparse vector, the performance of AUD and CE can be improved substantially. The proposed technique finds the best approximation of the posterior distribution of the sparse channel vector based on the expectation propagation (EP) algorithm. Using the approximate distribution, AUD and CE are jointly performed. Numerical simulations show that the proposed technique substantially enhances AUD and CE performances over competing algorithms.대규모 사물 통신(massive machine-type communications, mMTC)은 다양한 사물 인터넷(internet of things, IoT) 서비스를 지원하기 위해 차세대 무선 통신 표준에 새로 도입된 서비스 범주이다. 대규모 사물 통신 환경에서는 많은 수의 사물 기기(machine-type device, MTD)가 대부분의 타임 슬롯(time slot)에서 비활성 상태이며 데이터를 전송하지 않는다. 따라서, 활성 및 비활성 기기 모두의 데이터 심볼로 구성된 전송 벡터는 희소(sparse) 벡터로 표현될 수 있다. 희소 벡터로 표현된 다중 사용자 벡터를 복원하기 위해, 압축 센싱 기반 다중 사용자 검출(CS-MUD)이 사용될 수 있다. CS-MUD는 스케줄링(scheduling) 절차가 없는 상향링크(uplink) 비직교 다중 접속(non-orthogonal multiple access, NOMA)을 위한 핵심 기술 중 하나이다. 본 학위 논문에서는 대규모 사물 통신을 위한 새로운 CS-MUD 기술들을 제안한다. 논문의 첫 번째 부분에서는, 희소성을 고려한 정렬 순차적 간섭 제거(sparsity-aware ordered successive interference cancellation, SA-OSIC) 기술을 제안한다. CS-MUD에서 다중 사용자 벡터는 희소성을 고려한 최대 사후 확률(sparsity-aware maximum a posteriori probability, S-MAP) 기준에 따라 검출된다. S-MAP 검출의 계산 복잡성을 줄이기 위해 희소성을 고려한 순차적 간섭 제거(sparsity-aware successive interference cancellation, SA-SIC)를 사용할 수 있다. 희소 데이터 벡터 검출의 계산 복잡성을 줄이기 위해 사용되는 희소성 고려 연속 간섭 제거 기술은 오류 전파(error propagation)로 인해 적절한 사용자 정렬 없이는 성능이 좋지 않다. 다중 사용자 벡터가 희소 벡터이고 각 기기가 다른 확률로 활성일 경우 채널 이득(channel gain)에 의해서만 결정된 사용자 검출 순서는 최적이 아닐 수 있다. 본 논문에서는, 오류 전파를 줄이고 희소성 고려 연속 간섭 제거의 성능을 향상하기 위해 각 사물 기기의 활성 확률(activity probability)과 채널 이득을 기반으로 최적의 검출 순서를 찾는 새로운 알고리즘을 제안한다. 시뮬레이션을 통해 제안한 검출 순서 정렬 기술은 데이터 검출의 성능을 크게 향상함을 검증하였다. 논문의 두 번째 부분에서는, 기댓값 전파 기반 활성 사용자 검출 및 채널 추정(expectation propagation based active user detection channel estimation, EP-AUD/CE) 기술을 제안한다. CS-MUD에 관한 몇몇 연구에서, 각 사물 기기로부터 기지국(base station, BS)으로의 상향링크 채널 상태 정보(channel state information, CSI)는 기지국에 완전히 알려져 있다고 가정된다. 그러나 실제로는, 데이터 검출 전에 각 기기로부터 기지국으로의 상향링크 채널 상태 정보를 추정해야 한다. 이 문제를 해결하기 위해 다양한 활성 사용자 검출(active user detection, AUD) 및 채널 추정(channel estimation, CE) 기술이 제안되었다. 대규모 사물 통신에서는 하나의 타임 슬롯에 적은 수의 장치만 활성화되기 때문에 이진(binary)값으로 이루어진 활성 여부 벡터와 채널 벡터의 곱은 희소 벡터가 되어 압축센싱 알고리즘으로 복원이 가능하다. 하지만, 이러한 연구들의 단점 중 하나는 희소 벡터의 사전 분포(prior distritubion)가 활용되지 않는다는 것이다. 희소 벡터의 통계적 사전 분포를 이용하면 활성 사용자 검출 및 채널 추정의 성능을 크게 향상할 수 있다. 본 학위 논문에서는, 기댓값 전파(expectation propagation, EP) 알고리즘을 이용해 희소 채널 벡터의 사후 분포(posterior distribution)의 근사 분포를 찾고, 해당 근사 분포를 이용하여 활성 사용자 검출과 채널 추정을 동시에 수행하는 기술을 제안한다. 시뮬레이션을 통해 제안한 사용자 검출 및 채널 추정 기술은 활성 사용자 검출 및 채널 추정의 성능을 상당히 향상함을 검증하였다.1 Introduction . . . . . 1 1.1 Sparsity-Aware Ordered Successive Interference Cancellation . . . . . 3 1.2 Expectation Propagation-based Joint Active User Detection and Channel Estimation . . . . . 4 2 Sparsity-Aware Ordered Successive Interference Cancellation . . . . . 7 2.1 System model . . . . . 7 2.2 Sparsity-Aware Successive Interference Cancellation (SA-SIC) . . . . . 9 2.2.1 Derivation of S-MAP Detection . . . . . 9 2.2.2 Sparsity-Aware SIC (SA-SIC) Detection . . . . . 10 2.3 Proposed Activity-Aware Sorted-QRD (A-SQRD) Algorithm . . . . . 11 2.4 Complexity Analysis . . . . . 15 2.5 Numerical Results . . . . . 15 2.5.1 Simulation Setup . . . . . 16 2.5.2 Simulation Results . . . . . 20 3 Expectation Propagation-based Joint Active User Detection and Channel Estimation . . . . . 21 3.1 System model . . . . . 21 3.2 Joint Active User Detection and Channel Estimation . . . . . 23 3.3 EP-Based Active User Detection and Channel Estimation . . . . . 26 3.3.1 A Brief Review of Expectation Propagation . . . . . 29 3.3.2 Form of the Approximation . . . . . 30 3.3.3 Iterative EP Update Rules . . . . . 31 3.3.4 Active User Detection and Channel Estimation . . . . . 36 3.3.5 Data Detection . . . . . 37 3.3.6 Comments on Complexity . . . . . 38 3.4 Simulation Results and Discussions . . . . . 39 3.4.1 Simulation Setup . . . . . 39 3.4.2 Simulation Results . . . . . 52 4 Conclusion . . . . . 54 Abstract (In Korean) . . . . . 60Docto