Short Packets Research Articles

Learning to Demodulate From Few Pilots via Offline and Online Meta-Learning

This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characterized by unique transmission non-idealities, such as I/Q imbalance. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side distortion. This paper proposes to tackle this problem by using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated, including Model-Agnostic Meta-Learning (MAML), First-Order MAML (FOMAML), REPTILE, and fast Context Adaptation VIA meta-learning (CAVIA). Both offline and online solutions are developed. In the latter case, an integrated online meta-learning and adaptive pilot number selection scheme is proposed. Numerical results validate the advantages of meta-learning as compared to training schemes that either do not leverage prior transmissions or apply a standard joint learning algorithms on previously received data.

Is Multichannel Access Useful for Timely Information Update?

This paper investigates information freshness of multichannel access in information update systems. Age of information (AoI) is a fundamentally important metric to characterize information freshness, defined as the time elapsed since the generation of the last successfully received update. When multiple devices share the same wireless channel to send updates to a common receiver, an interesting question is whether dividing the whole channel into several subchannels will lead to better AoI performance. Given the same frequency band, dividing it into different numbers of subchannels lead to different transmission times and packet error rates (PER) of short update packets, thus affecting information freshness. We focus on a multichannel access system where different devices take turns to transmit with a cyclic schedule repeated over time. We first derive the average AoI by estimating the PERs of short packets. Then we examine bounded AoI, for which the instantaneous AoI is required to be below a threshold a large percentage of the time. Simulation results indicate that multichannel access can provide low average AoI and uniform bounded AoI simultaneously across different received powers. Overall, our investigations provide insights into practical designs of multichannel access systems with AoI requirements.

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.

Superimposed Pilot Code-Domain NOMA Scheme for Satellite-Based Internet of Things

Satellite-based Internet of Things (S-IoT) is regarded as an effective solution to enable ubiquitous connectivity in a global coverage and cost-effective manner for massive machine type communications (mMTC) in the future Internet of Things. In this article, we propose a superimposed pilots code domain nonorthogonal multiple access over a shadowed-Rician fading and path loss satellite-ground channel model in uplink mMTC S-IoT system. First, we analyze the interference due to the collision in the random access scenario, and propose an iteration channel estimation based on ridge regression algorithm to attain a more precise channel state information. Moreover, we utilize successive interference cancellation (SIC) and successive joint decoding (SJD) to recover the singleton user equipments. Then, we derive the expressions of the outage probability and maximum system throughput of SP scheme, and compared with the regular orthogonal pilot (RP) scheme under SIC and SJD, respectively. Simulations validate our analytical results and show that the achievable system throughput of the SP scheme under SJD can outperform that of the RP scheme in a S-IoT system for short packet transmission.

Implementation and Performance Comparison of High-Capacity Software Routers

Software routers are flexible low-cost platforms for implementation of network functionalities on general-purpose CPUs and NICs. General-purpose hardware is constantly improving, and, currently, it deploys high frequency CPUs and NICs that can support multiple 10/40/100 Gbps network interfaces. General-purpose hardware allows implementation of flexible network functions that can be modified on demand. However, it has lower throughput than the specialized hardware, which gets particularly limited for short packets. On the other side, traffic loads with short packets to be switched are increasing, and, they include gaming applications, social networking and other demanding loads. Specialized packet I/O frameworks provide custom network drivers and bypass kernel network stack, allowing direct packet exchange between applications and NICs with high throughputs for all packet sizes. We developed complete software routers with high performance data planes that are based on netmap and DPDK frameworks. We evaluated and compared implemented software routers on advanced server with potential switching capacity of 200 Gbps.

URLLC Facilitated by Mobile UAV Relay and RIS: A Joint Design of Passive Beamforming, Blocklength, and UAV Positioning

