With rapid advancements in large language models (LLMs), AI-generated content (AIGC) has emerged as a key driver of technological innovation and economic transformation. Personalizing AIGC services to meet individual user demands is essential but challenging for AIGC service providers (ASPs) due to the subjective and complex demands of mobile users (MUs), as well as the computational and communication resource constraints faced by ASPs. To tackle these challenges, we first develop a novel multi-dimensional quality-of-experience (QoE) metric. This metric comprehensively evaluates AIGC services by integrating accuracy, token count, and timeliness. We focus on a mobile edge computing (MEC)-enabled AIGC network, consisting of multiple ASPs deploying differentiated AIGC models on edge servers and multiple MUs with heterogeneous QoE requirements requesting AIGC services from ASPs. To incentivize ASPs to provide personalized AIGC services under MEC resource constraints, we propose a QoE-driven incentive mechanism. We formulate the problem as an equilibrium problem with equilibrium constraints (EPEC), where MUs as leaders determine rewards, while ASPs as followers optimize resource allocation. To solve this, we develop a dual-perturbation reward optimization algorithm, reducing the implementation complexity of adaptive pricing. Experimental results demonstrate that our proposed mechanism achieves a reduction of approximately$64.9\%$in average computational and communication overhead, while the average service cost for MUs and the resource consumption of ASPs decrease by$66.5\%$and$76.8\%$, respectively, compared to state-of-the-art benchmarks.

This paper investigates the state synchronization probability (SSP) of a goal-oriented relay-assisted real-time state tracking system, where a source node monitors a discrete-time Markov chain and transmits state updates either directly to a destination or via a relay. The goal-oriented approach emphasizes timely and accurate delivery of semantically significant updates to support real-time decision-making. While relay-assisted communication improves coverage and reliability, its impact on SSP within a goal-oriented framework remains unexplored. To the best of our knowledge, this is the first study to evaluate SSP in such a system. In relay-assisted state tracking, possible simultaneous transmissions from the source and relay result in superimposed signals at the destination, necessitating Successive Interference Cancellation (SIC) for decoding. Notably, decoding the source's packet depends on prior successful decoding of the relay's packet, even though the source carries the most recent state information. This dependency highlights SIC's critical role and motivates further exploration of its impact on SSP. To analyze SSP, we model the SIC-enabled relay-assisted system using a Markov chain and derive its theoretical expression. Results show that incorporating SIC significantly enhances SSP. Without SIC, interference from simultaneous transmissions can degrade performance, making relay-assisted schemes less effective than direct transmission. These findings highlight SIC's pivotal role in enabling efficient relay-assisted state tracking systems.

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k≥0 at the relay. Here, k=0, k=1, and k>1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI.

Ambient backscatter communication (ABC) enables low-cost and energy-efficient connectivity for Internet of Things (IoT) devices by leveraging ambient radio-frequency (RF) signals. However, the passive nature and open wireless medium of ABC systems make them vulnerable to detection by unauthorized receivers (wardens). To mitigate this risk, covert communication, which conceals transmissions by embedding them within noise, offers a promising security enhancement for ABC systems. This paper proposes a jammer-assisted reflection coefficient optimization framework to enhance the covertness and reliability of ABC systems with an endogenous warden and an external jammer. Specifically, we consider two distinct jamming patterns: uniformly distributed and truncated exponentially distributed artificial noise power. We derive closed-form expressions for both the outage probability of the backscatter link and the minimum detection error rate at the warden under these jamming patterns. Based on these expressions, we determine the optimal reflection coefficients that maximize the effective covert rate while satisfying a predefined covertness constraint. Additionally, we introduce the concept of jamming cost to evaluate the efficiency and applicability of different jamming patterns in terms of the required jamming power to achieve a desired level of covertness. Numerical results validate the effectiveness of the proposed optimization framework and reveal that while uniform jamming provides stronger covertness and lower jamming cost, truncated exponential jamming achieves a lower outage probability. These findings provide key insights for designing secure and efficient ABC systems across diverse IoT deployment scenarios.

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays.

