Grant-free Transmission Research Articles

Analytic Modeling for Grant-Free Transmission in Cell-Free Massive MIMO: A Stochastic Geometry Approach

Analytic Modeling for Grant-Free Transmission in Cell-Free Massive MIMO: A Stochastic Geometry Approach

Impact of NOMA on Age of Information: A Grant-Free Transmission Perspective

Impact of NOMA on Age of Information: A Grant-Free Transmission Perspective

NOMA-Assisted Grant-Free Transmission: How to Design Pre-Configured SNR Levels?

NOMA-Assisted Grant-Free Transmission: How to Design Pre-Configured SNR Levels?

RIS-Assisted Device Activity Detection With Statistical Channel State Information

This paper studies reconfigurable intelligent surface (RIS)-assisted device activity detection for grant-free (GF) uplink transmission in wireless communication networks. In particular, we consider mobile devices located in an area where the direct link to an access point (AP) is blocked. Thus, the devices try to connect to the AP via a reflected link provided by an RIS. Therefore, for the RIS, a phase-shift design is desired that covers the entire blocked area with a wide reflection beam because the exact locations and times of activity of the devices are unknown in GF transmission. In order to study the impact of the phase-shift design on the device activity detection at the AP, we derive a generalized likelihood ratio test (GLRT) based detector and present an analytical expression for the probability of detection, which is a function of the channel statistics and the phase-shift design. Assuming knowledge of statistical channel state information (CSI), we formulate an optimization problem for the phase-shift design for maximization of the guaranteed probability of detection for all locations within a given coverage area. To tackle the non-convexity of the problem, we propose two different approximations of the objective function and an algorithm based on the majorization-minimization (MM) principle. The first approximation leads to a design that aims to reduce the variations of the end-to-end channel while taking system parameters such as transmit power, noise power, and probability of false alarm into account. The second approximation can be adopted for versatile RIS deployments because it only depends on the line-of-sight (LoS) component of the end-to-end channel and is not affected by system parameters. For comparison, we also consider a phase-shift design maximizing the average channel gain and a baseline analytical phase-shift design for large blocked areas. Our performance evaluation shows that the proposed approximations result in phase-shift designs that guarantee a high probability of detection across the coverage area and outperform the baseline designs.

Research on active user detection and channel estimation algorithms in massive access

Massive Access is a supporting technology for scenarios like the Internet of Things, and is also one of the typical scenarios defined by 5G. Unlike traditional multi-user communication, the data of these nodes is generally sudden, so only a small proportion of nodes are active at a certain time. If the traditional request resource allocation protocol mode is adopted, it requires authorized access to achieve dynamic resource allocation, which will result in excessive protocol overhead. Therefore, Grant-Free transmission is required. It is impossible to recover the data transmitted by active users during Grant-Free transmission because the base station is unaware of who is transmitting the data. Therefore, the system must first do active user detection and channel estimation. A channel estimation technique based on Compressed Sensing is presented in this paper. the EM-BG-AMP algorithm is reproduced and applied. By comparing the accuracy of the EM-BG-AMP algorithm in channel estimation under physical backgrounds, this essay gives the range of optimal received signal-to-noise ratio:100dB and more. Also, this essay demonstrates that the performance of the EM-BG-AMP algorithm weakens as sparsity increases.

Deep Learning-Based User Activity Detection and Channel Estimation in Grant-Free NOMA

In the uplink machine-type communication (MTC) system, a combination of grant-free transmission and non-orthogonal multiple access (NOMA) emerges to reduce the control overhead and transmission latency. In the grant-free scenario, the base station needs to identify the active devices and estimate the channel state information before the data detection. However, due to the lack of a scheduling process, the user activity detection (UAD) and channel estimation (CE) are both challenging, especially when short non-orthogonal preambles are adopted. In this paper, by exploiting the framework of the compressive sensing-based algorithm, we propose a novel deep learning architecture, namely UAD and CE Neural Network (UAD-CE-NN), to effectively solve the joint UAD and CE problem for grant-free NOMA. In the proposed scheme, the user activity and channel state information hidden in the received data signals are also exploited to aid the preamble for higher detection accuracy. Specifically, UAD-CE-NN is composed of two stages: we first build a preamble detection neural network for a tentative UAD-CE; a data detection neural network is then deployed to exploit the data signals. Compared with the conventional schemes, the proposed scheme obtains much higher accuracy for both the UAD and CE, especially when short preamble sequences are employed.