Upcoming fifth-generation (5G) networks need to support novel ultrareliable and low-latency (URLLC) traffic that utilizes short packets. This requires a paradigm shift as traditional communication systems are designed to transmit only long data packets based on Shannon’s capacity formula, which poses a challenge for system designers. To address this challenge, this article relies on an unmanned aerial vehicle (UAV) and a reconfigurable intelligent surface (RIS) to deliver short URLLC instruction packets between ground Internet-of-Things (IoT) devices. In this context, we perform passive beamforming of RIS antenna elements as well as nonlinear and nonconvex optimization to minimize the total decoding error rate and find the UAV’s optimal position and blocklength. In this article, a novel, polytope-based method from the class of direct search methods (DSMs) named Nelder–Mead simplex (NMS) is used to solve the optimization problem based on its computational efficiency; in terms of lesser number of required iterations to evaluate objective function. The proposed approach yields better convergence performance than the traditional gradient-descent optimization algorithm and a lower computation time and equivalent performance for the blocklength variable as the exhaustive search. Moreover, the proposed approach allows ultrahigh reliability, which can be attained by increasing the number of antenna elements in RIS as well as increasing the allocated blocklengths. Simulations demonstrate the RIS’s performance gain and conclusively show that the UAV’s position is crucial for achieving ultrahigh reliability in short packet transmission.

Packet Error Probability and Effective Throughput for Ultra-Reliable and Low-Latency UAV Communications

In this paper, we study the average packet error probability (APEP) and effective throughput (ET) of the control link in unmanned-aerial-vehicle (UAV) communications, where the ground central station (GCS) sends control signals to the UAV that requires ultra-reliable and low-latency communications (URLLC). To ensure the low latency, short packets are adopted for the control signal. As a result, the Shannon capacity theorem cannot be adopted here due to its assumption of infinite channel blocklength. We consider both free space (FS) and 3-Dimensional (3D) channel models by assuming that the locations of the UAV are randomly distributed within a restricted space. We first characterize the statistical characteristics of the signal-to-noise ratio (SNR) for both FS and 3D models. Then, the closed-form analytical expressions of APEP and ET are derived by using Gaussian-Chebyshev quadrature. Also, the lower bounds are derived to obtain more insights. Finally, we obtain the optimal value of packet length with the objective of maximizing the ET by applying one-dimensional search. Our analytical results are verified by the Monte-Carlo simulations.

Group-based delivery of critical traffic in cellular IoT networks

Fifth generation (5G) networks are expected to connect a huge number of Internet of Things (IoT) devices in many usage scenarios. The challenges of typical massive IoT applications with sporadic and short packet uplink transmissions are well studied, while not enough attention is given to the delivery of content of common interest, such as software/firmware updates and remote control, towards IoT devices in emerging point-to-multipoint scenarios. Moreover, the delivery of delay-sensitive IoT traffic is not sufficiently addressed in the literature. In this work we (i) identify the drawbacks of the current Single-Cell Point-to-Multipoint (SC-PTM) solution for unplanned critical traffic delivery in cellular IoT (cIoT) networks, and (ii) propose paging and multicast schemes for a fast distribution of critical updates after, e.g., bug fixes or system failures. We benchmark the performance of the proposed paging scheme against similar solutions available in the literature. Our extended SC-PTM framework is energy efficient and guarantees low service latency, as demonstrated both analytically and by simulations.

Spatio-temporal Modeling for Massive and Sporadic Access

The vision for smart city imperiously appeals to the implementation of Internet-of-Things (IoT), some features of which, such as massive access and bursty short packet transmissions, require new methods to enable the cellular system to seamlessly support its integration. Rigorous theoretical analysis is indispensable to obtain constructive insight for the networking design of massive access. In this paper, we propose and define the notion of massive and sporadic access (MSA) to quantitatively describe the massive access of IoT devices. We evaluate the temporal correlation of interference and successful transmission events, and verify that such correlation is negligible in the scenario of MSA. In view of this, in order to resolve the difficulty in any precise spatio-temporal analysis where complex interactions persist among the queues, we propose an approximation that all nodes are moving so fast that their locations are independent at different time slots. Furthermore, we compare the original static network and the equivalent network with high mobility to demonstrate the effectiveness of the proposed approximation approach. The proposed approach is promising for providing a convenient and general solution to evaluate and design the IoT network with massive and sporadic access.

Average Age of Information in Short Packet Based Machine Type Communication

It is crucial to consider timely status updates for machine type communications (MTC) in some scenarios, where a controller continuously obtains state information of targets from a sender to complete control or monitoring tasks, such as vehicular networking and environmental detection. In this work, we measure the timeliness of the status by the recently proposed age of information (AoI) metric. Different from the previous works about AoI, we consider the short packet feature of MTC in our system, where status packets will suffer from a significant packet error rate. Considering that the status packets may not be decoded by the receiver correctly, this work investigates the performance of MTC under traditional protocol (the sender discards the error status packets directly) and automatic repeat-request (ARQ) protocol (the sender retransmits the error status packets), and obtains the approximated closed-form expressions of the average AoI, respectively. Based on the closed-form expressions of the average AoI, we design two suboptimal blocklengths to minimize the average AoI under the two protocols. Numerical results verify the correctness of theoretical analysis, and show that the proposed scheme can achieve almost the same optimization performance as the exhaustive search method. Moreover, in the case of different packet generation rates, we find that the ARQ/traditional protocol can achieve better performance in low/high packet generation rate scenarios, respectively.