This paper puts forth a new metric, dubbed channel cycle time (CCT), to measure the short-term fairness of communication networks. CCT characterizes the average duration between two consecutive successful transmissions of a user, during which all other users successfully accessed the channel at least once. In contrast to existing short-term fairness measures, CCT provides more comprehensive insight into the transient dynamics of communication networks, with a particular focus on users’ delays and jitter. To validate the efficacy of our approach, we analytically characterize the CCTs for two classical communication protocols: slotted Aloha and CSMA/CA. The analysis demonstrates that CSMA/CA exhibits superior short-term fairness over slotted Aloha. Beyond its role as a measurement metric, CCT has broader implications as a guiding principle for the design of future communication networks by emphasizing factors like fairness, delay, and jitter in short-term behaviors.

This paper puts forth SemFusion, a semantic information fusion and communication framework for two-hop multisource relay networks. Although deep learning-based semantic communication is considered a new paradigm for next-generation communication networks, most prior works have focused on single-source scenarios, especially in relay networks. In contrast, we investigate a multi-source scenario where multiple sensors monitor the same scene from different angles and send partial images to the destination via a relay. The destination receives partial images of the monitored scene to reconstruct the complete image. Empowered by semantic communication, in the first hop, SemFusion allows only a subset of sensors to send their semantic information of the partial images to the relay. In the second hop, instead of forwarding the semantic information of each sensor separately, the relay further performs semantic information fusion so that only the most valuable semantic information is sent to the destination. Moreover, in contrast to the conventional end-to-end training method used in semantic communication, we propose a two-stage training strategy, where each stage corresponds to one hop, to improve the training efficiency of SemFusion. Experiments indicate that SemFusion significantly saves communication resources and provides better image reconstruction quality than state-of-the-art semantic forwarding strategies.

We investigate the information freshness, measured by age of information (AoI), of a linear multi-hop wireless network, where a source tries to deliver status update packets to a destination via a series of half-duplex relays. Sending update packets frequently from the source is critical to achieve a low AoI at the destination. However, if the packet generation rate is not designed properly, an intermediate relay may receive mutual wireless interference from its previous and subsequent nodes. When the previous node sends a new packet to the intermediate relay, the subsequent node simultaneously forwards an old packet to the next hop in which the wireless signal is also received by the intermediate relay. A key motivation to solve this problem is that the intermediate relay has previously received the old packet (which thus can be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be used to remove the interference of the old packet and then recover the new packet. Taking a three-hop network as an example, we design an SIC-enabled packet relaying scheme tailored to low AoI, which can be generalized to scenarios beyond three hops. We model the three-hop relaying scheme using a Markov chain to derive the theoretical average AoI (AAoI) and peak AoI (PAoI). Theoretical and simulation results show that the SIC-enabled packet relaying scheme significantly reduces the AAoI and PAoI compared to the non-SIC counterpart, thanks to a more frequent packet generation rate at the source and the SIC technique at the relays.

Cooperative perception has been shown an effective approach to address the perception limitation problem faced by the individual perception in the era of autonomous driving. Considering a two-source two-hop cooperative perception vehicular network, we investigate the average age of information (AoI) with different automatic repeat-request (ARQ) strategies in this paper. We formulate the packet transmission process as a Markov chain, which allows us to establish linear equations about the first and second moments of all states’ residual waiting time for the destination to receive a new update packet. Then, we derive the average AoI based on the analytical solution to the equations. Furthermore, the analysis is applied to a special case of the single-source two-hop cooperative perception system. Simulation results show that the optimal strategy is neither non-ARQ nor infinite-ARQ, but truncated ARQ, which means that there exists a finite retransmission limit to minimize the average AoI, implying that better cooperative perception can be achieved by optimizing the retransmission limit, especially in the single-source two-hop situation.

This paper introduces a dynamic-frame time division multiple access (DF-TDMA) scheme aimed at decreasing the age of collection (AoC) in collaborative monitoring scenarios. Unlike the conventional age of information (AoI) metric, AoC decreases only when partial information from multiple sources is aggregated to form a complete observation. Previous studies on AoC predominantly assumed that complete observations are derived from aggregating information from all sources. However, this assumption does not hold in scenarios where sources are correlated, and a complete observation can be achieved with partial information from only a subset of sources. To address this, new channel access protocols are necessary to achieve a low network-wide average peak AoC. DF-TDMA is proposed as a solution, featuring dynamically adjustable TDMA frame sizes to minimize time wastage and thereby reduce AoC. While the dynamic frame size adjustment complicates AoC analysis, we theoretically derive the average peak AoC of DF-TDMA. Simulations show that DF-TDMA maintains a stable average peak AoC across varying numbers of sources N, and notably reduces the average peak AoC compared to a fixed-frame TDMA scheme, particularly under conditions with a large N.

