Channel sounding is crucial for achieving Extremely High Throughput (EHT) and Ultra-high reliability (UHR) in next-generation Wi-Fi systems, i.e., Wi-Fi 7 and beyond. In Downlink Multi-User Multiple-Input Multiple-Output (DL MUMIMO) communications, data rate significantly deteriorates under time-varying channel with Doppler effect. Therefore, effective channel sounding mechanisms must balance the Channel State Information (CSI) overhead and CSI staleness, which is governed by the channel coherence time. Despite its critical importance, channel sounding optimization under time-varying channel conditions remains under-explored. This paper addresses this research gap by proposing a cross-layer optimization problem for the channel sounding period with the objective of maximizing data rate by considering the CSI overhead over the MAC layer and the channel capacity degradation over the PHY layer. This problem is then converted into an equivalent formulation leveraging the EHT sounding protocol, which can be solved efficiently using our proposed optimal search algorithm. Through simulations, we evaluate the baseline EHT sounding using outdated beamforming matrices and benchmark it against our proposed solution. The numerical results demonstrate that the channel sounding period optimization significantly reduces CSI overhead by up to 11% while boosting the average data rate by up to 8%.

Status update systems require the timely collection of sensing information for which deploying multiple sensors/servers to obtain diversity gains is considered as a promising solution. In this work, we construct an absorbing Markov chain (AMC) to exactly model Age of Information (AoI) in a discrete-time dual-queue (DTDQ) status update system with generate-at-will (GAW) status updates, discrete phase-type (DPH-type) distributed service times and transmission freezing. Specifically, transmission is frozen for a certain number of slots following the initiation of a transmission, after which one of the two servers is allowed to simultaneously sample the monitored physical process and transmit a status update packet, according to the availabilities and priorities of the two servers. Based on the discrete-time AMC, we provide the exact distributions of both AoI and peak AoI (PAoI), enabling the derivation of arbitrary order moments. In addition, we analytically study the role of freezing using several typical service time distributions, including geometric, uniform, negative binomial, and triangular distributions. The introduction of freezing for DTDQ systems is demonstrated to be significantly beneficial in reducing the mean AoI for various service time distributions. Additionally, we study the impact of the statistical parameters of the service times and heterogeneity between the two servers on the freezing gain, i.e., reduction in mean AoI attained with optimum freezing policies.

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.

Next-generation Wi-Fi 8 (IEEE 802.11bn) targets ultra-high reliability (UHR) by introducing coordinated beamforming (CoBF). In dense networks with multiple access points (APs), simultaneous downlink (DL) multi-user MIMO (MU-MIMO) transmissions from multiple APs can cause severe intra-basic service set (intra-BSS) and inter-BSS interference. CoBF aided by only partial channel state information (CSI) feedback through medium access control (MAC) layer frame exchange is envisioned to support concurrent DL transmission with mitigated physical-(PHY-)layer interference. To improve the network throughput, not only the interference mitigation algorithm design requires careful design but also the selection of optimal AP CoBF clusters is crucial for dense AP deployments. This paper presents a cross-layer solution combining PHY and MAC layer design to optimize AP cluster formation for Wi-Fi 8 CoBF. At the PHY layer, we introduce two beamforming nulling strategies: full nulling, which completely cancels all intra-BSS and inter-BSS interference when sufficient spatial degrees of freedom are available, and partial nulling, which is used under limited degrees of freedom to reduce interference as much as possible. Based on this, we formulate the cross-layer problem that aims to optimize the network throughput, to which we propose an exact linear programming (LP) optimization to determine the optimal AP cluster formation. A greedy clustering algorithm is proposed as a low-complexity alternate. Simulation results demonstrate that the proposed CoBF approach significantly mitigates interference and achieves substantial throughput gains in dense AP scenarios. Furthermore, the LP-optimized AP clustering yields the higher network throughput than the greedy heuristic and mixed integer linear programming (MILP) by up to 12% and 26%, highlighting the benefits of global optimization in terms of performance and time complexity.

Extremely High Throughput (EHT) and Ultra-high reliability (UHR) are new objectives in Next-Gen Wi-Fi, i.e., Wi-Fi 7 and beyond; however, the data rate within a periodic channel sounding round is expected to significantly deteriorate under time-varying channels with Doppler effect in Downlink Multi-User Multiple-Input Multiple-Output. Therefore, Next-Gen channel sounding must carefully balance the MAC-layer CSI overhead reduction and the PHY-layer channel capacity degradation caused by the Doppler effect for data rate maximization. Despite its critical importance, the cross-layer (PHY + MAC) Wi-Fi channel sounding optimization in time-varying channels remains under-explored. This paper addresses this research gap by proposing a cross-layer optimization problem to find the optimal EHT sounding period that maximizes the average data rate by considering both MAC-layer CSI overhead and PHY-layer channel capacity degradation. This problem is then converted into an equivalent optimization problem that can be solved efficiently using our proposed model driven optimal search algorithm with proven convexity. Afterwards, we introduce a data driven Transformer-based partial CSI prediction framework to alleviate CSI staleness without introducing extra CSI overhead, which further enhances the average data rate. Through simulations, we evaluate the baseline EHT sounding protocol that always uses outdated partial CSI, and then benchmark the baseline against our proposed hybrid data and model driven approach. The numerical results demonstrate that integrating Transformer-based partial CSI prediction with the optimal channel sounding period significantly reduces CSI overhead by up to 25.2%, while increasing the average throughput by up to 30.9%.