Sparse Vector Transmission: An Idea Whose Time Has Come

In recent years, we have witnessed a bewildering variety of automated services and applications involving vehicles, robots, sensors, and machines powered by artificial intelligence (AI) technologies. Communication mechanisms associated with these services are clearly distinct from human-centric methods. One important feature of machine-centric communication is that the amount of information to be transmitted is tiny. In view of the short packet transmission, relying on today's transmission mechanisms would not be efficient due to the waste of resources, large decoding latency, and expensive operational cost. In this article, we present an overview of sparse vector transmission (SVT), a scheme to transmit short pieces of information after sparse transformation. We discuss the basics of SVT and two distinct SVT strategies [frequency-domain sparse transmission (FDST) and SV coding (SVC)] and demonstrate their effectiveness in realistic wireless environments.

Triangular Non-Orthogonal Random Access in mMIMO Systems

Massive multiple-input multiple-output (mMIMO) based grant-free random access (RA) (mGFRA) has been studied for massive machine-type communication (mMTC). In the existing works of mGFRA, it is implicitly assumed that each RA user equipment (UE) in mMTC has the same data-packet size. However, this may not be the case in practice as RA UEs in different applications can have different numbers of bits to send, which may result in data packets of different sizes. In addition to that, preamble collision is a key issue that degrades the performance of mGFRA when orthogonal preamble is used. In this article, we aim to address the preamble collision issue under a practical mGFRA scenario that consists of two different types of RA UEs (with short and large data packets). Specifically, by exploiting different sizes of data packets, we propose a triangular non-orthogonal RA (T-NORA) scheme, where the preamble transmission of RA UEs with small-sized data packet is embedded into the data transmission of RA UEs with large-sized data packet so that the preamble collision between two different types of RA UEs can be avoided. As a result, an improved performance over conventional mGFRA can be achieved by T-NORA with the assistance of mMIMO. Through analytical and simulation results, we can confirm that the proposed T-NORA scheme outperforms the conventional mGFRA scheme.

On the optimization of resources for short frame synchronization

We consider the transmissions of successive short packets. Each of them combines information to be transmitted (codeword) with information for synchronizing the frame (syncword). For short packets, the cost of including syncwords is no longer negligible and its design requires careful optimization. In particular, a trade-off arises depending on the way the total transmit power or the total frame length is split among the syncword and the codeword. Assuming optimal finite-length codes, we develop tight upper bounds on the probability of erroneous synchronization, for both frames with concatenated syncword and frames with superimposed syncword. We use these bounds to optimize this trade-off. Simulation results show that the proposed bounds and analysis have practical relevance for short packet communication system design.

Rapid Frequency Variations Within Intense Chorus Wave Packets

AbstractWhistler mode chorus waves are responsible for electron acceleration in Earth's radiation belts. It is unclear, however, whether the observed acceleration is still well described by quasi‐linear theory, or if this acceleration is due to intense waves that require nonlinear treatment. Here, we perform a comprehensive statistical analysis of intense lower‐band chorus wave packets to investigate the relationships between wave frequency variations, packet length, and wave amplitude, and their temporal variability. We find that 15% of the wave power is carried by long packets, with low frequency sweep rates (linear trend in time) that agree with the nonlinear theory of chorus wave growth. Eighty‐five percent of the wave power, however, comes from short packets with large frequency variations around the linear trend. The kappa‐like probability distribution of these variations is consistent with random superposition of different waves that could result in a destruction of nonlinear resonant interaction.