This paper studies information freshness in collaborative surveillance scenarios operated with non-orthogonal multiple access (NOMA), where each monitoring device observes a portion of a common target and reports its latest status on the target to a common access point (AP) to recover the complete observation. We use age of collection (AoC) as a metric of information freshness. Unlike the conventional age of information (AoI) metric, the instantaneous AoC decreases only when the AP receives all partial updates from different devices (i.e., successfully receives a “joint” update). Conventional NOMA schemes typically use multiuser decoding (MUD) techniques to decode update messages from different devices. However, MUD does not work well when the signal-to-noise ratios (SNRs) of different NOMA users are (nearly) balanced. Therefore, we consider network-coded multiple access (NCMA), an advanced NOMA scheme that integrates MUD with physical-layer network coding (PNC). PNC is a technique that turns wireless interferences into useful network-coded information, which works well even when the SNRs of different users do not differ much. Experimental results on software-defined radios indicate that NCMA is a practical solution for achieving low average AoC under different channel conditions. This is the first study to show that NCMA, thanks to the combination of MUD and PNC, can receive joint update messages in a shorter period of time, thus significantly reducing the average AoC of the system.

This paper puts forth a new metric, dubbed channel cycle time (CCT), to measure the short-term fairness of Communication networks. CCT characterizes the average duration between two consecutive successful transmissions of a user, during which all other users successfully accessed the channel at least once. In contrast to existing short-term fairness measures, CCT provides more comprehensive insight into the transient dynamics of communication networks, with a particular focus on users' delays and jitter. To validate the efficacy of our approach, we analytically characterize the CCTs for two classical commu-nication protocols: slotted Aloha and CSMA/CA. The analysis demonstrates that CSMA/CA exhibits superior short-term fairness over slotted Aloha. Beyond its role as a measurement metric, CCT has broader implications as a guiding principle for the design of future communication networks by emphasizing factors like fairness, delay, and jitter in short-term behaviors.

This article investigates the information freshness of Industrial Internet of Things (IIoT) systems, where each IoT device makes a partial observation of a common target and transmits the information update to a central receiver to recover the complete observation. We consider the Age of Collection (AoC) performance as a measure of information freshness. Unlike the conventional Age of Information (AoI) metric, the instantaneous AoC decreases only when all cooperative packets for a common observation are successfully received. Hence, effectively allocating wireless time-frequency resources among IoT devices to achieve a low average AoC at the central receiver is paramount. Three multiple access schemes are considered in this article: 1) time-division multiple access (TDMA) without retransmission; 2) TDMA with retransmission; and 3) frequency-division multiple access (FDMA). First, our theoretical analysis indicates that TDMA with retransmission outperforms the other two schemes in terms of average AoC. Subsequently, we implement information update systems based on the three schemes on software-defined radios. Experimental results demonstrate that considering the medium access control (MAC) overhead in practice, FDMA achieves a lower average AoC than TDMA with or without retransmission in the high signal-to-noise ratio (SNR) regime. In contrast, TDMA with retransmission provides a stable and relatively low average AoC over a wide SNR range, which is favorable for IIoT applications. Overall, we present a theoretical-plus-experimental investigation of AoC in IIoT information update systems.

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devicesK. This is sharply different from the activity detection problem in traditional setups, in which the knowledge ofKis not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator forKand a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation ofKare closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation ofKand the design of the ADC quantizer; 2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol.

We study the relay selection problem for information freshness-oriented two-way relay networks (TWRNs) operated with physical-layer network coding (PNC). Information freshness is quantified by age of information (AoI), defined as the time elapsed since the generation time of the latest received information update. Since PNC leads to mutual wireless interference in TWRNs, this complicates the relay selection for users. To address this problem, this paper formulates relay selection as a multi-armed bandit (MAB) to dynamically learn the optimal mapping between users and relays. Specifically, the two end users act as agents, interacting with the environment, receiving feedback as rewards in the MAB, and then optimizing the system-level AoI performance through the learning experience. Simulation results demonstrate that the proposed MAB approach significantly outperforms the conventional relay selection scheme.

