This work investigates the time-dependent statistical characteristics (SCs) of age of information (AoI) in V2I periodic-updating. The D/G/1/1 (non-)preemptive discrete-time (slotted) queue model is employed. The entire AoI process is split into a set of slot-specific AoI processes w.r.t. updating period (UP). A Markov multi-dimensional age process w.r.t. UP is used to track the evolution of slot-specific AoI. Accordingly, the time-dependent AoI distribution and expected AoI (EAoI) are derived, forming a framework to study the time-dependent AoI SCs under periodic updating. Based on the AoI SCs, we optimize the decision-making (DM) process at roadside unit to minimize age upon decisions, improving information freshness when conducting DMs. A quasiperiodic DM mechanism is regarded, where one DM slot is probabilistically determined in each UP. The optimal DM probabilities are obtained in closed-form. In the D/Geo/1/1 case, we find that the preemption-induced EAoI gains are identical among slots; for extremely reliable/error-prone channels, conducting DM at the second/middle slot in UP is optimal. Numerical results verify the effectiveness of theoretical analyses and DM process optimization.

This work considers the problem of remote estimation of Wiener processes and proposes a sample preprocessing method to ensure the convergence of the estimation distortion. Specifically, the autocorrelation of the Wiener process is exploited to counteract the effect of strong quantization noise arising from the linearly increasing variance over time. We first derive the exact expression for the convergent mean squared error (MSE) without considering transmission outages to explain the proposed preprocessing method. Then, the analysis is extended to more complex and general scenarios with outages. Based on the derived MSE, the quantization precision, the sampling interval, and the transmission time of a single piece of update information are optimized individually. We further give two algorithms to obtain two global suboptimal MSEs for practical cases considering low thresholds of quantization precision and sampling interval, following a demonstration of the unsolvability of the joint optimization. The numerical results reveal that dynamic distortion plays a greater role than static distortion due to the fast-varying nature of the Wiener process, which also verifies the effectiveness of the proposed preprocessing method in controlling the quantization error.

The reconfigurable intelligent surface (RIS)-aided cognitive radio (CR) system holds significant promise for enhancing spectrum utilization. However, its practical implementation hinges critically on accurate channel state information (CSI). Obtaining full cascaded CSI in RIS-aided CR systems with the cross interference between multiple cascaded channels is challenging. To fill this gap, we propose the deep learning-based channel acquisition schemes for the users in static scenario and mobile scenario, respectively. In static scenario, we propose a novel deep neural network (DNN)-based channel estimation scheme named dual output parameter estimation (DOPE). This scheme achieves remarkable normalized mean square error (NMSE) performance in CSI estimation while significantly reducing the required pilot overhead. In mobile scenario, we propose a channel prediction scheme with hybrid recurrent neural network (RNN) and Transformer (HRT-CP). This scheme utilizes RNN to extract dynamic and static features of cascaded channels, and introduces Transformer’s powerful parallel processing capability to efficiently predict dynamic features. By combining static and dynamic features appropriately, the HRT-CP scheme has predicted the future cascaded CSI accurately, and mitigated the error accumulation phenomenon effectively. The simulation results under both near-field and far-field channel models demonstrate that our proposed schemes provide significant gains on NMSE performance compared to other benchmarks.

While spatial, temporal, and frequency domains are explored to enhance spectral efficiency (SE) in wireless communications, utilizing the polarization of electromagnetic (EM) waves also contributes to achieving this goal. Unlike conventional reconfigurable intelligent surface (RIS)-aided communications, polarization-transforming RIS (PTRIS) is first applied in this work to assist the line-of-sight (LoS) communication system. We introduce a novel framework for accurately modeling the direct and cascaded channels in the PTRIS-aided LoS communication system. This framework considers the spatial positions of the transmitter and receiver, the radiation patterns, antenna rotations, and the physical propagation mechanisms of EM waves based on antenna theory. The aperture field method is used to model the physical reflection of EM waves by PTRIS. Additionally, we aim to maximize SE by investigating four cases regarding the polarization-transforming capability of the PTRIS. Optimal closed-form solutions are derived for Case 1 and Case 2, while a best-effort approximation-based alternative optimization (BEA-AO) method is proposed for Case 3 to obtain sub-optimal solutions. Case 4 can be solved optimally with the barnch-and-cut algorithm within a reasonable computation time. Numerical results demonstrate that PTRIS can provide a robust and enhanced SE in LoS communications, even with arbitrary antenna rotations, compared to the scenarios without PTRIS.