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.

Blokchain is a promising technology to enable distributed and reliable data sharing at the network edge. The high security in blockchain is undoubtedly a critical factor for the network to handle important data item. On the other hand, according to the dilemma in blockchain, an overemphasis on distributed security will lead to poor transaction-processing capability, which limits the application of blockchain in data sharing scenarios with high-throughput and low-latency requirements. To enable demand-oriented distributed services, this paper investigates the relationship between capability and security in blockchain from the perspective of block propagation and forking problem. First, a Markov chain is introduced to analyze the gossiping-based block propagation among edge servers, which aims to derive block propagation delay and forking probability. Then, we study the impact of forking on blockchain capability and security metrics, in terms of transaction throughput, confirmation delay, fault tolerance, and the probability of malicious modification. The analytical results show that with the adjustment of block generation time or block size, transaction throughput improves at the sacrifice of fault tolerance, and vice versa. Meanwhile, the decline in security can be offset by adjusting confirmation threshold, at the cost of increasing confirmation delay. The analysis of capability-security trade-off can provide a theoretical guideline to manage blockchain networks based on the requirements of data sharing scenarios.

5G/B5G networks are expected to deliver huge traffic and support various use cases with diverse requirements. With the increasing demand for network capacity, a cost-effective and flexible RAN is urgently needed to provide customized services for users. On this basis, advanced flexible RAN architectures with functional splits are introduced. In this paper, we study the slice-centric fine-grained functional split and resource allocation problem in flexible RAN. We first formulate a multi-objective problem to jointly optimize the functional split selection, processing, and transmission resource allocation for slices, aiming at maximizing the functional split gain while satisfying slices’ requirements. Since a multi-objective problem may have multiple Pareto optimal solutions and is difficult to solve, we mathematically analyze and transform the problem into an equivalent parametric convex problem. Then, we propose an upper bound algorithm and a dual based resource allocation algorithm to find the solution for the optimization problem. Theoretical analysis and simulation results show that the proposed algorithms can effectively solve the functional split gain maximization problem and obtain a trade-off between processing and transmission resource gain. In addition, the proposed algorithms also outperform other benchmark approaches in terms of resource saving and flexibility.

Emerging interaction-based Internet of Things (IoT) applications have stringent demand for timeliness, imposing critical challenges to the design of status update system. Using redundant devices to update the status of the same process is a promising way to improve timeliness, but this approach can result in out of order update arrivals, making it difficult to analyze timeliness. To that end, the present paper conducts a theoretical study toward the Age of Information (AoI) of a multi-queue status update system where multiple sensors observe one physical process and update a common monitor. Based on the stochastic hybrid systems method, the average AoI of the considered system is derived in closed form. The theoretical results are consistent with the simulation results, verifying the correctness of the theoretical analysis. It is shown that the logarithm of the average AoI is linearly decreasing with the logarithm of the number of sensors.

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.

5G/B5G networks are expected to deliver a huge traffic and support various use cases with diverse requirements. With the increasing demand for network capacity, a cost-effective and flexible RAN is urgently needed to provide customized services for users. On this basis, the advanced flexible RAN architectures with functional splits are introduced. In this paper, we study the slice-centric fine-grained functional split and resource allocation problem in flexible RAN. We first formulate a multi-objective problem to jointly optimize the functional split selection, processing, and transmission resource allocation for slices, aiming at maximizing the functional split gain while satisfying slices’ requirements. Since a multi-objective problem may have multiple Pareto optimal solutions and is difficult to solve, we mathematically analyze and transform the problem into an equivalent parametric convex problem. Then, we propose an upper bound algorithm and a dual based resource allocation algorithm to find the solution for the optimization problem. Theoretical analysis and simulation results show that the proposed algorithms can effectively solve the functional split gain maximization problem and obtain a trade-off between processing and transmission resource gain. In addition, the proposed algorithms also outperform other benchmark approaches in terms of resource saving and flexibility.

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.

In a D2D-enabled MEC system, devices cooperate in task computation by relaying tasks to servers or providing computation capabilities for users. We investigate how nodes choose the roles to join in the offloading process in a dynamic environment, where mobile devices forming a tree-like multihop network can play relays and intermediate executors earning corresponding economic utility. By mathematically modeling the multihop computation offloading, we formulate the task-flow constrained network-wide utility maximization problem as a potential game. Based on the properties of the potential game, we prove the existence of Nash equilibrium and propose two learning-based algorithms, i.e., myopic best response (MBR-CO) and stochastic learning-based computation offloading (SL-CO), to find the equilibrium point in a distributed manner. Theoretical and simulation results show that MBR-CO is dominant in static scenarios, and SL-CO achieves a high utility and stable performance in dynamic scenarios.

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%.

Blokchain is a promising technology to enable distributed and reliable data sharing at the network edge. The high security in blockchain is undoubtedly a critical factor for the network to handle important data item. On the other hand, according to the trilemma in blockchain, an overemphasis on distributed security will lead to poor transaction-processing capability, which limits the application of blockchain in data sharing scenarios with high-throughput and low-latency requirements. To enable demand-oriented distributed services, this paper investigates the relationship between capability and security in blockchain from the perspective of block propagation and forking problem. First, a Markov chain is introduced to analyze the gossiping-based block propagation among edge servers, which aims to derive block propagation delay and forking probability. Then, we study the impact of forking on blockchain capability and security metrics, in terms of transaction throughput, confirmation delay, fault tolerance, and the probability of malicious modification. The analytical results show that with the adjustment of block generation rate, transaction throughput improves at the sacrifice of fault tolerance, and vice versa. Meanwhile, the decline in security can be offset by adjusting confirmation threshold, at the cost of increasing confirmation delay.