Data-Aided Active User Detection With False Alarm Correction in Grant-Free Transmission

In most existing grant-free (GF) studies, the two key tasks, namely active user detection (AUD) and payload data decoding, are handled separately. In this letter, a two-step data-aided AUD scheme is proposed, namely the initial AUD step and the false alarm correction step respectively. To implement the initial AUD step, an embedded low-density-signature (LDS) based preamble pool is constructed. In addition, two message passing algorithm (MPA) based initial estimators are developed. In the false alarm correction step, a redundant factor graph is constructed based on the initial active user set, on which MPA is employed for data decoding. The remaining false detected inactive users will be further detected by the false alarm corrector with the aid of decoded data symbols. Simulation results reveal that both the data decoding performance and the AUD performance are significantly enhanced by more than 1.5 dB at the target accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-3}$ </tex-math></inline-formula> compared with the traditional compressed sensing (CS) based counterparts.

Broadcast Approach to Uplink NOMA: Queuing Delay Analysis.

Emerging wireless technologies are envisioned to support a variety of applications that require simultaneously maintaining low latency and high reliability. Non-orthogonal multiple access techniques constitute one candidate for grant-free transmission alleviating the signaling requirements for uplink transmissions. In open-loop transmissions over fading channels, in which the transmitters do not have access to the channel state information, the existing approaches are prone to facing frequent outage events. Such outage events lead to repeated re-transmissions of the duplicate information packets, penalizing the latency. This paper proposes a multi-access broadcast approach in which each user splits its information stream into several information layers, each adapted to one possible channel state. This approach facilitates preventing outage events and improves the overall transmission latency. Based on the proposed approach, the average queuing delay of each user is analyzed for different arrival processes at each transmitter. First, for deterministic arrivals, closed-form lower and upper bounds on the average delay are characterized analytically. Secondly, for Poisson arrivals, a closed-form expression for the average delay is delineated using the Pollaczek-Khinchin formula. Based on the established bounds, the proposed approach achieves less average delay than single-layer outage approaches. Under optimal power allocation among the encoded layers, numerical evaluations demonstrate that the proposed approach significantly minimizes average sum delays compared to traditional outage approaches, especially under high arrival rates.

OFDM-Based Massive Grant-Free Transmission Over Frequency-Selective Fading Channels

In massive grant-free transmission, joint user activity detection (UAD) and channel estimation (CE) is essential for data recovery at the receiver, which has been extensively researched in frequency-flat fading scenarios. However, in practical orthogonal frequency division multiplexing (OFDM)-based systems, frequency-selective fading (FSF) leads to a significant increase in the number of channel coefficients to be estimated, imposing new challenges for the design of joint UAD and CE algorithms. Therefore, this paper investigates joint UAD and CE for OFDM-based massive grant-free transmission over FSF channels. Firstly, by employing the discrete cosine transform (DCT), the joint estimation problem is formulated as the compressed sensing (CS) problem with the reduced dimension of the DCT-domain channel response vector. Then, based on the low-dimension sparse channel model, we develop a hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) minimization framework to achieve efficient joint UAD and CE. To deal with the lack of the DCT-domain prior information in practical scenarios, we parameterize it as the Cauchy distribution or the Laplacian distribution and learn their parameters by the proposed HMP algorithm. Numerical results confirm the superior joint UAD and CE performance of the proposed algorithm over FSF channels.

Optimization of Grant-Free NOMA With Multiple Configured-Grants for mURLLC

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.