The integration of Ambient Backscatter Communication (ABC) with covert communication is expected to support emerging Internet of Things (IoT) applications (e.g., Radio Frequency (RF)-powered networks) due to the need for low-cost connectivity and confidential transmission. In general, the purpose of covert communication is to hide the existence of the RF-powered wireless link to ensure the information security of the ABC link. However, the ABC link may have a high rate requirement, thus inevitably increasing the risk of information leakage. Hence, this paper considers jammer-aided endogenous covert communication, where an RF tag sends information covertly to an ABC receiver and exploits the jammer's Artificial Noise (AN) under the supervision of a warden-like legacy receiver. To obtain the maximum data rate of the backscatter link without being detected, we derive the minimum detection error rate of the warden and the outage probability of the backscatter link under random channel fading and the jammer's AN, respectively. Then, we optimize the tag's reflection coefficient to maximize its effective covert rate under the covert constraint based on the warden's mean detection error rate. Since the optimal reflection coefficient cannot be solved directly, monotonicity analyses of the objective and the constraint with respect to the reflection coefficient are adopted to achieve an efficient solution. Numerical results demonstrate the effectiveness of the optimized reflection coefficient for the jammer-aided system.

This paper studies random access protocols for timely status update systems with information freshness requirements, measured in terms of age of information (AoI). Considering an extensive network, a fundamental problem is how to schedule massive transmitters to access the wireless channel to achieve low network-wide AoI. In conventional packet-based random access protocols, transmitters contend for the channel by sending the whole data packet. When the packet duration is long, the time wasted due to packet collisions is considerable. In contrast, connection-based random access protocols first establish connections with the receiver before the data packet is transmitted. From the information freshness perspective, there should be conditions favoring either side. We present a comparative study of the average AoI of packet-based and connection-based random access protocols. Specifically, we consider frame slotted Aloha (FSA) as a representative of packet-based random access and design a request-then-access (RTA) protocol for connection-based random access. Our analyses indicate that whether packet-based or connection-based protocols should be used depends mainly on the payload size of data packets. Except for the case where the payload size of an update packet is very tiny, RTA outperforms FSA in average AoI because the established connections help avoid direct collisions of data packets.

In a two-way relay channel (TWRC), physical-layer network coding (PNC) doubles the system throughput by turning superimposed signals transmitted simultaneously by different end nodes into useful network-coded information (known as PNC de-coding). Prior works indicated that the PNC decoding performance is affected by the relative phase offset between the received signals from different nodes. In particular, some "bad" relative phase offsets could lead to huge performance degradation. Previous solutions to mitigate the relative phase offset effect were limited to the conventional bit-oriented communication paradigm, aiming at delivering a given information stream as quickly and reliably as possible. In contrast, this paper puts forth the first semantic communication-empowered PNC-enabled TWRC to address the relative phase offset issue, referred to as SC-PNC. Despite the bad relative phase offsets, SC-PNC directly extracts the semantic meaning of transmitted messages rather than ensuring accurate bit stream transmission. We jointly design deep neural network (DNN)-based transceivers at the end nodes and propose a semantic PNC decoder at the relay. Taking image delivery as an example, experimental results show that the SC-PNC TWRC achieves high and stable image reconstruction quality under different channel conditions and relative phase offsets, compared with the conventional bit-oriented counterparts.

Information freshness, characterized by Age of Information (AoI), is important for sensor applications involving timely status updates. In many cases, the wireless signals from one sensor can be received by multiple access points (APs). This article investigates the average AoI for cooperative APs (Co-APs), in which they can share information through a wired backbone network. We first study a basic backbone-assisted Co-AP system where APs share only decoded packets. Experimental results on software-defined radios (SDRs) indicate that Co-AP significantly improves the average AoI performance over a single-AP system. Next, we investigate an improved Co-AP system, called Soft-Co-AP. In addition to sharing decoded packets, Soft-Co-AP shares and collects soft information of packets that the APs fail to decode for further joint decoding. A critical issue in Soft-Co-AP is determining the number of quantization bits that represent the soft information (each soft bit) shared over the backbone. While more quantization bits per soft bit improves the joint decoding performance, it leads to higher backbone delay. We experimentally study the average AoI of Soft-Co-AP by evaluating the tradeoff between the backbone delay and the number of quantization bits. SDR experiments show that when the number of sensors is large, Soft-Co-AP further reduces the average AoI by 12% compared with Co-AP. Interestingly, good average AoI performance is usually achieved when the number of quantization bits per soft bit is neither too large nor too small.