In reconfigurable intelligent surfaces (RIS)-aided cognitive radio (CR) systems, the estimation of high-dimensional channel state information (CSI) with the interferences for all the cascaded channels between the RIS and any pair of the transceivers is challenging. To solve this problem, we propose a three-stage scheme of the full cascaded channel estimation based on the deep neural network (DNN). At the beginning, the protocol of the two-step pilot transmissions is designed for generating the labeled channel dataset in data preparation stage. Specifically, we propose dual-output parameter estimation (DOPE) architecture to establish and train the DNN model for full cascaded channel estimation simultaneously with a low pilot overhead in model construction and training stage. Notably, our proposed DNNbased DOPE architecture can accomplish the estimation of both the near-field and far-field cascaded channels in the RIS-aided CR system. Besides, the protocol of only one pilot transmission is introduced for online channel estimation in the model deployment stage. The simulation results show that our proposed scheme outperforms the compared benchmark estimation algorithms at the aspects of both the normalized mean square error (NMSE) performance and the cost of the pilot overhead.

In this paper, we consider the problem of remote estimation of Wiener processes and propose a sample preprocessing method to ensure the convergence of estimation distortion. The autocorrelation of the Wiener process is exploited to counteract the effect of strong quantization noise arising from the linearly increasing variance over time. We first derive the convergent expression for the mean squared error without considering transmission outages to explain the proposed preprocessing method. Then, the analysis is extended to more complex and general scenarios with outages. Based on the analyses, the quantization precision and the sampling interval are optimized individually.

This work investigates the time-dependent statistical characteristics of age of information (AoI) in the real-time Internet of Things systems with wireless periodic status updating. The periodic updating system is modeled as a discrete-time D/G/1/1 non-preemptive queue. The entire AoI process w.r.t. slot is split into a set of slot-specific AoI processes w.r.t. updating period. A Markov high-dimensional age process is introduced to track the evolution of the slot-specific AoI. Accordingly, the time-dependent AoI distribution, expected AoI (EAoI), and the time-average AoI are derived, forming a framework to study the time-dependent AoI statistical characteristics under periodic updating. Numerical results verify the effectiveness of theoretical analyses and usefulness of packet retransmission. It is found that the EAoI varies non-monotonously and greatly w.r.t. slot in an updating period; the EAoI variations are distinct for the update transmission times with different distributions; and the EAoI gains of channel enhancement differ greatly among slots.

The Age of Information (AoI), which measures the time since the generation of the latest update, quantifies information freshness in timeliness-critical systems. Minimizing AoI and characterizing it precisely are crucial for system efficiency and decision-making. This work investigates AoI under external interference modeled as an On-Off process, providing a foundation for future research in more complex scenarios. We consider a discrete-time remote status-updating system with a monitor and a sensor, where the sensor observes a physical process, generates timestamped updates, and sends them to the monitor. Both inter-arrival and service times follow geometric distributions, with service interrupted according to a two-state On-Off process. We analyze AoI under two queuing disciplines: 1) non-preemptive, where arriving updates are discarded if the server is occupied, and 2) preemptive, where in-service updates are replaced with new ones during the Off state. For both, we derive closed-form expressions for average AoI and peak AoI (PAoI). We also explore the relationship between discrete-time and continuous-time systems, showing that the latter is the limiting case of the former. Numerical results validate the theoretical analysis, revealing a linear relationship between the relative normalized increase in average PAoI and AoI and the proportion of Off state time. Frequent On-Off switching and higher service rates under the same system load help mitigate freshness deterioration caused by interruptions. The On-Off process is shown to have a large impact on the average AoI (resp. PAoI) of systems with relatively high (resp. low) arrival and service rates.