Semi-Grant-Free NOMA: A Stochastic Geometry Model

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 users to share the same spectrum resources. With the aid of semi-GF protocols, uplink NOMA networks are investigated by invoking stochastic geometry techniques. We propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic protocol</i> to interpret which part of the GF users are allocated in NOMA transmissions via transmitting various channel quality thresholds by an added handshake. We utilize open-loop protocol with a fixed average threshold as the benchmark to investigate performance improvement. It is observed that dynamic protocol provides more accurate channel quality thresholds than open-loop protocol, thereby the interference from the GF users is reduced to a large extent. We analyze the outage performance and diversity gains under two protocols. Numerical results demonstrate that dynamic protocol is capable of enhancing the outage performance than open-loop protocol.

Guest Editorial Special Issue on 6G-Enabled Internet of Things

Sixth-generation (6G) wireless communications and networks will continue to move to higher frequency and wider bandwidth, with much higher data rate and spectral efficiency. Given the heterogeneity and densification of Internet of Things (IoT), 6G wireless network may need to be extended to modern random access (RA) for IoT applications, which can be achieved via smart protocol design and advanced signal processing and communications technologies. The modern RA techniques, such as massive multiple-input–multiple-output (MIMO), OFDMA, nonorthogonal multiple access (NOMA), sparse signal processing, or new orthogonal design techniques provide good candidacy for 6G-enabled IoT. In 6G, grant-free transmission should be designed for distributed IoT applications. IoT applications are often involved in self-organizing decision making. 6G-enabled IoT will take advantage of the recent development of artificial intelligence (AI) techniques. It will generate new knowledge and understanding and accelerate discovery and innovation in IoT. Each of these efforts is designed to amplify the intrinsically multidisciplinary nature of the emerging field of IoT. The 6G-enabled IoT will establish theoretical, technical, and ethical frameworks that will be applied to tackle many challenges in IoT, advancing technology for humanity.

Outage Performance Analysis of Widely Linear Receivers in Uplink Multi-User MIMO Systems

This paper considers the application of widely linear (WL) receivers in an uplink multi-user system using real-valued modulation schemes, where the cellular base station (BS) with multiple antennas provides connectivity for randomly deployed single-antenna users. The targeted use case is massive machine type communication (mMTC) with grant-free access in the uplink, where the network is required to host a large number of low data rate devices transmitting in an uncoordinated fashion. Four types of WL receivers are investigated, namely the WL zero-forcing (ZF) and the WL minimum mean-squared error (MMSE) receivers, along with their enhanced versions employing successive interference cancellation (SIC) with channel-dependent ordering, i.e., the WL-ZF-SIC and WL-MMSE-SIC receivers. The outage performances of these receivers are analytically characterized in the high signal-to-noise ratio (SNR) regime and compared to those of conventional linear (CL) receivers using complex-valued modulation schemes. For the non-SIC receivers, we show that, when compared to the CL counterparts, the WL receivers yield a higher diversity gain when decoding the same number of users and have the same diversity gain but a decreased coding gain when the number of users is nearly doubled. The outage performance analysis of WL-SIC receivers is facilitated by the marginal distribution of ordered eigenvalues of a real-valued Wishart matrix. It is shown that the SIC operation with channel-dependent ordering brings no additional diversity gain to the WL receivers but instead increases the coding gain. Moreover, the coding gain of WL-SIC receivers grows as the number of users increases and even exceeds that of CL-SIC receivers under suitable conditions. For the mMTC scenario with grant-free transmission, it is demonstrated that the WL receivers outperform their CL counterparts in terms of offering a lower outage (and packet drop) probability and a higher system throughput for a given packet drop probability.

