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.

With the rapid advancement of robotics technologies, a group of robots are able to communicate with one another by wireless transmissions and form a robot swarm. Robot swarm has many applications and a typical one is target search in which swarm robots are sent to places that might be dangerous for human workers, and they coordinate with one another to search for targets such as survivors in a disaster. However, motion coordination of swarm robots for target search has received little attention especially when the targets are mobile. In this work, we develop a motion coordination algorithm for swarm robots to search for targets in an unknown area. Our basic idea is to divide the search area into grids and build a gray-scale map in which each grid is associated with a gray scale indicating the efficiency of searching targets in this grid. The Voronoi diagram is adopted to coordinate swarm robots to search different portions of the search area for maximizing search efficiency. By theoretical analysis, our motion coordination algorithm is validated to ensure that all static targets are guaranteed to be found. We derive an upper-bound on the total time for robots to traverse all the grids in the search area. Extensive simulations are conducted and the results show that the proposed motion coordination algorithm outperforms the state-of-the-art and achieves a success rate of over 90% in finding all mobile targets with low search latency.

Rising concerns about data privacy and volume have driven the development of encrypted and compressed key-value (KV) storage systems. To defend against pattern-analysis attacks, the length and access frequency distributions of packs should appear uniform to adversaries. The design of the packing algorithm is crucial because it determines both pack length and frequency distributions, thereby impacting the overhead for hiding pack pattern information. Existing algorithms focus on minimizing length differences, leading to large variations in pack frequency and thus causing large bandwidth overhead. In this paper, we study the optimal packing problem for encrypted and compressed KV stores, aiming to minimize the overheads for protecting both pack length and frequency information. We propose DualPacking, a two-dimensional packing algorithm with an approximation ratio that depends on the length and frequency distributions of KV pairs. We further develop an encrypted and compressed KV storage system that adapts well to dynamic updates of outsourced stores. Finally, we formally analyze the security of our design and implement it on Redis and RocksDB. Experimental results indicate that, compared to existing packing algorithms, our design reduces the bandwidth overhead by up to 25% and the storage overhead for pack length protection by 33%, confirming its superior efficiency.

Timely updating of Internet of Things (IoT) data is crucial for achieving immersion in vehicular metaverse services. However, challenges such as latency caused by massive data transmissions, privacy risks associated with user data, and computational burdens on metaverse service providers (MSPs) hinder the continuous collection of high-quality data. To address these challenges, we propose an immersion-aware model trading framework that enables efficient and privacy-preserving data provisioning through federated learning (FL). Specifically, we first develop a novel multi-dimensional evaluation metric for the immersion of models (IoM). The metric considers i) the freshness and accuracy of the local model, and ii) the amount and potential value of raw training data. Building on the IoM, we design an incentive mechanism to encourage metaverse users (MUs) to participate in FL by providing local updates to MSPs under resource constraints. The trading interactions between MSPs and MUs are modeled as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains, where MSPs as leaders determine rewards, while MUs as followers optimize resource allocation. To ensure privacy and adapt to dynamic network conditions, we develop a distributed dynamic reward algorithm based on deep reinforcement learning, without acquiring any private information from MUs and other MSPs. Experimental results show that the proposed framework outperforms state-of-the-art benchmarks, achieving improvements in IoM of 38.3% and 37.2%, and reductions in training time to reach the target accuracy of 43.5% and 49.8%, on average, for the MNIST and GTSRB datasets, respectively. These findings validate the effectiveness of our approach in incentivizing MUs to contribute high-value local models to MSPs, providing a flexible and adaptive scheme for data provisioning in vehicular metaverse services.

Robots are widely used for target search in applications such as search and rescue, environmental monitoring, and surveillance. Existing search algorithms typically rely on target signals or predictable movement patterns, which might not be available in real applications. In this paper, we address the practical challenge of a target search problem where targets do not emit signals and have unpredictable movement patterns. The problem is formulated as an area coverage problem: how to maximize the coverage of the robots’ detection areas within a limited time. We propose an algorithm that divides the search area into multiple partitions and assigns specific partitions to robots for maximizing their coverage and the success rate of target detection. Within each partition, the random walk technique is adopted by the robots to handle robot failures and unknown obstacles. Through theoretical analysis and experiments, we explore the optimal number of partitions as well as the optimal partition shape to facilitate searching. Extensive simulations across different dynamic environments validate the effectiveness and adaptability of our proposed algorithm.