This work studies the average Age of Information (AoI) of a remote monitoring setup in which a multi-sensor system observes independent sources and updates the status to a common monitor using orthogonal channels. Considering the limited buffer size at the sensors, we first model each sensor as a first-come-first-served M/M/1/1 queue. Leveraging tools from stochastic hybrid systems, we derive the average AoI of a homogeneous single-source multi-sensor system in which all sensors’ arrival and service rates are the same. We then extend the results to the multi-source, multi-sensor system. For a multi-source dual-sensor system, we present an approximate optimal arrival rate for a given sum arrival rate at a light load. For heterogeneous cases with different arrival and service rates at sensors, the average AoI is derived for the single-source dual-sensor and more general multi-source systems. Our analysis shows that the average AoI decreases by 16.44% and 21.44% for the dual-sensor and three-sensor systems, respectively, compared to the single-sensor system when the service rate and the total arrival rate of the sensors are normalized. Numerical results confirm that the average AoI performance of the single-source dual-sensor system outperforms the M/M/2 system at high system load.

Using multiple sensors to update the status process of interest is promising in improving the information freshness. The unordered arrival of status updates at the monitor end poses a significant challenge in analyzing the timeliness performance of parallel updating systems. This work investigates the age of information (AoI) of a discrete-time dual-sensor status updating system. Specifically, the status update is generated following the zero-waiting policy. The two sensors are modeled as a geometrically distributed service time queue and a deterministic service time queue in parallel. We derive the analytical expressions for the average AoI and peak AoI using the graphical analysis method. Moreover, the connection of average AoI between discrete-time and continuous-time systems is also explored. It reveals that in dual-queue systems, the AoI results of continuous-time systems with exponential time distribution can be extended from the limit cases of discrete-time systems with geometric distribution. Numerical results validate the effectiveness of our analysis and further show that randomness of service time contributes more AoI reduction than determinacy of service time in dual-queue systems in most cases, which is different from what is known about the single-queue system.

This work studies information freshness of a V2I status updating link in IoV. The status updating link is modeled as a multi-source Ber/Geo/1/1 non-preemptive or preemptive queue. We focus on statistical characteristics of the age of information (AoI) and peak AoI (PAoI). To fully track the AoI evolutions under non-preemptive and preemptive policies, Markov three-dimensional age process (3DAP) and two-dimensional age process (2DAP) are respectively introduced. Their first element is the AoI process; The second one stands for if an update of the concerned source is in transmission and its current age; The third element of 3DAP denotes if an update of another source is in transmission. An analytical approach for studying the AoIs and PAoIs in discrete-time multi-source systems is presented. By studying the state transitions, balance equations, and stationary distributions of 3DAP and 2DAP, analytical expressions of the distributions and averages of AoIs and PAoIs under both queueing policies are derived. Moreover, the optimal probabilistic update selection mechanism (PUSM) that maximizes overall freshness is derived in closed-form for the two-source case. Numerical results validate effectiveness of the theoretical analyses and reveal usefulness of the retransmission. It is found that in terms of improving the overall freshness, the PUSM should be designed to make effective update generation probabilities of sources as close as possible.

Combining half-duplex (HD) and full-duplex (FD) is promising in improving the information transmission rate of relay channels. This work proposes a novel two-phase hybrid duplex scheme for Gaussian relay channel where the relay operates in FD mode for a fraction of time and only transmits information for the rest of time. The achievable rate of the proposed hybrid duplex scheme is characterized in detail. Based on the obtained result, a joint time division and power allocation problem is formulated to maximize the achievable rate. In particular, the formulated problem is solved through a two-step optimization method. Firstly, the optimal relay power allocation is obtained for given time division factors. Then, the achievable rate maximization problem is addressed by finding the optimal time division factors. The closed-form expression for the maximal achievable rate is derived for some specific cases. Numerical results show that the proposed two-phase hybrid duplex scheme significantly improves the achievable rate of Gaussian relay channel compared with existing benchmark schemes.