Optimization of ultra-reliable low-latency communication systems

The increasingly ubiquitous applications of Ultra-Reliable Low-Latency Communications (URLLC) require innovative solutions that can only be achieved through a flexible communication system such as the The Fifth Generation (5G) New Radio (NR). Though a number of Grant-Free (GF) and Grant-based (GB) transmission schemes have been proposed to meet URLLC requirements there is a lack of work on the design of these systems. In this paper, we develop an analytical model that enables optimization of the performance of the URLLC systems with heterogeneous traffic. We consider uplink communications and assume that multiple copies of the same packet will be transmitted, utilizing both dedicated and shared resources. The network considered in this study consists of users with both periodic and sporadic traffic. Users in the network are grouped into classes according to their packet generation probabilities. While all users access shared resources through GF transmission, access to dedicated resources is done in two different ways depending on the user's packet generation rate, namely, Periodic Scheduling (PS) and GB scheduling. Although recent studies were disinclined towards the GB scheme due to its high latency, we show that the exploitation of 5G NR's new scalable numerology results in significant reductions to GB's latency, making it suitable for the URLLC use case. Following this latency examination, we present probabilistic expressions representing the reliability of our proposed solution. Then, we formulate an optimization problem in terms of minimizing the required bandwidth or maximizing the traffic capacity while satisfying reliability requirements. The performance of the system is maximized through optimal allocation of the resources among the transmission schemes and classification of the user classes as PS or GB based.

Dynamic Resource Configuration for Low-Power IoT Networks: A Multi-Objective Reinforcement Learning Method

Considering grant-free transmissions in low-power IoT networks with unknown time-frequency distribution of interference, we address the problem of Dynamic Resource Configuration (DRC), which amounts to a Markov decision process. Unfortunately, off-the-shelf methods based on single-objective reinforcement learning cannot guarantee energy-efficient transmission, especially when all frequency-domain channels in a time interval are interfered. Therefore, we propose a novel DRC scheme where configuration policies are optimized with a Multi-Objective Reinforcement Learning (MORL) framework. Numerical results show that the average decision error rate achieved by the MORL-based DRC can be even less than 12% of that yielded by the conventional R-learning-based approach.

CS-Based Near-Optimal MUD for Uplink Grant-Free NOMA

In order to furnish connectivity to an incredible number of devices and to satisfy the capacity demands, non-orthogonal multiple access (NOMA) is considered as a hopeful solution for future 5G. Moreover, grant-free transmission is substantially expected in the uplink NOMA to minimize the latency time and detection overhead. However, multi-user detection (MUD) without user-activity information in uplink grant-free NOMA is hard in practice. In this paper, by benefit from the user-activity sparsity existing in uplink grant-free NOMA, the multipath matching pursuit (MMP) is used for user-activity detection, and then message passing algorithm (MPA) can be efficiently applied for active users’ data detection. Simulation results show that the proposed MMP-MAP MUD with acceptable complexity can achieve better performance than conventional solutions. Also, the MMP-MAP detector reaches the performance obtained by the MPA detector with perfect user-activity information once the level of signal-to-noise ratio exceed 11 dB.

An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things.

Random access is one of the most competitive multiple access schemes for future space-based Internet of Things (S-IoT) due to its support for massive connections and grant-free transmission, as well as its ease of implementation. However, firstly, existing random access schemes are highly sensitive to load: once the load exceeds a certain critical value, the throughput will drop sharply due to the increased probability of data collision. Moreover, due to variable satellite coverage and bursty traffic, the network load of S-IoT changes dynamically; therefore, when existing random access schemes are applied directly to the S-IoT environment, the actual throughput is far below the theoretical maximum. Accordingly, this paper proposes an intelligent load control-based random access scheme based on CRDSA++, which is an enhanced version of the contention resolution diversity slotted ALOHA (CRDSA) and extends the CRDSA concept to more than two replicas. The proposed scheme is dubbed load control-based three-replica contention resolution diversity slotted ALOHA (LC-CRDSA3). LC-CRDSA3 actively controls network load. When the load threatens to exceed the critical value, only certain nodes are allowed to send data, and the load is controlled to be near the critical value, thereby effectively improving the throughput. In order to accurately carry out load control, we innovatively propose a maximum likelihood estimation (MLE)-based load estimation algorithm, which estimates the load value of each received frame by making full use of the number of time slots in different states. On this basis, LC-CRDSA3 adopts computational intelligence-based time series forecasting technology to predict the load values of future frames using the historical load values. We evaluated the performance of LC-CRDSA3 through a series of simulation experiments and compared it with CRDSA++. Our experimental results demonstrate that in S-IoT contexts where the load changes dynamically, LC-CRDSA3 can obtain network throughput that is close to the theoretical maximum across a wide load range through accurate load control.

