Short Packet Transmission Research Articles

Open-Loop Communications for Up-Link URLLC Under Clustered User Distribution

To achieve ultra-low latency for ultra-reliable and low latency communications (URLLC) in fifth-generation (5G) systems, open-loop communications (OLC), which removes the time-consuming feedback procedure, has been proposed as a promising technique, especially for uplink transmission (UL). The performance of the OLC has been commonly analysed under a uniform user distribution model. Since the users are commonly clustered around highly populated areas, a clustered or non-uniform user distribution model is more realistic. However, the performance analysis under a clustered user distribution model is not trivial. In this paper, we therefore aim to analyse the OLC network performance under a clustered user distribution. We also investigate how to achieve URLLC by jointly employing the OLC mode, short packet transmission, multi-cell association and diversity schemes such as selection combining (SC) and maximum ratio combining (MRC). We provide a tractable approach to analyse the UL reliability and its approximated closed form. The simulation results show that the derived closed form can provide a reasonable approximation for the UL reliability when the SC algorithm is employed. Moreover, for the case of MRC, the analytical results can provide a tight approximation on the uplink reliability when the number of the associated BSs is lower than 2, while serving as a corresponding upper bound when the number of the associated BSs is larger than 2.

Resource Allocation for Deterministic Delay in Wireless Control Networks

Meeting the high requirements of deterministic transmission in wireless control systems is very challenging, but also very important. In order to improve the determinacy of wireless short packet transmission, we optimize the communication resource required for multi-sensors in the wireless control system in this letter. In particular, we derive the probability of the transmission delay falling within a specified time window to characterize the deterministic transmission and then investigate the relationship between the delay determinacy and the wireless resources by taking the system control performance into account. We then optimize the transmission power spectral density of sensors and the transmission bandwidth allocation to maximize the delay determinacy, and obtain its suboptimal solution by numerical derivation. Finally, numerical results show that the proposed allocation achieves significantly greater the delay determinacy than that of other allocation schemes.

A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.

Age and Energy Tradeoff for Multicast Networks With Short Packet Transmissions

Age of information (AoI) and energy efficiency (EE), as two important performance metrics in status update systems, usually cannot be simultaneously optimized. This paper investigates the age and energy tradeoff for multicast networks with retransmissions, where each sensed status update is encoded as a short blocklength packet and is broadcasted to multiple destinations via independent and identically distributed error-prone channels. By considering the stopping threshold, the average AoI and EE expressions for stopping at earliest- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> , stopping at preselected- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> , and wait-for-all schemes are derived as a function of the packet length. On this basis, the average AoI-EE ratio is introduced and is minimized to trade off age and energy by optimizing the packet length. Numerical results indicate that among the three schemes, earliest- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> scheme attains the minimum AoI when the packet length is small, and preselected- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> scheme attains the maximum EE when the packet length is large. An optimal packet length can always be found for each scheme to minimize age-energy ratio. Moreover, the selection of stopping threshold <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> will also impact the age and energy performance.

Performance of Uplink NOMA with User Mobility Under Short Packet Transmission

The scenario of an uplink two-user non-orthogonal multiple access (NOMA) communication system is analytically studied when it operates in the short packet transmission regime. The considered users support mobility and each is equipped with a single antenna, while they directly communicate with a multi-antenna base station. Power-domain NOMA is adopted for the signal transmission as well as the successive interference cancellation approach is performed at the receiver for decoding. The packet error rate (PER) is obtained in simple closed formulae under independent Rayleigh faded channels and for arbitrary user mobility profiles. The practical time variation and correlation of the channels is also considered. Moreover, useful engineering insights are manifested in short transmission time intervals, which define a suitable setup for the forthcoming ultra-reliable and low latency communication systems. Finally, it turns out that the optimal NOMA power allocation can be computed in a straightforward cost-effective basis, capitalizing on the derived PER expressions.

Low-Latency Short-Packet Transmission over a Large Spatial Scale.