This paper investigates the information freshness of two-way relay networks (TWRNs) operated with physical-layer network coding (PNC). Information freshness is quantified by age of information (AoI), defined as the time elapsed since the generation time of the latest received information update. PNC reduces the communication latency of TWRNs by turning superimposed electromagnetic waves into network-coded messages so that end users can send update packets to each other more frequently via the relay. While sending update packets more frequently has the potential to reduce AoI, how to handle packet corruption in TWRNs has not been investigated. Specifically, if an old packet is corrupted in any hop of a TWRN, one needs to decide whether to drop or to retransmit the old packet, e.g., a new packet has more recent information but may take more time to be delivered. Therefore, we study the average AoI with and without automatic repeat request (ARQ) in PNC-enabled TWRNs. Interestingly, our analysis shows that neither the non-ARQ scheme nor the pure ARQ scheme achieves a good average AoI. Hence, we put forth an uplink-lost-then-drop (ULTD) protocol that combines packet drop and ARQ. Experiments on software-defined radios indicate that ULTD significantly outperforms non-ARQ and pure ARQ schemes in terms of average AoI, especially when the two end users have imbalanced channel conditions. We believe the insight of ULTD on TWRNs generally applies to other two-hop networks: to achieve high information freshness, when packets are corrupted in the first hop, new packets should be generated and sent (i.e., old packets are discarded); when packets are corrupted in the second hop, old packets should be retransmitted until they are successfully received.

This paper presents a low-complexity physical-layer network coding (PNC) enabled non-orthogonal multiple access (NOMA) system with the help of deep neural networks (DNN). NOMA allows multiple users to send packets simultaneously to a common access point (AP) using the same frequency band. In PNC-enabled NOMA systems, the AP decodes not only individual packets of different users by conventional multiuser decoding (MUD) techniques, but also different linear combinations of individual packets by PNC decoding, referred to as PNC packets. Prior works showed that the decoded PNC packets could significantly improve the system throughput. However, when the number of simultaneously transmitting users increases, the number of possible PNC packets also increases exponentially, leading to high decoding complexity if the AP tries to blindly decode all possibilities. Therefore, this paper exploits DNNs to reduce the decoding complexity. Specifically, we find that the decoding results of different linear combinations are mainly affected by the relative phase offsets among the wireless signals of different users. Hence, we can use a DNN to learn the relationships between the relative phase offsets and the decoding results. Since our DNN can learn the decoding patterns, i.e., which linear combinations are more likely to be decoded given the same relative phase offsets, the AP can attempt to decode only a subset of all the linear combinations, thus reducing the decoding complexity. Experimental results show that our DNN-assisted decoding scheme reduces the decoding complexity by more than 30% compared with the traditional brute-force approach, while maintaining almost the same throughput performance.

This paper investigates the information freshness of two-hop networks. Age of information (AoI) is used as the metric to characterize the information freshness, defined as the time elapsed since the latest received status update was generated. In error-prone wireless networks, prior studies indicated that Automatic Repeat-reQuest (ARQ) does not help improve the average AoI performance of single-hop networks, because sending a new packet always carries the most up-to-date information (i.e., discarding the old packet). We believe that this observation does not apply to two-hop networks. For example, when a packet transmission fails in the second hop, although a new packet has more recent information, it may require more time to be delivered (i.e., the communication has to restart from the first hop), thus leading to a high AoI. This paper analyzes the theoretical average AoI of two-hop networks with and without ARQ. Specifically, we model the two schemes using Markov chains, from which we derive the average AoI. Our theoretical and simulation results confirm that, unlike single-hop networks, ARQ should be used in two-hop networks to achieve lower average AoI. In particular, when ARQ is used, the successful decoding probability of the second hop has a greater impact on the average AoI than that of the first hop. Overall, our findings provide insight into the ARQ design for two-hop timely status update systems.