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.

The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm.

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.

Vehicular edge computing (VEC) has been envisioned as an important application of edge computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VEC. They cooperate with edge severs to process offloading tasks at the vehicular network edge, leading to a new paradigm called parked-vehicle assisted edge computing (PVEC) in the Internet of Vehicles (IoV). However, recent researchers mostly focus on how to optimize the total cost of requesting vehicle (RV), and rarely pay attention to the optimization of the utility of PVs that provide services, including the reward from RV and the overhead of executing task. In this paper, we study a task assignment problem with computing delay constraints for PVEC in IoV. Specially, extra performance loss caused by offloading subtasks to PVs is taken into the cost function of RV. The optimal task assignment problem is formulated and solved with the Stackelberg game framework and a ternary search-based algorithm to minimize the cost of RV and maximize the utility of PVs. Finally, extensive numerical results are provided to demonstrate that our scheme is more efficient in deducing the total cost of RV and increasing the reward for PVs than other two existing schemes.

We consider an Internet-of-Things (IoT) system in which a sensor observes a phenomenon of interest with exponentially distributed intervals and delivers updates to a monitor with random service times. At the monitor, the received updates are used to make decisions with deterministic or random intervals. For this system, we investigate the freshness of the received updates at decision epochs using the age upon decisions (AuDs) metric. With the first-come-first-served (FCFS) policy, theoretical results show that: 1) when the decisions are made with exponentially distributed intervals, the average AuD of the system is smaller when the service time (e.g., transmission time) is uniformly distributed than when it is exponentially distributed and would be the smallest if it is deterministic; 2) when the decisions are made periodically, the average AuD of the system is larger than, and decreases with decision rate to, the average AuD of the corresponding system with Poisson decision intervals; and 3) the probability of missing to use a received update for any decisions is decreasing with the decision rate and is the smallest if the service time is deterministic. When the last-come-first-served (LCFS) policy with preemption is used, we observe that systems with Poisson service processes perform the best while systems with periodic service processes perform the worst. For IoT-based monitoring systems, therefore, it is suggested to use deterministic monitoring schemes, deterministic transmitting schemes, and Poisson decision schemes so that the received updates are as fresh as possible at the time they are used to make decisions.

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.

Simultaneous wireless information and power transfer (SWIPT) is a promising technique for energy-constrained wireless networks. To improve the security of SWIPT-enabled relay networks, in this paper, a full-duplex (FD) destination-aided jamming strategy is proposed, where a power splitting (PS) scheme is considered and two transmission phases are designed. In the first phase, the energy-limited half-duplex relay receives the signal superposed by confidential information and artificial noise (AN) from the source and destination, respectively. And then the FD destination transmits AN to the passive eavesdropper and receives the signal from the relay concurrently in the second phase. To maximize the secrecy capacity (SC), the individual optimizations of the PS ratio and power allocation (PA) coefficient are investigated, and the approximate optimal closed-form solutions of PS and PA ratios are derived. Besides, an iterative algorithm is also proposed to obtain the global optimal SC by updating the PS and PA ratios alternately. Simulation results confirm that the theoretical PS and PA ratios coincide closely with the exact results, and demonstrate that the optimal SC obtained by the iterative algorithm matches well with that given by the exhaustive search.

As an important metric of transmission performance, the maximum overall throughput of two-hop half-duplex relaying (HDR) in Ultra-Reliable and Low-Latency Communications (URLLC) is still not fully understood. In particular, an expression which can be evaluated straightforwardly is not available, and the explicit blocklength (BL) and coding rate configurations of source and relay which achieve the maximum throughput are not known either. In this paper, first we derive a closed-form expression of optimal BL configuration, then a closed-form expression of suboptimal coding rates configuration is obtained, finally we derive the closed-form expression of maximum overall throughput. Numerical results validate our theoretical analysis, and show that the maximum throughput of two-hop HDR in URLLC is far superior to conventional relaying and close to the corresponding Shannon capacity.

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.

In this article, we consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as age upon decisions (AuD), which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, and controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision-making process. The theoretical results show that: 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and will be minimized if the arrival process is periodic (i.e., deterministic); 2) when both the decision process and the arrival process are periodic, the average AuD is larger, but decreases with decision rate to, the average AuD of the corresponding system with the Poisson decisions (i.e., random); and 3) when both the decision process and the arrival process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making the periodical updates and decisions with properly controlled offset is also a promising solution, if the timing information of the two processes can be accessed by the monitor.

In this paper, we propose a novel non-orthogonal multiple access (NOMA) technique from a spatial domain (SD) perspective for indoor multiple-input multiple-output visible light communication (MIMO- VLC) systems. By fully exploiting the spatial distributions of light-emitting diode (LED) transmitters in the ceiling and users over the receiving plane, SD-NOMA is achieved by assigning all the users to different LEDs in the MIMO-VLC system. Hence, each user only receives data from a specific LED and users assigned to the same LED can use the overall modulation bandwidth of the system. Moreover, a signal-to-noise ratio (SNR) based LED selection scheme is further proposed for each user to efficiently select its desired LED. The achievable rates of a general indoor MIMO-VLC system using conventional MIMO orthogonal frequency division multiple access (MIMO-OFDMA) and the proposed SD-NOMA are analytically derived. The superiority of SD-NOMA over conventional MIMO-OFDMA for multi-user MIMO-VLC systems is successfully verified by detailed analytical results.