Emerging applications in industrial Internet of things (IIoT) pursue ultra-reliability, low-latency, and high data rate. While majority of the industries are in the remote areas, capability of distant ultra-reliable and low-latency communication (uRLLC) has become one of the key performance indices of IIoT which we need to make a breakthrough. While relaying provides intuitive solution for improving communication distance, the introduction of relay in distant uRLLC intensifies the conflict between reliability and low-latency which further deteriorates the throughput of the relaying-based IIoT. In this work, we adopt full-duplex relaying to enhance the performance of distant uRLLC in IIoT. Specifically, we improve the reliability at MAC layer and throughput at physical layer in full-duplex relaying-based IIoTs, through jointly optimizing the coding rate and the power allocation between the source and relay node. In particular, a low-complexity algorithm is developed to find the exact optimal coding rate and relay power under total system power constraint. Extensive numerical results validate the conclusion that the reliability and throughput of distant uRLLC in IIoT are enhanced through full-duplex relaying, power allocation, and rate adaptation.

In practical status updating systems, most decisions made at monitors are based on diverse data streams. Due to the cask effect, it can be cognised that the effectiveness of decisions is often constrained by the stalest one, i.e., the straggler among all the streams. This work studies the statistical characteristics of age of the stalest information (AoSI) which describes the timeliness of the stalest stream. The AoSI is defined as the time elapsed since the latest successfully received update of the currently stalest stream among all different streams at the monitor was generated. Peak age of the stalest information (PAoSI) is also studied for evaluating the worst cases, i.e., the peaks of AoSI process. We develop an analytical approach to derive the AoSI and PAoSI based on the per-stream age of information (AoI) and peak age of information (PAoI), for the multi-stream single-monitor system with separate status updating. In particular, to comprehensively characterize the timeliness of the stalest stream, the distributions of AoSI and PAoSI are derived in closed-form for the general multi-stream system. Moreover, we concisely derive the explicit expressions of the distributions and averages of AoSI and PAoSI, upon a typical two-stream case with the classical automatic repeat-request protocol. Finally, the accuracy of the theoretical analyses is validated by the numerical results. Appropriateness and advantages of the AoSI (PAoSI) are elaborated by comparing with the maximum average AoI (PAoI), i.e., the maximal one among the averages of all the per-stream AoIs (PAoIs).

Timely status updating is the premise of emerging interaction-based applications in the Internet of Things (IoT). Using redundant devices to update the status of interest is a promising method to improve the timeliness of information. However, parallel status updating leads to out-of-order arrivals at the monitor, significantly challenging timeliness analysis. This work studies the Age of Information (AoI) of a multi-queue status update system where multiple devices monitor the same physical process. Specifically, two systems are considered: theBasic System, which only has type-1 devices that are ad hoc devices located close to the source, and theHybrid System, which contains additional type-2 devices that are infrastructure-based devices located in fixed points compared to theBasic System. Using the Stochastic Hybrid Systems (SHS) framework, a mathematical model that combines discrete and continuous dynamics, we derive the expressions of the average AoI of the considered two systems in closed form. Numerical results verify the accuracy of the analysis. It is shown that when the number and parameters of the type-1 devices/type-2 devices are fixed, the logarithm of average AoI will linearly decrease with the logarithm of the total arrival rate of type-2 devices or that of the number of type-1 devices under specific condition. It has also been demonstrated that the proposed systems can significantly outperform the FCFS M/M/Nstatus update system.

This work studies the average Age of Information (AoI) of a remote monitoring system in which two sensors observe the same physical process and update the status to a common monitor using orthogonal channels. While using redundant devices to update the status of a process can improve the information timeliness at the monitor, the out-of-order arrivals of updates impose a challenge to the AoI analysis. We first model the system as two parallel M/M/1/1 queues. By leveraging tools from stochastic hybrid systems, we obtain analytically the average AoI of the system. In particular, when the arrival or service rates are the same for the two sensors, the average AoI is given in closed form. Our analysis reveals that the average AoI of the considered system is reduced by 16.44% compared to the single-sensor system when the arrival and service rates are equal to 1. Numerical results show that the considered system outperforms the M/M/2 system in average AoI at high arrival rates.