Priority Enabled Grant-Free Access With Dynamic Slot Allocation for Heterogeneous mMTC Traffic in 5G NR Networks

Although grant-based mechanisms have been a predominant approach for wireless access for years, the additional latency required for initial handshake message exchange and the extra control overhead for packet transmissions have stimulated the emergence of grant-free (GF) transmission. GF access provides a promising mechanism for carrying low and moderate traffic with small data and fits especially well for massive machine type communications (mMTC) applications. Despite a surge of interest in GF access, how to handle heterogeneous mMTC traffic based on GF mechanisms has not been investigated in depth. In this paper, we propose a priority enabled GF access scheme which performs dynamic slot allocation in each 5G new radio subframe to devices with different priority levels on a subframe-by-subframe basis. While high priority traffic has access privilege for slot occupancy, the remaining slots in the same subframe will be allocated to low priority traffic. To evaluate the performance of the proposed scheme, we develop a two-dimensional Markov chain model which integrates these two types of traffic via a pseudo-aggregated process. Furthermore, the model is validated through simulations and the performance of the scheme is evaluated both analytically and by simulations and compared with two other GF access schemes.

Sequence Design and User Activity Detection for Uplink Grant-Free NOMA in mMTC Networks

Massive machine type communications (mMTC) is one of the three major scenarios in 5G wireless networks. mMTC is an uplink-dominant scenario that supports massive connectivity of small-size packets and sporadic traffic. To meet the requirement of mMTC, non-orthogonal multiple access (NOMA) with grant-free transmission is considered a promising technology because of its higher spectrum efficiency and lower signaling overhead. Request-and-grant-based scheduling process is eliminated in grant-free transmission and thus the base stations need to identify active users first before data detection. Previously, several user activity detection (UAD) algorithms were proposed and they can obtain good performance in flat-fading channels. However, the performance of these works degrades significantly in doubly selective fading channels. In this work, we propose an effective solution for uplink grant-free NOMA with a new user sequence scheme and a novel UAD algorithm suitable for practical channel scenarios. Simulation results validated that the proposed sequences can mitigate the inter-user interference at the base stations, and the proposed UAD algorithm can achieve better detection accuracy. Moreover, detailed complexity analysis showed that the proposed UAD algorithm has relatively low complexity and good scalability.

A Systematic Review on NOMA Variants for 5G and Beyond

Over the last few years, interference has been a major hurdle for successfully implementing various end-user applications in the fifth-generation (5G) of wireless networks. During this era, several communication protocols and standards have been developed and used by the community. However, interference persists, keeping given quality of service (QoS) provision to end-users for different 5G applications. To mitigate the issues mentioned above, in this paper, we present an in-depth survey of state-of-the-art non-orthogonal multiple access (NOMA) variants having power and code domains as the backbone for interference mitigation, resource allocations, and QoS management in the 5G environment. These are future smart communication and supported by device-to-device (D2D), cooperative communication (CC), multiple-input and multiple-output (MIMO), and heterogeneous networks (HetNets). From the existing literature, it has been observed that NOMA can resolve most of the issues in the existing proposals to provide contention-based grant-free transmissions between different devices. The key differences between the orthogonal multiple access (OMA) and NOMA in 5G are also discussed in detail. Moreover, several open issues and research challenges of NOMA-based applications are analyzed. Finally, a comparative analysis of different existing proposals is also discussed to provide deep insights to the readers.