Short-packet transmission has attracted considerable attention due to its potential to achieve ultralow latency in automated driving, telesurgery, the Industrial Internet of Things (IIoT), and other applications emerging in the coming era of the Six-Generation (6G) wireless networks. In 6G systems, a paradigm-shifting infrastructure is anticipated to provide seamless coverage by integrating low-Earth orbit (LEO) satellite networks, which enable long-distance wireless relaying. However, how to efficiently transmit short packets over a sizeable spatial scale remains open. In this paper, we are interested in low-latency short-packet transmissions between two distant nodes, in which neither propagation delay, nor propagation loss can be ignored. Decode-and-forward (DF) relays can be deployed to regenerate packets reliably during their delivery over a long distance, thereby reducing the signal-to-noise ratio (SNR) loss. However, they also cause decoding delay in each hop, the sum of which may become large and cannot be ignored given the stringent latency constraints. This paper presents an optimal relay deployment to minimize the error probability while meeting both the latency and transmission power constraints. Based on an asymptotic analysis, a theoretical performance bound for distant short-packet transmission is also characterized by the optimal distance–latency–reliability tradeoff, which is expected to provide insights into designing integrated LEO satellite communications in 6G.

Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems.

The flourishing of Internet of Things (IoT) applications, characterized by vast transmitter populations and the sporadic transmission of small data units, demands innovative solutions for the sharing of the wireless medium. In this context, satellite connectivity is an important enabler for all scenarios in which terminals are under-served by terrestrial communications and are thus fundamental for providing worldwide coverage. In turn, the design of medium access policies that attain efficient use of the scarce spectrum and can cope with flexible yet unpredictable IoT traffic is of the utmost importance. Starting from these remarks, we investigate in this work the coexistence of a quality of service (QoS)-constrained service with IoT traffic in a shared spectrum as alternative to a more traditional orthogonal allocation among the two services, with an eye on satellite applications. Leaning on analytical tools, we provide achievable rate regions, assuming a slotted ALOHA access method for IoT terminals and accounting for practical aspects, such as the transmission of short packets. Interesting trends emerge, showcasing the benefit of an overlay allocation with respect to segregating the resources for the two services.

Uplink Grant-Free NOMA With Sinusoidal Spreading Sequences

Grant-access based legacy networks are unable to deliver Uplink (UL) dominated sporadic transmission of short packets with low signalling overhead, which are the requirements of Massive Machine Type Communication (mMTC). Hence, grant-free Non-Orthogonal Multiple Access (NOMA) techniques are more suited for mMTC. Due to the inherent sparsity in the user activity pattern in an mMTC system, the existing literature revolves around Compressive Sensing based Multi User Detection (CS-MUD) algorithms, which employ random or Zadoff-Chu spreading sequences for NOMA. In this paper we explore the utility of sinusoidal spreading sequences, which allow some non-iterative algorithms to be employed in the context of active user detection, channel estimation and data detection in a UL grant-free mMTC system. These algorithms do not need prior knowledge about the user behaviour statistics, noise statistics, and the level of sparsity in the user activity, which might be difficult to obtain at the Base Station (BS). We present a comprehensive performance and complexity analysis to assess the feasibility of our proposal. Extensive numerical trials demonstrate the utility of sinusoidal codes for mMTC.

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.

Mapping Algorithm of Multi - code combination spread spectrum technology for Short Packet Transmission

Multi-code combined spread spectrum (MCSS) is based on M-ary spread spectrum to improve information payload by combining multiple spread spectrum sequences, which solved the contradictions between spread spectrum gain and transmission efficiency under limited channel bandwidth conditions. This paper focuses on two mapping algorithms of multi-code combination spread spectrum and their decoding algorithms for MCSS, which transmits the information by combining multiple spreading sequences. The R-combined mapping algorithm is provided to keep the sequence orthogonal and the design complexity increases with the increase of the number of correlators at the receiver while the SVC algorithm is adopted and MMP algorithm is used to realize multi-code combination spread spectrum with increasing sequences. The BER performance of the two algorithms is compared through simulation analysis

Secrecy analysis of short-packet transmissions in ultra-reliable and low-latency communications

In this paper, we investigate the secrecy performance of short-packet transmissions in ultra-reliable and low-latency communications (URLLC). We consider the scenario where a multi-antenna source communicates with a single-antenna legitimate receiver requiring ultra-high reliability and low latency, in the presence of a single-antenna eavesdropper. In order to safeguard URLLC, the source transmits the artificial noise (AN) signal together with the confidential signal to confuse the eavesdropper. We adopt a lower bound on the maximal secrecy rate as the secrecy performance metric for short-packet transmissions in URLLC, which takes the target decoding error probabilities at the legitimate receiver and the eavesdropper into account. Using this metric, we first derive a compact expression of the generalized secrecy outage probability (SOP). Then, we formally prove that the generalized SOP is a convex function with respect to the power allocation factor between the confidential signal and the AN signal. We further determine the optimal power allocation factor that minimizes the generalized SOP. The results presented in this work can be useful for designing new secure transmission schemes for URLLC.