Applications with more sensitive delay and larger data volumes, such as interactive gaming and augmented reality, have become popular recently. Computation offloading is expected as a promising technique to meet low latency for mobile users. However, computation offloading requires communication between mobile users and the mobile edge computing (MEC) server, the delay and energy consumption caused by the transmission are considerable expenses for users. Motivated by this, we consider joint computation offloading and task caching optimization in a cellular network where users can proactively cache and offload their tasks at the MEC server. The objective of this paper is to minimize the system cost, which is defined as the weighted sum of task execution delay and energy consumption for all users. By formulating the problem as mixed-integer non-linear programming, we propose to find the optimal solution by three steps. Through which we have obtained the optimal computing resource allocation, the optimal task caching scheme and an algorithm which yields the optimal computation offloading scheme. Simulation results show that in comparison to the other three benchmark methods, the proposed scheme can effectively reduce the system cost.

Caching popular contents at radio access networks is a promising approach to improve the content delivery efficiency. Most of the existing content delivery schemes focus on the perspective of content providers, paying less attention to the service demand of content requesters. In this paper, we investigate the content delivery policy of a mobile device with service delay constraint in a cache- enabled heterogeneous network (HetNet), where a macro base station (MBS) is overlaid with some small base stations (SBS) with caches. In the considered network, the mobile device needs to make content delivery decisions based on the time, cache state, and signal-to-interference-plus-noise ratio (SINR) state. The problem of solving an optimal content delivery policy is modeled as a Markov decision process (MDP), where the objective is to minimize the delivery cost of the mobile device under the constraint of content service deadline. In order to address this problem, we propose a reinforcement learning (RL) algorithm to learn the optimal policy. The simulation results demonstrate that our proposed RL-based policy achieves a significant improvement in content delivery cost compared with other benchmark solutions.

Caching popular contents at the mobile devices is a promising technique to alleviate the backhaul data rate demand. Since both file placement and data exchange among mobile devices consume energy, the energy status of devices has a significant effect on the caching utility of the whole system. This work considers the caching optimization in a cellular network where mobile devices are served by one base station (BS). As the devices can collect the file segments from the local storage, via device-to-device (D2D) links, and via a cellular link, we aim at minimizing the percentage of file segment that should be collected from the BS by optimizing the file placement scheme at devices to improve caching performance. Due to the difficulty of solving the optimal caching problem, we propose a residual energy-aware file placement algorithm based on the popularity distribution of contents and causality of energy arrival. Simulation results show that in comparison to other two conventional caching methods, the proposed algorithm can effectively reduce the percentage of file segments that collected from the BS.

In Internet of Things, wireless access networks are required to support a large number of user equipments (UEs) in real time. With UEs' frequently arrival and departure, plenty of packet collisions can occur. Successive decoding thus becomes a promising technique to support the massive connectivity as it is capable of recovering packets from mixed received signals. In this paper, we propose an adaptive framing with successive decoding (AFSD) frame structure to deal with the fluidity of UEs. In the AFSD structure, the frame length is adaptively adjusted based on the present UE number, and successive decoding is adopted to alleviate the packet loss caused by packet collision. To quantify its performance, we provide exact as well as asymptotic results for the average delay. The analytical results are validated by simulations, and further extended to practical systems with estimated UE number, which shows that knowledge of active UE number is useful for delay reduction. Optimizations on UE transmission probability and frame length are also presented. In particular, the numerical studies reveal that by using optimized parameters, the average delay is reduced significantly for a wide range of arrival rates, which validates the effectiveness of the AFSD structure.

With the emergence of mobile edge computing (MEC), mobile users are able to process various tasks by offloading large-computation-demanding tasks to MEC server located at the edge of the network. As computation offloading requires communication between mobile users and the MEC server, an efficient computation offloading scheme which decreases both task executive delay and transmission energy consumption of mobile users plays a key role in MEC. Motivated by this, we study the computation offloading scheme in a novel MEC system where mobile users can offload tasks to the MEC server or a distributed computing node (DCN). As mobile users' offloading scheme affects the delay and energy consumption each other, we show that the offloading decision-making problem of users can be formulated as a sequential game. In particular, we demonstrate that the Nash equilibrium of the game exists which manifests that the system can converge to a stable status. A multi-user and multi-destination computation offloading scheme is also proposed to achieve the Nash equilibrium. Simulation results show that the proposed computation offloading scheme can significantly decrease the task execution delay as well as the energy consumption of mobile users.

Capacity of energy harvesting communications with deterministic energy arrival and finite battery size is investigated. An abstraction of the physical layer is considered, where binary sequences are transmitted through a binary symmetric channel, and a cost function is associated with the transmission of each symbol. Upper and lower bounds on the channel capacity are derived for the general case by studying the normalized exponent of the cardinality of the set of feasible input sequences. Several upper bounds on the exponent are proposed by studying supersets of the feasible set. Lower bounds are derived by applying the binary entropy-power inequality and by using specific signaling schemes based on a save-and-transmit strategy. Numerical results are presented for several values of the energy arrival rate and battery size, validating the usefulness of the capacity bounds established for the energy harvesting channels.

