Device activity detection is one main challenge in grant-free random access, which is recently proposed to support massive access for massive machine-type communications (mMTC). Existing solutions fail to consider interference generated by massive Internet of Things (IoT) devices, or important prior information on device activities and interference. In this paper, we consider device activity detection at an access point (AP) in the presence of interference generated by massive devices from other cells. We consider the joint maximum likelihood (ML) estimation and the joint maximum a posterior probability (MAP) estimation of both the device activities and interference powers, jointly utilizing tools from probability, stochastic geometry and optimization. Each estimation problem is a difference of convex (DC) programming problem, and a coordinate descent algorithm is proposed to obtain a stationary point. The proposed ML estimation extends the existing ML estimation by considering the estimation of interference powers together with the estimation of device activities. The proposed MAP estimation further enhances the proposed ML estimation by exploiting prior distributions of device activities and interference powers. Numerical results show the substantial gains of the proposed joint estimation designs, and reveal the importance of explicit consideration of interference and the value of prior information in device activity detection.
The design of highly scalable multiple access schemes is a main challenge in the evolution towards future massive machine-type communications, where reliability and latency constraints must be ensured to a large number of uncoordinated devices. In this scenario, coded random access (CRA) schemes, where successive interference cancellation algorithms allow large improvements with respect to classical random access protocols, have recently attracted an increasing interest. Impressive performance can be potentially obtained by combining CRA with massive multiple input multiple output (MIMO). In t…
Device activity detection is one main challenge in grant-free massive access, which is recently proposed to support massive machine-type communications (mMTC). Existing solutions for device activity detection fail to consider inter-cell interference generated by massive IoT devices or important prior information on device activities and inter-cell interference. In this paper, given different numbers of observations and network parameters, we consider both non-cooperative device activity detection and cooperative device activity detection in a multi-cell network, consisting of many access point…
Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of things (IoT), mainly concentrate on narrow band systems under flat fading. However, little is known about massive grant-free access for wideband systems under frequency-selective fading. This paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive grant-free access scheme. Then, we propose two different but equivalent models for the received pilot si…
Grant-free transmission is an important feature to be supported by future wireless networks since it reduces the signalling overhead caused by conventional grant-based schemes. However, for grant-free transmission, the number of users admitted to the same channel is not caped, which can lead to a failure of multi-user detection. This paper proposes non-orthogonal multiple-access (NOMA) assisted semi-grant-free (SGF) transmission, which is a compromise between grant-free and grant-based schemes. In particular, instead of reserving channels either for grant-based users or grant-free users, we fo…
Contention-based grant-free transmission is very promising for future massive machine-type communication (mMTC). In contention-based transmission, the random pilot collision is a challenging problem. To solve this problem, multiple pilots scheme is used to reduce the pilot collision probabliltiy. However, the existing work on multiple pilots relies on the low correlation of spatial channels, limiting its applicability. In this paper, an independent multi-pilot scheme is proposed, which utilizes the diversity of multiple pilots and is not limited by the spatial correlation. The receiver employs…
Massive machine-type communication (MTC) is expected to play a key role in supporting Internet of Things (IoT) applications such as smart cities, smart factory, and connected vehicles through cellular networks. MTC is characterized by a large number of MTC devices and their sparse activities, which are difficult to be supported by conventional approaches and motivate the design of new access technologies. In particular, in the 5th generation (5G), grant-free or 2-step random access schemes are introduced for MTC to be more efficient by reducing signaling overhead. In this article, we first int…
Due to the limited number of traditional orthogonal pilots, pilot collision will severely degrade the performance of contention-based grant-free transmission. To alleviate the pilot collision and exploit the spatial degree of freedom as much as possible, an extremely sparse orthogonal pilot scheme is proposed for uplink grant-free transmission. The proposed sparse pilot is used to perform active user detection and estimate the spatial channel. Then, inter-user interference suppression is performed by spatially combining the received data symbols using the estimated spatial channel. After that,…
In this paper, we consider a massive uncoordinated non-orthogonal multiple access (NOMA) scheme where devices have strict latency requirements and no retransmission opportunities are available. Each device chooses a pilot sequence from a predetermined set as its signature and transmits its selected pilot and data simultaneously. A collision occurs when two or more devices choose the same pilot sequence. Collisions are treated as interference to the remaining received signals. We consider successive joint decoding (SJD) and successive interference cancellation (SIC) under a Rayleigh fading and …
In this paper, a novel blind multi-user detection(MUD) framework for autonomous grant-free high-overloading non-orthogonal multiple access is introduced in detail aimed at fulfilling the requirements of fifth-generation massive Machine Type Communications. From the perspective of the transmitter side, pros and cons regarding diverse types of emerging grant-free transmission, particularly autonomous grant-free, are elaborated and presented in a comparative manner. In the receiver end,code word-level successive interference cancellation (CL-SIC) is revealed as the main framework to perform MUD. …
Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as unknown identity of active users and multi-user interference (MUI) on shared access resources. In this work, we …
Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable …
To support machine-type communication (MTC), massive multiple-input multiple-output (MIMO) has been considered for grant-free random access. In general, the performance of grant-free random access with massive MIMO is limited by the number of preambles and the number of active devices. In particular, when there are a number of active devices transmitting data packets simultaneously, the signal-to-interference-plus-noise ratio (SINR) cannot be high enough for successful decoding. In this paper, in order to improve performance, we consider immediate re-transmissions for an active device that has…