Data-Oriented View for Convolutional Coding With Adaptive Irregular Constellations

Current wireless systems offer various use-cases where conventional channel capacity focused performance criteria might not apply. Following the similar perspective, convolutional encoding can find more room due to its low complexity and low decoding delay. Besides, it has been also shown that error performance of a convolutional encoder can be improved further by using adaptive irregular constellations. A new performance measure, data-oriented approach, was recently proposed for the transmission of small data packets, i.e. mission-critical IoT applications, over fading channels. In this letter, delay performance gain resulting from convolution coding optimized irregular constellations is investigated. Then, we derive a new performance criterion based on delay and finite block length constraints. Based on this criterion, we design irregular constellations together with convolutional coding for short packet transmission.

Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

Quantum Learning Based Nonrandom Superimposed Coding for Secure Wireless Access in 5G URLLC

Secure wireless access in ultra-reliable low-latency communications (URLLC), which is a critical aspect of 5G security, has become increasingly important due to its potential support of grant-free configuration. In grant-free URLLC, precise allocation of different pilot resources to different users that share the same time-frequency resource is essential for the next generation NodeB (gNB) to exactly identify those users under access collision and to maintain precise channel estimation required for reliable data transmission. However, this process easily suffers from attacks on pilots. We in this article propose a quantum learning based nonrandom superimposed coding method to encode and decode pilots on multidimensional resources, such that the uncertainty of attacks can be learned quickly and eliminated precisely. Particularly, multiuser pilots for uplink access are encoded as distinguishable subcarrier activation patterns (SAPs) and gNB decodes pilots of interest from observed SAPs, a superposition of SAPs from access users, by joint design of attack mode detection and user activity detection though a quantum learning network (QLN). We found that the uncertainty lies in the identification process of codeword digits from the attacker, which can be always modelled as a black-box model, resolved by a quantum learning algorithm and quantum circuit. Novel analytical closed-form expressions of failure probability are derived to characterize the reliability of this URLLC system with short packet transmission. Simulations how that our method can bring ultra-high reliability and low latency despite attacks on pilots.

Performance Analysis of Grant-Free Random-Access NOMA in URLL IoT Networks

Internet-of-Things (IoT) networks have recently emerged to provide massive connectivity for many application scenarios and services. Additionally, developing spectrum-access strategies for a large number of nodes with sporadic data traffic behaviors in IoT networks has attracted much attention recently. However, developing such strategies becomes more challenging when ultra-reliable low-latency (URLL) transmissions are required. As IoT networks entail spectrum-efficient transmission schemes, non-orthogonal multiple-access (NOMA) has emerged as a key enabler for such networks. On the other hand, grant-free random-access (RA) techniques are particularly promising for high spectral-efficiency and massive connectivity, since they reduce signaling overhead, and packet latency. Therefore, in this paper, uplink RA-NOMA IoT networks with clustered IoT devices is studied, where short packet and diversity transmissions are adopted to meet the URLL requirements. To reduce the negative effect of diversity transmission on packet latency, multiple replicas of packets are accommodated within different resource blocks (RBs) in the same transmission time interval (TTI). The analytical expressions of network metrics, namely, average packet latency, reliability, and GoodPut are derived. Furthermore, the effect of the number of packet replicas, blocklength, and cluster size on the network metrics is evaluated. Finally, the analytical derivations are utilized to find the optimal values for the number of packet replicas, blocklength, and power control parameters, such that the network GoodPut is maximized, subject to URLL constraints.