Ultra-reliable and low-latency communications (URLLC) have stringent requirements on quality-of-service and network availability. Due to path loss and shadowing, it is very challenging to guarantee the stringent requirements of URLLC with satisfactory communication range. In this paper, we first provide a quantitative definition of network availability in the short blocklength regime: the probability that the reliability and latency requirements can be satisfied when the blocklength of channel codes is short. Then, we establish a framework to maximize the available range, defined as the maximal communication distance subject to the network availability requirement, by exploiting multi-connectivity. The basic idea is using both device-to-device (D2D) and cellular links to transmit each packet. The practical setup with correlated shadowing between D2D and cellular links is considered. Besides, since processing delay for decoding packets cannot be ignored in URLLC, its impacts on the available range are studied. By comparing the available ranges of different transmission modes, we obtained some useful insights on how to choose transmission modes. Simulation and numerical results validate our analysis and show that multi-connectivity can improve the available ranges of D2D and cellular links remarkably.

This paper considers the placement of unmanned aerial vehicle base stations (UAV-BSs) with criterion of minimum UAV-recall-frequency (UAV-RF), indicating the energy efficiency of mobile UAVs networks. Several different power consumptions, including signal transmit power, on-board circuit power and the power for UAVs mobility, and the ground user density are taken into account. Instead of conventional empirical stochastic models, this paper utilizes a pattern formation system to track the instable and non-ergodic time-varying nature of user density. We show that for a single time-slot, the optimal placement is achieved when the transmit power of UAV-BSs equals their on-board circuit power. Then, for multiple time-slot duration, we prove that the optimal placement updating problem is an integer nonlinear programming coupled with an inherent integer linear programming. Since the original problem is NP-hard and cannot be solved with conventional recursive methods, we propose a sequential-Markov-greedy-decision strategy to achieve near minimal UAV-RF in polynomial time. Furthermore, we prove that the increment of UAV-RF caused by inaccurate predicted user density is proportional to the generalization error of learned patterns. Here, in regions with large area, high-rise buildings, or low user density, large sample sets are required for effective pattern formation.

Wireless energy transfer (WET) is emerging as a promising paradigm that provides sustainability for pervasive battery-powered devices to complete various tasks. Due to high attenuation of WET, it is crucial to design new architecture that conserves energy while guaranteeing task completion. In this paper, we propose an energy bank-based crowdsourcing framework and an incentive mechanism for energy conservation in wireless-powered task-oriented networks. An employer device outsources the whole or a part of its task to several worker devices and pays them energy as reward. Through energy-service trading, the employer consumes less energy and workers make energy profits. The virtual energy bank keeps accounts for all devices, authenticates the trading, and settles payments through a lossless bookkeeping-like manner. We analyze the employer's expense-minimized and workers' profit-maximized decisions and prove that the optimal decisions compose a Stackelberg equilibrium. To quantify the potential in energy saving, we further apply the framework to a relay-based sensor network where a source employs relays to forward data with a minimum rate requirement. An algorithm is developed for the NP-hard expense minimization problem. The simulation results reveal that our proposed framework and mechanism improve the energy efficiency by providing a win-win situation for both sides.

This work focuses on an energy harvesting communication system where a static sensor harvests energy from the environment and transmits information to a moving agent. Assuming that the battery is infinite and the sensor always has information to transmit, we first maximize the channel service theoretically, and then implement the event-triggered scheduling to control the time for starting transmission. We prove that energy depletion time is only relevant to the initial energy in the battery. There is no relation between the harvesting power and the energy depletion time. We provide the transmission triggered condition by deriving the initial energy threshold. Moreover, we extend the transmission model in single time-slot to an M/G/s(0) queuing system in multiple time slots and discuss the blocking probability caused by the event-triggered strategy. Our numerical results show the channel service of the scheme along with constant PA under various different configurations.

We investigate the capacity of energy harvesting binary symmetric channels with deterministic energy arrival process and finite battery size. Using an abstraction of the physical layer, binary symbols are transmitted. A cost function is associated with each transmitted symbol. Upper and lower bounds on the channel capacity are derived as functions of the normalized exponent of the cardinality of the set of feasible input sequences. Upper and lower bounds on the normalized exponent are established by studying supersets defined by relaxed constraints and employing a harvest-and-transmit signaling scheme, respectively. Numerical results validate that bounds on the exponent imply effective bounds on the channel capacity.

Incorporating the effect of residual self-interference, the spectral efficiency (SE) of full-duplex relay channel is analyzed for different relay schemes. The optimal relay power and the corresponding optimal SE have been derived in closed form for decode-forward, compress-forward, and amplify-forward (AF) schemes. In particular, backward decoding is introduced to the AF scheme in the presence of self-interference and it is shown to outperform forward decoding for high relay-destination SNR. Based on the obtained SE, bounds on the relay energy efficiency (REE) are also established. It evaluates how much the cooperation gain can be obtained from unit relay power consumption. We show that time sharing can improve the REE of AF scheme.

In Internet of Things, wireless access networks need to support a large number of user equipments (UEs) in real time, where UEs frequently arrive and leave and plenty of packet collisions occur. In this paper, we propose a protocol termed adaptive framing with successive decoding (AFSD), that not only supports vast connectivity but also involves little signalling overhead. By using AFSD, the frame length can be adaptively adjusted based on the present UE number to deal with the fluidity of UEs, and successive decoding is adopted to recover signal from the mixed received signal to alleviate the packet collision. We explicitly analyze the average delay of our protocol. Numerical results verify the accuracy of our analysis, as well as the effectiveness of the proposed protocol on handling massive access.