Considering the spectrum sharing system (SSS) coexisting with multiple primary networks, we have employed a well-designed reconfigurable intelligent surface (RIS) to control the radio environments of wireless channels and relieve the scarcity of the spectrum resource. Specifically, the enhancement of the spectral efficiency of the secondary user in the considered SSS is decomposed into two subproblems which are a second-order cone programming (SOCP) and a fractional programming of the convex quadratic form (CQFP), respectively, to optimize alternatively the beamforming vector at the secondary access point (S-AP) and the reflecting coefficients at the RIS. The SOCP subproblem is shown as a concave problem, which can be solved optimally using standard convex optimization tools. The CQFP subproblem can be solved by a low-complexity method of gradient-based linearization with domain (GLD), providing a sub-optimal solution for fast deployment. Taking the discrete phase control at the RIS into account, a nearest point searching with penalty (NPSP) method is also developed, realizing the discretization of the phase shifts of the RIS in practice. The simulation results indicate that both GLD and NPSP can achieve an excellent performance.

Abundant real-time applications over Internet of Things (IoT) have imperative demands on timely information. Compared to average Age of Information (AoI), distribution of AoI characterizes the timeliness in more details. This article studies the timeliness of an IoT-based multisource status update system. By modeling the system as a multisource M/G/1/1 bufferless preemptive queue, general formulas of violation probabilities and probability density functions (p.d.f.s) of AoI and PAoI are derived based on a time-domain approach. For the case with exponentially distributed service time, the violation probabilities and p.d.f.s are obtained in closed form. To fully characterize the overall timeliness of the multisource system, the maximal violation probabilities of AoI and PAoI are proposed. To improve the overall timeliness under the resource constraint of IoT device, the arrival rate allocation is optimized to control the maximal violation probabilities. It is proved that the optimal arrival rates can be found by convex optimization. In particular, we show that the minimum of maximal violation probability of AoI (PAoI) is achieved only if all violation probabilities of AoI (PAoI) are equal. Finally, numerical results verify the theoretical analysis and show the effectiveness of the arrival rate allocation in improving the overall timeliness.

Reconfigurable intelligent surfaces (RISs) have the capability to change the wireless environment smartly Considering the attenuation of subchannels and crowding users involved in the wideband system, we introduce RISs into the multi-user multi-input single-output (MU-MISO) system with orthogonal frequency division multiplexing (OFDM) for performance enhancement. Maximizing the minimum rate of dense users in an MU-MISO-OFDM system assisted by RIS with an approximate practical model is formulated as the joint optimization problem involving subcarrier allocation, transmit precoding (TPC) matrices at the base station, and RIS passive beamforming. A coalition-game subcarrier allocation (CSA) algorithm is proposed to solve space–frequency resource allocation on subcarriers, which reforms the interference topology among dense users. Fractional programming and convex optimization method are used to optimize the TPC matrices and the RIS passive beamforming, which improves the spectral efficiency synthetically across all subchannels in the wideband system. Simulation results indicate that the CSA algorithm provides a significant gain for dense users. Besides, the proposed joint optimization method shows the considerable advantage of the RISs in the MU-MISO-OFDM system.

Emerging Internet of Things applications pursue both data timeliness and fidelity at the fusion center (FC), raising challenges for network design. This work investigates the optimal node number achieving the best timeliness-fidelity tradeoff. Specifically, we consider a wireless network where homogeneous sensors observe one source simultaneously and deliver their observations to the FC over orthogonal block fading channels. Two scenarios are considered: The FC waits for observations from all nodes and the FC waits for observations from partial nodes. We evaluate the data timeliness and fidelity using the age of information (AoI) and the mean squared error (MSE), respectively. We first present a tight approximation of the average AoI in closed-form and derive a tight lower bound on the MSE of the sensing system. Then, sub-optimal numbers of sensor nodes minimizing a weighted-sum of the average AoI and the MSE are obtained in closed-forms for both scenarios based on high signal-to-noise ratio regime analysis. Iteration algorithms are further provided to approach the optimums. It is shown that the optimal partial number of nodes is proportional to the square root of total number of nodes asymptotically. Numerical results validate the accuracy and effectiveness of the solutions in improving the timeliness-fidelity tradeoff.