Performance Analysis of URLL Energy-Harvesting Cognitive-Radio IoT Networks With Short Packet and Diversity Transmissions

The advent of the Internet-of-Things (IoT) and proliferation of wireless devices and systems have put stringent requirements on reliability and latency, in addition to the scarcity of energy and spectrum resources. More importantly, ultra-reliability and low-latency (URLL) combined with concepts of energy-harvesting (EH) and cognitive-radio (CR) make the analysis of IoT networks much more complex. This paper analyzes the performance of uplink EH-CR-IoT networks with URLL requirements. Analytical expressions for IoT network metrics, namely, average packet latency, reliability, and energy-efficiency are derived, while incorporating diversity transmissions under the finite blocklength (FBL) regime. The effect of network parameters, such as number of resource blocks allocated to each IoT user equipment (UE), blocklength, and number of packet replicas is examined on the network metrics, and their tradeoffs are discussed. Finally, the derived expressions are utilized to maximize the energy-efficiency of the IoT UEs subject to energy-causality and URLL constraints.

An energy efficient hybrid MAC protocol for smart home networks

In this paper cluster-based hybrid MAC (CB-HMAC) protocol, based on IEEE 802.15.4 MAC is proposed for smart home networks. The CB-HMAC is specially designed for short packet transmission in a dense home network environment. Simulation results show that the proposed CB-HMAC is more energy-efficient than other medium access control (MAC) protocols without sacrificing much delay and throughput.

Energy-Efficient Precoder Design for Downlink Multi-User MISO Networks With Finite Blocklength Codes

One of the key applications in the fifth generation (5G) cellular networks is to support ultra-reliable and low-latency communication (URLLC) which requires extremely high reliability (~ 99.999%) and low latency (< 1 ms). In this article, the energy efficiency (EE) is maximized for downlink multi-user multiple-input single-output (MISO) networks under short packet transmission. We jointly optimize the precoders at the base station (BS) for serving multiple downlink users and the decoding error probability (DEP) with finite blocklength (FBL) codes, subject to the constraints on the BS transmit power and DEP per URLLC user. This non-convex optimization problem is then accurately approximated by applying the Dinkelbach method and approximating the channel dispersion in the high signal-to-interference-plus-noise ratio (SINR) regime and the entire (practical) SINR regime, respectively. The resulting problems are convex in the design variables of the precoders and DEPs when the remaining variables are held fixed, and using this fact we provide iterative algorithms to efficiently find locally optimal solutions. Furthermore, the convergence of the proposed algorithms and the closed-form expressions of the precoders are also derived. Simulation results validate the effectiveness of the proposed algorithms that support energy-efficient URLLC as well as the impact of various network parameters such as the blocklength, number of users, distance between the BS and URLLC users and the threshold of the DEP on the EE and spectral efficiency (SE) performance.

Generalized Differential Index Modulation for Pilot-Free Communications

Short packet transmission is a key ingredient for the Internet-of-Things (IoT) communications that require extremely low latency. The pilot-based short packet transmission makes lower spectral efficiency and larger transmission delays. To resolve this problem, in this article, we present a generalized differential subcarrier index modulation (GDIM) for pilot-free short packet transmission. The central idea of GDIM is to map information bits to possible indices that can be constructed by joint row and column permutations of a unitary matrix. We show that the proposed GDIM achieves higher spectral efficiency than that attained by the existing differential modulation techniques, including differential subcarrier index modulation and differential Alamouti code. We present a noncoherent maximum-likelihood (ML) detection method and a low-complexity variant of it for GDIM. Furthermore, by analyzing the average bit-error-rate when applying GDIM, we propose an algorithm to optimize GDIM constellation sets. Simulation results demonstrate that the proposed GDIM outperforms the existing differential modulation techniques in terms of bit-error-rates for numerous target spectral efficiencies.

Sparse Vector Coding Aided Ultra-Reliable and Low-Latency Communications in Multi-User Massive MIMO Systems

Ultra-reliable and low-latency communication (URLLC) has been recognized as a key service to support delay-sensitive applications for next generation wireless systems. A critical challenge is how to support multiple users with ultra reliability in the short packet transmission. In this paper, we propose a sparse vector coding (SVC)-aided URLLC multi-user transmission scheme by employing the massive multiple-input multiple-output (MIMO) technique. To reliably acquire the support information, we formulate the SVC decoding problem in multi-user massive MIMO systems as the problem to find out nonzero positions of a sparse vector. Then, the support recovery problem is transformed into a series of sub-problems by decoupling the support identification of each user, where the successive interference cancellation based matching pursuit (SIC-MP) algorithm is proposed to estimate the support sequentially. In addition, the user sorting scheme is proposed to alleviate the inter-user interference in the SVC decoding. The complexity of the proposed SIC-MP algorithm is only linear with the system parameters.