Rarity is known to be a factor in the price of non-fungible tokens (NFTs). Most investors make their purchasing decisions based on the rarity score or rarity rank of NFTs. However, not all rare NFTs are associated with a higher price, especially for play-to-earn gaming NFTs. In this paper, we studied the top-ranked play-to-earn gaming NFTs on Axie Infinity. We found that, in addition to rarity, utility is also a significant factor influencing the price. Furthermore, we use utility as a predictor to predict the price of Axies using the XGBoost regressor. Our results reveal that, compared to using rarity-based predictors only, leveraging utility-based predictors can improve the prediction accuracy, thus highlighting utility as a price determinant for play-to-earn gaming NFTs.

The paper investigates the information freshness and energy harvesting (EH) in downlink Internet of Things (IoT) networks. Information freshness is measured by Age of Information (AoI). We consider a scenario where an access point periodically sends short packets to N independent IoT devices. All the devices are equipped with capacitors to store energy through wireless power transfer (WPT) when the devices receive signals conveying unwanted packets. Conventionally, short packets for multiple devices are jointly coded into one larger packet to improve reliability. However, whether short packets should be jointly coded to reduce average AoI and improve EH performance has not been well investigated. On the one hand, a larger number of packets jointly coded decreases the packet error rate (PER), which may reduce the average AoI because the time to successfully receive the next update may be shorter. On the other hand, more packets jointly coded cause less time for WPT, as the devices spend most of their time receiving update packets, resulting in poor EH performance. Therefore, we investigate the tradeoff between AoI and EH by examining the number of packets to be jointly coded. Closed-form AoI and EH formulas are derived. Numerical results show that there exist optimal numbers of jointly coded packets that can achieve both high information freshness and high EH at the same time.

This paper uses the Age of Information (AoI) to investigate the information freshness of non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC). We consider two sensors sending update packets to a common access point. The SIC decoder exploits the different powers of the signal received from the sensors to separate their signals, e.g., the signal from the strong sensor is decoded first, followed by the weak sensor. A key issue in analyzing the AoI of SIC-based NOMA is that the weak sensor needs to wait for the successful decoding of the strong sensor before decoding its own packet. In particular, the information rates of the two sensors in SIC-based NOMA may differ due to the different received signal powers, resulting in different packet durations. Therefore, one sensor may send more packets than the other sensor during the same period, and the SIC operation complicates the AoI analysis further. To this end, this paper puts forth algorithms based on different information rates of sensors to compute the average AoI of SIC-based NOMA. Numerical results show that when the received signal powers are significantly different, SIC-based NOMA outperforms orthogonal multiple access (OMA) schemes, such as time division multiple access (TDMA) and frequency division multiple access (FDMA), in terms of average AoI.

This paper investigates the effectiveness of candlestick patterns in cryptocurrency trading. Our data set includes historical daily opening, high, low, and closing prices of the top 23 cryptocurrencies by market capitalization. We examine 68 commonly used candlestick patterns using statistical analysis and find that the studied candlestick patterns are of little use in cryptocurrency trading. On the contrary, there are more patterns with relatively low accuracy. Investors should be cautious with their trading strategies and decisions when these patterns appear, as they may be a false trading signal that could cause losses rather than gains. To the best of our knowledge, this paper is one of the first research studies to investigate the effectiveness of candlestick patterns in cryptocurrency trading. Our findings could serve as a reference for investors when developing cryptocurrency trading strategies.

This article puts forth a new channel coding paradigm for multi-source information streams with Age of Information (AoI) requirements. The recently introduced AoI metric characterizes the freshness of information, defined as the time elapsed since the generation of the last successfully received update. We study a setup in which a large number of sensors want to send update information to a common monitor with the help of aggregators. Specifically, an aggregator collects update packets from sensors and forwards them to the monitor. Conventional block codes (such as LDPC codes) that encode and decode each update packet separately do not perform well in such an information aggregation and update scenario. When update packets suffer from packet loss, we show that block codes lead to high instantaneous AoI because a sensor waits for a long time for the next update opportunity. This article investigates stream-based codes to tackle this problem. A distinguishing feature of stream-based codes is the joint encoding of update packets from different sensors, and a series of coded packets are sent continuously like a stream. Different update packets are then jointly decoded using multiple coded packets from the stream. A key challenge with AoI requirements is the joint design of error corrections of old packets and fast decodings of new packets. We design a practical encoding-decoding scheme and a sliding decoding window mechanism to control the decoding complexity. We evaluate two AoI metrics, average AoI and bounded AoI. In particular, bounded AoI corresponds to an AoI threshold that the instantaneous AoI is below a large percentage of the time. Experimental results on software-defined radio show that stream-based codes significantly outperform block codes in both average AoI and bounded AoI under varying channel conditions. Overall, stream-based codes provide a viable channel coding solution to multi-source information streams with timely update requirements.