Grant-free (GF) transmission holds promise in terms of low latency communication by directly transmitting messages without waiting for any permissions. However, collision situations may frequently happen when limited spectrum is occupied by numerous GF users. The non-orthogonal multiple access (NOMA) technique can be a promising solution to achieve massive connectivity and fewer collisions for GF transmission by multiplexing users in power domain. We utilize a semi-grant-free (semi-GF) NOMA scheme for enhancing network connectivity and spectral efficiency by enabling grant-based (GB) and GF us…
In this paper, we propose a cognitive radio-inspired rate-splitting multiple access (CR-RSMA) scheme to assist semi-grant-free (SGF) transmissions in which a grant-based user (GBU) and multiple grant-free users (GFUs) access the base-station (BS) by sharing the same resource block. Using the cognitive radio principle, the GBU and admitted GFU are treated as the primary and secondary users, respectively, and rate-splitting is applied at the admitted GFU to realize SGF transmissions. The admitted GFU's transmit power allocation, target rate allocation, and successive interference cancellation de…
Radio access management plays a vital role in delay and energy consumption of connected devices. The radio access in existing cellular networks is unable to efficiently support massive connectivity, due to its signaling overhead. In this paper, we investigate an asynchronous grant-free narrowband data transmission protocol that aims to provide low energy consumption and delay, by relaxing the synchronization/reservation requirement at the cost of sending several packet copies at the transmitter side and more complex signal processing at the receiver side. Specifically, the timing and frequency…
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 sy…
In this paper, we generate a transmit power pool (PP) for Internet of things (IoT) networks with semi-grant-free non-orthogonal multiple access (SGF-NOMA) via multi-agent deep reinforcement learning (MA-DRL) to enable open loop power control (PC). The PP is mapped with each resource block (RB) to achieve distributed power control (DPC). We first formulate the resource allocation problem as stochastic Markov game, and then solve it using two MA-DRL algorithms, namely double deep Q network (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the optimal transmit power level and RB…
Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulat…
Many real-world scenarios for massive machine-type communication involve sensors monitoring a physical phenomenon. As a consequence, the activity pattern of these sensors will be correlated. In this letter, we study how the correlation of user activities can be exploited to improve detection performance in grant-free random access systems where the users transmit pilot-hopping sequences and the detection is performed based on the received energy. We show that we can expect considerable performance gains by adding regularizers, which take the activity correlation into account, to the non-negati…
A Deep Learning-based Receiver for Asynchronous Grant-Free Random Access in Control-to-Control Networks
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche（信息工程系，波兰马尔凯大学）；CNIT/WiLab, DEI, University of Bologna（CNIT/WiLab，博洛尼亚大学信息工程学院）；FixIA s.r.l.
Low-power wide-area network (LPWAN) technologies are gaining momentum for internet-of-things (IoT) applications since they promise wide coverage to a massive number of battery-operated devices using grant-free medium access. LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has emerged as the widely adopted LPWAN solution. By using chirp spread spectrum modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers coverage to wide-area applications while supporting high-density of devices. However, thus far its scalability performance has been inadequately model…
In this work we treat the unsourced random access problem on a Rayleigh block-fading AWGN channel with multiple receive antennas. Specifically, we consider the slowly fading scenario where the coherence block-length is large compared to the number of active users and the message can be transmitted in one coherence block. Unsourced random access refers to a form of grant-free random access where users are considered to be a-priori indistinguishable and the receiver recovers a list of transmitted messages up to permutation. In this work we show that, when the coherence block length is large enou…
This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA), which enables next-generation ultra-reliable low-latency communications (URLLC) with massive devices. Specifically, the active devices transmit their uplink access signals in a grant-free manner to realize ultra-low access latency. Meanwhile, the base station aims to achieve ultra-reliable data detection under severe inter-device interference without exploiting explicit channel state information (CSI). We first propose an efficient transmitter design, where a small amount of reference inf…
This paper considers the support of grant-free massive access and solves the challenge of active user detection and channel estimation in the case of a massive number of users. By exploiting the sparsity of user activities, the concerned problems are formulated as a compressive sensing problem, whose solution is acquired by approximate message passing (AMP) algorithm. Considering the cooperation of multiple access points, for the deployment of AMP algorithm, we compare two processing paradigms, cloud computing and fog computing, in terms of their effectiveness in guaranteeing ultra reliable lo…
The main challenge of massive machine-type communications (mMTC) is the joint activity and signal detection of devices. The mMTC scenario with many devices transmitting data intermittently at low data rates and via very short packets enables its modelling as a sparse signal processing problem. In this work, we consider a grant-free system and propose a detection and decoding scheme that jointly detects activity and signals of devices. The proposed scheme consists of a list detection technique, an $l_0$-norm regularized activity-aware recursive least-squares algorithm, and an iterative detectio…
Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scalable access for a large number of devices. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. Particularly, the two key tasks in massive access systems, namely, user activity detection and data detection, were handled separately in most existing studies, which ignored the common sparsity pattern in the received pilot and data signal. Mor…
Unsourced random-access (U-RA) is a type of grant-free random access with a virtually unlimited number of users, of which only a certain number $K_a$ are active on the same time slot. Users employ exactly the same codebook, and the task of the receiver is to decode the list of transmitted messages. Recently a concatenated coding construction for U-RA on the AWGN channel was presented, in which a sparse regression code (SPARC) is used as an inner code to create an effective outer OR-channel. Then an outer code is used to resolve the multiple-access interference in the OR-MAC. In this work we sh…
This paper investigates the performance of an orthogonal frequency-division multiplexing (OFDM)-based cognitive radio (CR) spectrum sharing communication system that assumes random allocation and absence of the primary user's (PU) channel occupation information, i.e., no spectrum sensing is employed to acquire information about the availability of unused subcarriers. In case of a single secondary user (SU) in the secondary network, due to the lack of information of PUs' activities, the SU randomly allocates the subcarriers of the primary network and collide with the PUs' subcarriers with a cer…