Quasi-Optimization of Uplink Power for Enabling Green URLLC in Mobile UAV-Assisted IoT Networks: A Perturbation-Based Approach

Efficient resource allocation can maximize power efficiency, which is an important performance metric in future fifth-generation (5G) communications. The minimization of sum uplink power in order to enable green communications while concurrently fulfilling the strict demands of ultrareliability for short packets is an essential and central challenge that needs to be addressed in the design of 5G and subsequent wireless communication systems. To address this challenge, this article analyzes the joint optimization of various unmanned aerial vehicle (UAV) systems parameters, including the UAV’s position, height, beamwidth, and the resource allocation for uplink communications between ground Internet-of-Things (IoT) devices and a UAV employing short ultrareliable and low-latency (URLLC) data packets. Toward achieving the aforesaid task, we proposed a perturbation-based iterative optimization to minimize the sum uplink power in order to determine the optimal position for the UAV, its height, beamwidth of its antenna, and the blocklength allocated for each IoT device. It is shown that the proposed algorithm has lower time complexity, yields better performance than other benchmark algorithms, and achieves similar performance to exhaustive search. Moreover, the results also demonstrate that Shannon’s formula is not an optimum choice for modeling sum power for short packets as it can significantly underestimate the sum power, where our calculations show that there is an average difference of 47.51% for the given parameters between our proposed approach and Shannon’s formula. Finally, our results confirm that the proposed algorithm allows ultrahigh reliability for all the users and converges rapidly.

Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems

This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, packet error probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.

NOMA-Assisted Secure Short-Packet Communications in IoT

The Internet of Things (IoT) is expected to provide ubiquitous wireless machine-type communication devices and extensive information collection, resulting in an unprecedented amount of privacy and secrets exposed to the radio space. Security issues become a major restriction on the further development of IoT. However, secure transmissions in IoT are challenged by low complexity limitation and massive connectivity demand, especially by the use of short packets, which are expected to satisfy the delay requirement in ultra-reliable low-latency communications. Physical layer security can be employed without the constraints of packet length and number of connections. Nevertheless, due to the limitations of complexity, not all existing PLS techniques can be adopted in IoT. Non-orthogonal multiple access (NOMA) is a promising technique for increasing connectivity and reducing delay. Assuming an eavesdropper (Eve) is capable of the same detection capability as legitimate users, this article further exploits the inherent characteristics of NOMA to secure short-packet communications in IoT networks without introducing extra security mechanisms. Both downlink and uplink NOMA schemes are introduced to secure transmission by deliberately increasing the co-channel interference at Eve, which can be viewed as a special cooperative jamming strategy. Simulations show that in both uplink and downlink, although secrecy performance deteriorates in short-packet communications, the performance gains of NOMA over traditional orthogonal multiple access are significant. Finally, we analyze the challenges and future trends in this emerging area.

Pilot-Less One-Shot Sparse Coding for Short Packet-Based Machine-Type Communications

This paper presents a novel transmission scheme to support massive machine-type communications (MTC) devices sending very short packets for Internet-of-Things (IoT) applications. The proposed scheme, termed as pilot-less one-shot (PLOS) transmission, does not require the pilot signaling. The key idea behind PLOS is to encode information into the inter-block nonzero positions and intra-block nonzero positions of a sparse vector. In the receiver, we propose a deep neural network-based scheme, referred to as deep learning-based PLOS (DL-PLOS) to recover the nonzero positions of the sparse vector. From the simulations results, we demonstrate that PLOS is effective in the short packet transmission and DL-PLOS outperforms the conventional greedy algorithms.

Bayesian Receiver Design for Grant-Free NOMA With Message Passing Based Structured Signal Estimation

Grant-free non-orthogonal multiple access (NOMA) is promising to achieve low latency massive access in Internet of Things (IoT) applications. In grant-free NOMA, pilot signals are often used for user activity detection (UAD) and channel estimation (CE) prior to multiuser detection (MUD) of active users. However, the pilot overhead makes the communications inefficient for IoT devices with sporadic transmissions and short data packets, or when the channel coherence time is short. Hence, it is desirable to improve the efficiency by avoiding the use of pilot signals, which can also further achieve lower latency. This work focuses on Bayesian receiver design for grant-free low density signature orthogonal frequency division multiplexing (LDS-OFDM), where each user is allocated a unique low density spreading sequence. We propose to use the low density spreading sequences for active user detection, thereby avoiding the use of pilot signals. Firstly, the task of joint UAD, CE and MUD is formulated as a structured signal estimation problem. Then message passing based Bayesian approach is developed to solve the structured signal estimation problem. In particular, belief propagation (BP), expectation propagation (EP) and mean field (MF) message passing are used to develop efficient hybrid message passing algorithms to achieve trade-off between performance and complexity. Simulation results demonstrate the effectiveness of the proposed receiver for grant-free LDS-OFDM without the use of pilot signals.