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.

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 investigates the information freshness of joint status updates, quantified by the age of collection (AoC), in uplink coordinated direct and relay transmission (CDRT). In an uplink CDRT setup, a direct sensor communicates directly with the destination, while a relay-aided sensor relies on a decode-and-forward relay. The update packet of each sensor contains partial information about a common observation target. The AoC measures the time elapsed since the generation of the latest set of update packets received at the destination. Hence, unlike the age of information (AoI) metric, the AoC decreases only when all update packets from multiple sources for a common observation are collected (i.e., a successful joint update). When simultaneous transmissions from the direct and relay-aided sensors cause packet collisions at the relay, conventional multiuser decoding (MUD) is usually used to decode native packets explicitly from the superimposed signals. Nevertheless, MUD does not work well when the signal-to-noise ratios (SNRs) of different sensors are (nearly) equal. To this end, this paper puts forth a physical-layer network coding (PNC)-aided CDRT scheme for joint information updating, utilizing both MUD and PNC decoders. The PNC decoder decodes superimposed signals into network-coded packets, particularly effective when the SNRs of different sensors are close. We design an automatic repeat request (ARQ) protocol tailored to low AoC and study how network-coded packets can be utilized to reduce the AoC of uplink CDRT, where the closed-form peak AoC formula is derived. We evaluate the PNC-aided CDRT scheme using software-defined radios. Experimental results show that our PNC-aided CDRT scheme significantly reduces the average peak AoC under various SNR conditions.

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.

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.

The high mobility feature of vehicular networks poses tremendous challenges to maintaining network connectivity. In this paper, we investigate the possibility of enhancing the connectivity of Cellular Vehicle-to-Everything (C-V2X) networks through distributed trajectory adjustment. Based on a physical layer abstraction model, we characterize the network connectivity enhancement problem as a network utility maximization and study its concavity. We propose a distributed trajectory updating algorithm that dynamically adjusts the trajectory of vehicles on top of their planned trajectory. The algorithm is distributed and requires only geo-location exchanges, which are readily available in V2X networks. Simulation results show that the mobility updating algorithm converges and improves the aggregated network utility by up to 48% compared to the scenarios without mobility tuning.

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 paper puts forth an interference mitigation scheme, named signal-aligned network coding (SNC), for both uplink and downlink cloud radio access networks (C-RANs). The base stations (BSs) are connected to a central processor (CP) via digital links with individual limited capacities. The user equipments (UEs) communicate with the CP through BSs serving as relay nodes. We focus on the situation that the digital links have moderate rate constraint. Loosely speaking, the capacities of the digital links and the wireless links are of the same order of magnitude. The SNC scheme strategically aligns the signals by signal alignment (SA) and then decodes the aligned signals by physical-layer network coding (PNC). With the proper designs of the network-coded messages transmitted and the alignment of signals, the SNC scheme significantly mitigates the inter-cell interference. No matter the size of the network, the SNC scheme achieves full degrees of freedom (DoF) asymptotically. For the finite signal-to-noise ratio (SNR) performances, simulation results show that the SNC scheme achieves superior sum-rate than the conventional compute-and-forward scheme in the two-user and the three-user cases, especially in the high SNR regime.

This paper proposes the ideas of interference alignment with physical-layer network coding (IAPNC). While conventional interference alignment (IA) aligns interfering signals and then treats them as noise, IAPNC treats the aligned signals as combined signals and utilizes them to convey data streams to receivers. We show the ideas of IAPNC in multi-hop multiple-input multiple-output (MIMO) channels consisting of 3 transmitter-receiver pairs and 2 half-duplex decode-and-forward (DF) relays in each intermediate layer, i.e., 3-2 ... 2-3 MIMO channels. We consider three users want to convey independent data streams to distinct destinations with the aid of relays. Simulation results show that IAPNC scheme in the 3-2-3 MIMO channel achieves the same degrees of freedom (DoF) as conventional IA scheme in the 3-3-3 MIMO channel which has one more relay. The results also show that the end-to-end sum-rate of IAPNC scheme outperforms that of zero-forcing (ZF) filtering scheme in medium-to-high signal-to-noise ratio (SNR) regime for the 3-2-3 MIMO channel. The performance improvement of IAPNC scheme mainly comes from efficient utilization of signals in interfering signal subspaces.