This article studies information freshness in information update systems with nonorthogonal multiple access (NOMA). Information freshness is characterized by age of information (AoI), defined as the time elapsed since the generation of the last successfully received update. Conventional orthogonal multiple access (OMA) systems, say time-division multiple access (TDMA) systems, lead to high average AoI when a large number of users take turns to transmit their latest samples to a common receiver over a wireless medium. In contrast to OMA, NOMA allows multiple users to transmit simultaneously. Although NOMA could lead to higher packet error rates (PER) due to the wireless interference among users, we show that higher PERs do not always lead to a higher average AoI. Specifically, our experiments on software-defined radio indicate that NOMA with conventional multiuser decoding (MUD) techniques leads to higher PERs but lower average AoI than OMA does in the high SNR regime. Furthermore, to improve the AoI performance in the medium SNR regime, we combine MUD with physical-layer network coding (PNC), a technique that turns wireless interference into useful network-coding information. PNC works well even when the SNRs of different NOMA users do not differ much. This article is the first attempt to apply PNC to information update systems. Experiments show that the combined use of PNC and MUD reduces the average AoI significantly in a practical network setting. Overall, PNC-enabled NOMA is a promising solution to information update systems.

This article presents a cross-layer design of backbone-assisted wireless local area network (WLAN) for dense WLAN deployment. The popularity of 802.11-based WLANs leads to dense WLAN deployment in geographically limited space, including dense access points (AP) and dense users. With dense APs, an AP could overhear packets destined for other APs. Backbone-assisted WLAN is a new system architecture where cooperative APs share the overheard packets through a backbone network, thereby reducing packet retransmission and improving system throughput. Conventional WLAN, such as Wi-Fi, uses Stop-and-Wait ARQ. This article argues that Stop-and-Wait does not work well with backbone-assisted WLAN because of large backbone delays. We first show that with a variant of Selective Repeat ARQ tailored for backbone-assisted WLAN, a single-user backbone-assisted WLAN system can achieve substantial throughput improvement over that with Stop-and-Wait ARQ. Then, we put forth a new system architecture targeted for dense users, referred to as network-coded backbone-assisted WLAN, in which multiple users are allowed to transmit simultaneously. A distinguishing feature of this system is the joint use of physical-layer network-coding (PNC) decoding and multiuser decoding (MUD) in multipacket reception. This article is the first attempt to design an ARQ for multiuser backbone-assisted WLAN. Our overall system design solves a PNC sequence obfuscation problem and addresses long packet latency in Selective Repeat ARQ. Experiments on our software-defined radio prototype indicate that network-coded Ethernet-backbone-assisted WLAN can achieve high system throughput and low packet latency. Specifically, the system throughput can outperform an MUD-only multiuser WLAN and a single-user WLAN by 60 and 100 percent, respectively. Overall, we believe that network-coded backbone-assisted WLAN is a viable solution for boosting throughput and reducing latency in dense WLAN environments.

This paper presents the first network-coded multiple access (NCMA) downlink system on unmanned aerial vehicle (UAV). The use of UAV as a mobile aerial base station has received much attention in the 5G community in the context of highly mobile and flexible-configurable communication systems. As UAVs are limited by their flight time in the air, achieving high spectral and power efficiency while they are inflight is of great importance. Non-orthogonal multiple access (NOMA) is a promising technique to increase the spectral and power efficiency. Conventional NOMA downlink that makes use of superposition coding in combination with successive interference cancellation (SIC) decoding does not work well in scenarios where the channel conditions of different downlink users are not readily available at the transmitter side. This is the case, for example, in the UAV scenario in which the UAV transmitter moves quickly, causing the channel conditions to vary in a very dynamic manner. This paper investigates a new NOMA downlink architecture, referred to as network-coded multiple access. In the absence of channel information, an NCMA transmitter allocates equal power to the superposed signals of different downlink users. A key challenge is how to achieve high NOMA throughput under such equal power allocation. Toward this end, NCMA makes joint use of physical-layer network coding (PNC) and multiuser decoding (MUD) together with a new superposition coding scheme, referred to as NCMA-based superposition coding. In NCMA-based superposition coding, equal powers are allocated to the signals of different users, but a relative phase offset between the signals is introduced to optimize PNC and MUD decodings. To demonstrate the feasibility and advantage of the NCMA downlink, we implemented our designs on a software-defined radio and UAV. Our experimental results show that NCMA is robust against varying channel conditions. Moreover, the throughput of NCMA can outperform the state-of-the-art SIC-based superposition coding system and the time-division multiple access system by 50% and 80%, respectively, demonstrating that NCMA is a practical solution to boost throughput in UAV NOMA.