We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process through two independent channels. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M system where the service time of two servers is exponentially distributed; the M-D system in which the service time of one server is exponentially distributed and that of the other is deterministic. For the two dual-queue systems, closed-form expressions of average AoI and PAoI are derived by resorting to the graphic method and state flow graph analysis method. Our analysis reveals that when the two servers have the same service rate, compared with the single-queue system with an exponentially distributed service time, the average PAoI and the average AoI of the M-M system decrease by 33.3% and 37.5%, respectively, and those of the M-D system decrease by 27.7% and 39.7%, respectively. Numerical results show that the two dual-queue systems also outperform the M/M/2 single queue dual-server system with optimized arrival rate in terms of average AoI and PAoI.

We study the average age of information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. We capture the state transition characteristics of the considered system by establishing a Markov chain. Using the state flow graph analysis method, we derive closed-form expressions of the average peak age of information (PAoI) and the average age of information (AoI) for the dual-queue update system. The numerical results show that compared with the single-queue update system, the average PAoI of the dual-queue update system is reduced by 33.5% and the average AoI dropped by 37.5%.

Pursuing both data freshness and preciseness in emerging Internet of Things applications brings big challenge for network design. This work investigates the optimal node number achieving the best fidelity-timeliness tradeoff. Specifically, we consider a wireless sensor network where multiple sensors observe one source simultaneously and deliver the observations to Fusion Center (FC) over orthogonal channels. We assume that the FC waits for observations from only partial nodes. We evaluate the fidelity and timeliness using mean squared error (MSE) and age of information (AoI) metric respectively. Firstly, explicit expressions of AoI and MSE are derived. Secondly, a closed-form approximate optimal number of sensor nodes is obtained to achieve the minimum weighted-sum of AoI and MSE. Iteration algorithm is further provided to approach the optimum. It is proved that the optimal number of partial nodes is proportional to the square root of number of total nodes. Numerical results verify that the proposed near-optimal node number is accurate and can significantly improve the fidelity-timeliness performance.

Age of information (AoI) is proposed to characterize the freshness of information. Since it describes the time elapsed since the information is generated, it can accurately characterize the freshness of the information. Most of the existing studies have focused on the average AoI of systems, but time-sensitive applications such as industrial control systems and sensing networks have strict requirements for information freshness. We consider the scenario of multi-terminal wireless uplink with random packet arrivals and study the system average peak AoI (PAoI) minimization and maximum PAoI minimization problem. First we derive a closed expression of the system average PAoI and sufficient and necessary conditions of the optimal policy, from which an optimal ratio based no butter policy (OR-NB) is developed. We further generalize it to a system design method to make the average PAoI of all terminals satisfy the corresponding hard constraints. In addition, a maximum expected peak AoI (EPAoI) increasing probability minimization policy is proposed to minimize the maximum PAoI, which is proved to be near-optimal.

To take advantages of the high spectral efficiency of full-duplex (FD) mode and control rate reduction caused by the self-interference introduced to the relay receiver, a novel hybrid duplex scheme is proposed where the relay works in FD mode following a duty cycle, and receives-only for the rest of time. After characterizing the achievable rate, a joint FD duty cycle and source power allocation problem is formulated to maximize the achievable rate. It is proved that the optimal source power allocation follows a water-filling algorithm over time. Moreover, the optimal FD duty cycle is obtained by considering low-, medium-, and high-source power cases. Specially, closed-form approximation of the optimal FD duty cycle for medium-source power case is presented. Besides, it is shown that the proposed hybrid duplex scheme degenerates to half-duplex and FD modes for low-and high-source power cases, respectively. Numerical results demonstrate that the proposed scheme can effectively improve the achievable rate for a wide range of parameters.

This work investigates a remote status updating system where the transmission process is modeled as a multi-stream M/G/1/1 non-preemptive system. We derive the closed-form expression of the average AoI of each stream in a heterogeneous case, where the distributions of service time are different for streams. To obtain more insights, we apply the results in a homogeneous system, where the service time distributions are identical, and find that preemption of packets would not always lead to the reduction of AoI, especially when the variance coefficient of the service time is small. We further optimize the generation rate to minimize the sum of average AoI. The results in heterogeneous cases show that given the same average service time for all streams, a higher generation rate should be allocated to the stream with a small service time variance. For the homogeneous cases with different AoI urgency weights for each stream, a higher generation rate should be reserved for the stream with more urgent AoI requirements for timeliness improvement. Besides, a lower bound on sum of average AoI is also provided, which only depends on the service rate and the number of data streams in homogeneous systems. Numerical results validate our theoretical analysis.