Full-duplex relaying has the potential to improve the spectral efficiency (SE) of cooperative communication systems. Due to residual self-interference (RSI), increase of the relay power does not always contribute to SE improvement. To fully utilize full-duplex relaying in cooperative communications, the effect of RSI on the SE achieved by different relay schemes need to be investigated. In this paper, we study bounds on the SE of full-duplex relay channel with decode-forward (DF) relaying, compress-forward (CF) relaying, and amplify-forward (AF) relaying in the presence of RSI. For respective schemes, optimal relay power and the corresponding maximal SE are derived in closed-form. Bounds on the relay energy-efficiency (REE) are presented for different schemes, which are useful for system design under per-node energy efficiency constraint. Based on the SE performance, the conditions of employing full-duplex relay, criteria for selecting relay scheme among DF, CF, and AF schemes, and the conditions of adopting hybrid full-duplex or half-duplex mode are elaborated regarding to RSI strength. In summary, this paper investigates the relationship among SE, REE, and system design by taking into account the effect of RSI for a general class of cooperation schemes.

Highly mobile wireless communication attracts much more attention currently due to the rapid development of high speed railways (HSRs) all over the world. Although the single train scenario has been well studied by now, two trains encountering scenario over the general two-way railways is also an important problem deserving to investigate. To this end, this paper concentrates on the uplink information transmission of HSRs in the two trains encountering scenario, which is modeled as a time- varying partial multiple access channel. In order to evaluate the transmission performance, the achievable rate region is utilized as a metric to characterize the tradeoff between the rates that each train can obtain under limited channel source constraint. With the help of superposition modulation and sequential interference cancelling, an optimal adaptive power allocation scheme aided by real-time position information is proposed to achieve the maximal boundary of the achievable rate region, namely alleviating the effect of encountering on information transmission to the largest extent. According to the numerical results, great improvement can be obtained by new proposed adaptive power allocation along time.

In 5G heterogeneous networking, it is promising to integrate different wireless networks to provide higher data rate. This paper models the integrated system as a receiver frequency division relay channel (RFDRC) and studies how to improve the transmission rate by combining decode-forward (DF), compress-forward (CF), and amplify-forward (AF) schemes. First, we establish clear criterions on how to select a relay scheme among DF, CF, and AF schemes and prove that the CF outperforms AF for all possible configurations. Based on the scheme selection criterions, we propose a hybrid DF-CF scheme which takes advantage of both DF and CF schemes in RFDRC. A near-optimal resource allocation is presented for the DF-CF-based system, leading to a new achievable rate for RFDRC. For ease of implementation, we further put forward a hybrid DF-AF scheme and reconsider the joint bandwidth and power allocation. Two suboptimal resource allocation solutions are established. In particular, when source frequency band and relay frequency band have equivalent bandwidth, we show that the proposed hybrid DF-AF scheme can achieve the concave envelope of the maximum between DF rate and AF rate. Numerical results show that the proposed schemes bring significant gains for RFDRC.

Wireless energy transfer, namely, radio frequency (RF)-based energy harvesting, is a potential way to prolong the lifetime of energy-constrained devices, especially in wireless sensor networks. However, due to huge propagation attenuation, its energy efficiency is regarded as the biggest bottleneck to wide applications. It is critical to find appropriate transmission policies to improve the global energy efficiency in this kind of system. To this end, this paper focuses on the sensor networks scenario, where a mobile control center powers the sensors by RF signal and also collects information from them. Two related schemes, called harvest-and-use scheme and harvest-store-use scheme, are investigated. In the harvest-and-use scheme, as a benchmark, both constant and adaptive transmission modes from sensors are discussed. In the harvest-store-use scheme, we propose a new concept, the best opportunity for wireless energy transfer, and use it to derive an explicit closed-form expression of optimal transmission policy. It is shown by simulation that a considerable improvement in terms of energy efficiency can be obtained with the help of the transmission policies developed in this paper. Furthermore, the transmission policies are also discussed under the constraint of fixed information rate. The minimal required power, the performance loss from the new constraint, and the effect of fading are then presented.

Abstract We investigate the multicast throughput of a butterfly network, which may be a promising topology for network coding application in next‐generation wireless communication systems. The butterfly network consists of two sources, two destinations and a relay, where each destination requires decoding of data from two independent sources. It is assumed that all the nodes are operated in half‐duplex mode. Each end‐to‐end packet transmission should be completed in a two‐phase period. In order to reduce processing complexity and multiple interference, other nodes should keep silent when the relay transmits a signal. By using Avestimehr, Diggavi and Tse's deterministic model, we first introduce a deterministic butterfly network and demonstrate that its maximal multicast rate region can be achieved by employing a network coding policy. According to the results obtained in deterministic case, we then put forward a near‐optimal design on the transmitted signal and decoding scheme for Gaussian scenarios based on a nested lattice code. It is proved that the gap between the achievable rate region and an outer bound is less than 3bits/s/Hz, which is not related to the signal‐to‐noise ratio. That is, the proposed scheme can approach the maximal multicast throughput. Finally, numerical results demonstrate that the gap is robust to both channel gains and time division of the two phases. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Multiple‐Input, Multiple‐Output (MIMO)‐orthogonal frequency division multiplexing (OFDM) is a promising technique in 5G wireless communications. In high‐mobility scenarios, the transmission environments are time‐varying and/or the relative moving velocity between the transmitter and receiver is also time‐varying. In the literature, most of previous works mainly focused on fixed subcarrier group size and precoded the MIMO signals with unitary channel state information. In this way, the subcarrier grouping may naturally lead to big loss of channel capacity in high‐mobility scenarios because of the channel state information difference on the subcarriers in each group. To employ the MIMO‐OFDM technique, adaptive subcarrier grouping scheme may be an efficient way. In this paper, we first consider MIMO‐OFDM systems over double‐selective i.i.d. Rayleigh channels and investigate the quantitative relation between subcarrier group size and capacity loss theoretically. With developed theoretical results, we also propose an adaptive subcarrier grouping scheme to satisfy the preset capacity loss threshold by adjusting grouping size with the sensed environmental information and mobile velocity. Theoretical analysis and simulation results show that to achieve a better system capacity, a sparse scattering, lower signal‐to‐noise ratio, and lower velocity as well as properly large antenna number are matched with larger subcarrier group size. One important observation is that if the antenna number is too large and higher than a threshold, which will not bring any additional gain to the subcarrier grouping. That is, the system capacity loss will converge to a lower bound expeditiously with respect to antenna number, which is given in theory also. Copyright © 2016 John Wiley & Sons, Ltd.

The relay scheme and the corresponding rate performance are considered for a half-duplex relay channel in which the source can only transmit information with fixed power. As different relay strategies result in different rate performance, we first present a decision criterion for selecting between Decode-and-Forward (DF) and Compress-and-Forward (CF) strategies by thoroughly analyzing their achievable rate. Based on the analysis result, the maximum of the DF rate and CF rate can be achieved by strategy selection procedure. To further obtain a larger rate, we put forward a hybrid DF-CF scheme in which the strategy selection between DF and CF is combined with active relay power allocation efficiently. It is shown that the concave envelope of the maximum of DF rate and CF rate is achievable via our new developed hybrid scheme. For the convenience of implementation, we also present a suboptimal setting for the hybrid DF-CF scheme. Numerical results show that the suboptimal setting can achieve a rate approaching the maximal rate.

In 5G networking, it is promising to integrate cellular system and wireless local area networks (WLAN) to enhance the throughput. The access point of the WLAN can be authenticated as a relay receiver in cellular system to assist the communication between the base station and the user equipment. In this paper, we model the integrated system as a Receiver Frequency Division Gaussian Relay Channel (RFD-GRC) and study how to improve the achievable transmission rate by adopting Decode-and-Forward (DF) and Compress-and-Forward (CF) schemes in the system.Specifically, making use of the orthogonality between the source frequency band (SFB, the cellular system frequency band) and the relay frequency band (RFB, the WLAN frequency band), we independently divide the available SFB and RFB into two subbands and adopt DF and CF in the two subbands, respectively. Joint bandwidth and power allocation of this hybrid DF-CF scheme is optimized, resulting in a cooperation gain larger than that achieved by DF and CF schemes individually.A sub-optimal setting for the hybrid DF-CF scheme is also given, simplifying the implementation and approaching the optimal rate performance. Numerical analysis confirms the effectiveness of the new scheme.

Network coding is famous for its capability in significantly improving the throughput of network. The successful decoding of the network coded data relies on some side information of the original data. In that framework, independent data flows are usually decoded first and then network coded by relay nodes. If appropriate signal design is adopted, physical layer network coding is a natural way in wireless networks. In this work, a network coding tree algorithm which enhances the efficiency of the multiple access system (MAS) is presented. For MAS, researchers try to avoid the collisions but collisions happen frequently under heavy load. By introducing network coding into MAS, our proposed algorithm achieves a better trade-off between average delay and system throughput. When multiple users transmit signal in a time slot, the sum signals are saved and used to jointly decode the collided frames after some component frames of the network coded frame are received. Splitting tree structure is extended to our proposed algorithm for collision solving. The system throughput and average delay of frames are presented in a recursive way. Besides, extensive simulations show that network coding tree algorithm enhances the system performance in terms of system throughput and average frame delay compared with other algorithms.

In fading channels, power allocation over channel state may bring a rate increment compared to the fixed constant power mode. Such a rate increment is referred to power allocation gain. It is expected that the power allocation gain varies for different relay protocols. In this paper, Decode-and-Forward (DF) and Compress-and-Forward (CF) protocols are considered. We first establish a general framework for relay power allocation of DF and CF over channel state in half-duplex relay channels and present the optimal solution for relay power allocation with auxiliary parameters, respectively. Then, we reconsider the power allocation problem for one hybrid scheme which always selects the better one between DF and CF and obtain a near optimal solution for the hybrid scheme by introducing an auxiliary rate function as well as avoiding the non-concave rate optimization problem. Simulation results show that the developed power allocation solutions bring significant rate gains in various fading relay channels compared to constant power allocation mode.

3D High Efficiency Video Coding (3D-HEVC) is an extension of the HEVC standard for coding of multiview videos and depth maps. It inherits the same quadtree coding structure as HEVC for both components, which allows recursively splitting into four equal-sized coding units (CU). One of 11 different prediction modes is chosen to code a CU in inter-frames. Similar to the joint model of H.264/AVC, the mode decision process in HM (reference software of HEVC) is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion cost using a Lagrange multiplier. Furthermore, both motion estimation and disparity estimation need to be performed in the encoding process of 3D-HEVC. Those tools achieve high coding efficiency, but lead to a significant computational complexity. In this article, we propose a fast mode decision algorithm for 3D-HEVC. Since multiview videos and their associated depth maps represent the same scene, at the same time instant, their prediction modes are closely linked. Furthermore, the prediction information of a CU at the depth level X is strongly related to that of its parent CU at the depth level X-1 in the quadtree coding structure of HEVC since two corresponding CUs from two neighboring depth levels share similar video characteristics. The proposed algorithm jointly exploits the inter-view coding mode correlation, the inter-component (texture-depth) correlation and the inter-level correlation in the quadtree structure of 3D-HEVC. Experimental results show that our algorithm saves 66% encoder runtime on average with only a 0.2% BD-Rate increase on coded views and 1.3% BD-Rate increase on synthesized views.

A relay channel (RC), consisting of a source, a relay, and a destination, is a basic transmission unit of cooperative communication networks. The capacity of an RC is not known in general. In this paper, an SNR decomposition (SD) strategy is presented to implement time sharing, which provides a new tractable and achievable rate for a full-duplex Gaussian RC. More specifically, we first expand the SNR of a relay destination channel (SNR-RD) into two terms under the relay power constraint and divide the system into two subbands. Then, we assign the obtained SNR-RD for each subband and employ decode-forward (DF) or compress-forward (CF) according to the assigned SNR-RD. It is shown that the achievable rate of the SD strategy is competitive with that of superposing CF on DF. As the superposition structure requires a sophisticated codeword design, the SD strategy provides another practical combination structure of DF and CF strategies. Approximations for the SNR-RD and bandwidth allocation for subbands are also given. Based on the obtained results, two application scenarios, i.e., mobile relay and quasi-static fading RCs, are also considered. Finally, various numerical results are shown to support our developed theoretical results.

In full-duplex Gaussian relay channels, neither Decode-and-Forward (DF) nor Compress-and-Forward (CF) can achieve a larger rate than the other for all the channel gain combinations. Combining DF and CF strategies, we show that a new achievable rate, which is the concave envelop of the maximal rate achieved by DF and CF with respect to the source power, is achievable. To this end, we actively adjust the transmission power of the source for different time and switch the transmission strategy between DF and CF according to the source power. It is proved that when the signal to noise ratio (SNR) of the source-destination link falls into a certain range, the new achievable rate is strictly larger than that achieved by pure DF and pure CF. The optimal power allocation and corresponding time proportions are also obtained. Numerical results show that the new achievable rate is also competitive with the rate achieved by superposing CF on DF. As strategy switching avoids complex codeword constructions, it is more practical than superposition structures to be implemented in relay systems.

This work considers the achievable rate region of full-duplex Gaussian relay channel. Under Gaussian signaling, it was found that neither Decode-Forward (DF) nor Compress-Forward (CF) can achieve better performance than the other for all channel gains. Recently, it was verified that superposing CF on DF in one band has a better performance than both DF and CF for Gaussian signaling. In this work, we consider another combining structure of CF and DF by making use of Subband Division (SD). It will show that our new developed strategy will achieve a larger rate for Gaussian signaling by comparing with DF lower bound, CF lower bound and the result of superposing CF on DF. In addition, a closed form solution of the achievable rate is found, in which the subband division factors are also given. Numerical results confirm our developed theoretical results.

Two user Gaussian interference channel (GIC) consists of two source-destination pairs which transmit independent messages and interfere with each other. The best achievable rate region, referred to HK sum rate bound, requires the sources to split the information into public messages and private messages. As Gaussian signaling holds the potential of approaching the capacity, finding the HK sum rate achieved by Gaussian signaling is of great importance. However, The optimal power allocation over messages for Gaussian signaling are not known yet This work clearly describes the optimal power allocation and corresponding sum rate achieved by Gaussian signaling without time sharing (TS) in closed form. It lays a foundation for finding the TS strategy achieving the optimal sum rate. The obtained power allocation indicates that without TS, message splitting may not be always necessary. Besides, the conditions for using and not using message splitting are also characterized in detail.

We investigate the multicast throughput of asymmetrical butterfly network which consists of two sources, two destinations and a half-duplex relay, where each destination needs to decode the data from two independent sources. In order to reduce processing complexity and multiple interference, other nodes should keep silent when the relay transmits signals. We first present an explicit expression of the deterministic maximal multicast throughput by using Avestimehr, Diggavi and Tse's deterministic approach. With the insight of the obtained result, we put forward a near-optimal network coding strategy to approach the maximal multicast throughput of this network. Finally, we compare our achievable rate region with an outer bound and show the gap between them is less than a constant 2.45 bits in some cases.

This paper considers the multicast throughput of a special butterfly network which is made up of two sources, two destinations and a half-duplex relay. Each destination requires to decode the data from two independent sources. All the nodes are assumed to be half-duplex. When the relay transmits signal, other nodes of the system should keep in receiving state to reduce processing complexity and interference. First, we present an explicit expression of the maximal multicast throughput in deterministic butterfly network which is derived from Aves-timehr, Diggavi and Tse's deterministic model. Based on the result obtained in deterministic butterfly network, we propose a near-optimal transmitting and decoding policy to approach the maximal throughput by using network coding in Gaussian butterfly network. Finally, we evaluate the gap between our achievable rate region with an outer bound and show it consists of a constant item which is less than 2.45 bits and a variable item which only exists in some cases and increases logarithmically with the signal to noise ratio (SNR) over all the links.