This paper presents the first Network-Coded Multiple Access (NCMA) system with multiple users adopting different signal modulations, referred to as rate-diverse NCMA. A distinguishing feature of NCMA is the joint use of physical-layer network coding (PNC) and multiuser decoding (MUD) to boost throughput of multipacket reception systems. In previous NCMA systems, users adopt the same modulation regardless of their individual channel conditions. This leads to suboptimal throughput for many practical scenarios, especially when different users have widely varying channel conditions. A rate-diverse NCMA system allows different users to use modulations that are commensurate with their channel conditions. A key challenge is the design of PNC mapping and decoding mechanisms in NCMA when different users adopt different modulations. While there have been past work on non-channel-coded rate-diverse PNC, this paper is the first attempt to design channel-coded rate-diverse PNC to ensure the reliability of the overall NCMA system. Specifically, we put forth a symbol-splitting channel coding and modulation design so that PNC/NCMA can work over different modulations. We implemented our rate-diverse NCMA system on software-defined radios. Experimental results show that the throughput of rate-diverse NCMA can outperform the state-of-the-art rate-homogeneous NCMA by 80%. Overall, the introduction of rate diversity significantly boosts the NCMA system throughput in practical scenarios.

This paper is a theoretical-plus-experimental investigation of practical 5G strategies for power-balanced non-orthogonal multiple access (NOMA). By allowing multiple users to share the same time and frequency, NOMA can scale up the number of served users and increase spectral efficiency compared with existing OMA. Conventional NOMA schemes with successive interference cancellation (SIC) do not work well when users with comparable received powers transmit together. To allow power-balanced NOMA (more exactly, near power-balanced NOMA), this paper investigates a new NOMA architecture, named network-coded multiple access (NCMA). A distinguishing feature of NCMA is the joint use of physical-layer network coding (PNC) and multiuser decoding to boost NOMA throughputs. We first show that a simple NCMA architecture in which all users use the same modulation, referred to as rate-homogeneous NCMA, can achieve substantial throughput improvement over SIC-based NOMA under near power-balanced scenarios. Then, we put forth a new NCMA architecture, referred to as rate-diverse NCMA, in which different users may adopt different modulations commensurate with their relative SNRs. A challenge for rate-diverse NCMA is the design of a channel-coded PNC system. This paper is the first attempt to design channel-coded rate-diverse PNC. Experimental results on our software-defined radio prototype show that the throughput of rate-diverse NCMA can outperform the state-of-the-art rate-homogeneous NCMA by 80%. Overall, rate-diverse NCMA is a practical solution for near power-balanced NOMA.

This paper presents the first network-coded multiple access (NCMA) system operated on higher- order modulations beyond BPSK. NCMA allows multiple nodes to transmit simultaneously to an access point (AP) to boost throughput of wireless local area networks (WLAN): the key idea is to jointly exploit multiuser decoding (MUD) and physical-layer network coding (PNC). High- order modulations are commonly adopted in WLAN systems when the signal-to-noise ratio (SNR) is medium or high. However, direct generalization of the existing NCMA decoding algorithm, originally designed for BPSK, to higher-order modulations will lead to huge performance degradation. We find that the throughput degradation is caused by the relative phase offset between received signals from different nodes. To circumvent the throughput degradation, this paper investigates an NCMA system with multiple receive antennas at the AP, referred to as MIMO-NCMA. We have implemented MIMO-NCMA on software-defined radios. Our experimental results show that, at SNR of 10dB, the throughput of MIMO-NCMA outperforms single-antenna NCMA and conventional distributed MIMO-MUD, respectively. We believe that MIMO-NCMA throughput can be further improved with modulations beyond QPSK (e.g., 64-QAM).