Full-duplex (FD) mode has great potential in improving the spectral efficiency. Mitigating the effect of self-interference becomes one key for performance enhancement of FD system. This work proposes a novel hybrid duplex scheme where the relay receives information for a fraction of time and simultaneously transmits and receives information for the rest, following a duty cycle. First, we formulate the achievable rate maximization of the proposed scheme as a joint FD duty cycle and source power allocation optimization problem. The optimal FD duty cycle, the optimal source power allocation, and the maximal achievable rate are explicitly given for some cases and characterized in detail for other cases. Then, the proposed scheme is applied to two-hop relaying systems. Specifically, the optimal source power allocation is proved to be a water-filling solution over the FD phase and the receives-only phase on the source-relay link. By dividing the system as low-, medium-, and high-source power cases, the optimal FD duty cycle and the maximal achievable rate are obtained in (approximate) closed-form case-by-case, where the source power thresholds among cases are clearly expressed. Numerical results validate that the proposed hybrid duplex scheme outperforms other benchmark schemes and can improve the achievable rate significantly.

For timeliness-sensitive applications in Internet of things (IoT) systems, it is critical to efficiently allocate transmission resources such that the freshness of information updates can be improved. This paper focuses on timely status updating in an orthogonal frequency division multiplexing-based IoT systems, in which all devices update status to one data center by sharing available bandwidth. To improve the timeliness of updates, a resource allocation optimization problem is formulated, based on finite blocklength (FBL) transmission and the age of information (AoI) metric. Two suboptimal policies, namely, fixed time slot policy and fixed blocklength policy, along with an iterative optimization algorithm, and an approximate optimal policy are presented for addressing the optimal resource allocation. By comparing the performance of different policies, it is shown that the iterative algorithm and the approximate optimal policy outperforms the other two suboptimal policies, and closely approaches the global optimal resource allocation.

The age of information (AoI) becomes a fashion and effective measurement for evaluating the timeliness and freshness of state updates in Internet of Things (IoT). The majority of existing works provide abundant insights for optimizing age through packet management. In this paper, the average AoI of a remote data transmission system in which the transmission process is modeled as a multiple stream M/M/1/1 non-preemptive queue process is considered. We first derive the exact theoretical expression of the average AoI of multiple stream M/M/1/1 non-preemptive queue and then extend this result to more general M/G/1/1 queues. Results suggest that the M/G/1/1 non-preemptive queue strategy can effectively improve the system performance. The comparison of preemption strategy and non-preemption strategy under different service processes shows that preemption of packets does not always lead to reduction of AoI, especially for the system with small coefficient of variance of service time. Moreover, it is found that given the same average service time, the M/G/1/1 queue with small coefficient of variance of service time performs better.

The Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. The reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the information freshness in IoT systems. By modeling the IoT networks as M/M/1 and M/D/1 queuing systems, this work focuses on characterizing the violation probability of peak AoI and AoI in IoT systems, where a sensor delivers updates to a monitor under M/M/1 and M/D/1 queues with first-come-first-served policy. From a time-domain perspective, we explore the correlation between interdeparture time and system time, by which the closed-form expressions of peak AoI distribution and the violation probability for any AoI constraint are derived. The obtained results induce accurate characterizations for probability distribution functions of peak AoI and AoI. Consequently, accurate characterizations of average AoI and the variance of AoI are obtained. Then, for peak AoI and AoI, the optimal generation rate of the status update that induces the minimal violation probability is also found. The numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints. The theoretical findings and predictions are verified by numerical simulation results as well as provide guidance for the design of IoT networks.

Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. Reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the data freshness in IoT systems. This work focuses on characterizing the violation probability of AoI in IoT systems where a sensor delivers updates to a monitor under M/M/1 queue with first-come-first-served (FCFS) policy. By exploring the correlation between inter-departure time and system time, the closed-form expression of the violation probability for any AoI constraint is derived. The obtained result induces an accurate characterization of the probability distribution function of AoI. The optimal generation rate of the status update that induces the minimal violation probability is also found. Numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints.