Age of Information (AoI) is emerging as a novel metric for measuring information freshness in real-time monitoring systems. For computation-intensive status data, the information is not revealed until being processed. We consider a status update problem in a multi-source single-server system where the sources are scheduled to generate and transmit status data which are received and processed at the edge server. Generate-at-will sources with both random transmission time and process time are considered, introducing the joint optimization of source scheduling and status sampling on the basis of transmission-computation balancing. We show that a random scheduler is optimal for both non-preemptive and preemptive server settings, and the optimal sampler depends on the scheduling result and its structure remains consistent with the single-source system, i.e., threshold-based sampler for non-preemptive case and transmission-aware deterministic sampler for preemptive case. Then, the problem can be transformed to jointly optimizing the scheduling frequencies and the sampling thresholds/functions, which is non-convex. We proposed an alternation optimization algorithm to solve it. Numerical experiments show that the proposed algorithm can achieve the optimal in a wide range of settings.

An optimal preemption framework is proposed to minimize Age of Information (AoI) in single-link systems with stochastic packet arrivals and known packet lengths. The problem is formulated as a Markov Decision Process (MDP) solved through relative value iteration (RVI), demonstrating that packet-length-dependent threshold policies achieve minimal average AoI. For scenarios with unknown traffic statistics, a Deep Q-Network (DQN) algorithm learns these thresholds adaptively through real-time interactions without prior distribution knowledge. Numerical evaluations under exponential and bounded Pareto packet length distributions reveal 15.5 percent average AoI reduction compared to non-preemptive baselines using the RVI method, while the DQN method achieves 14.4 percent improvement with less than 1.1 percent performance gap from the optimal policy. Both methods exhibit threshold-driven decision structures that balance immediate AoI reduction against long-term scheduling efficiency. Comparative analysis reveals the framework’s robustness across stationary and dynamic environments, with the DQN maintaining near-optimal performance through dimension-reduced state representation. These results establish a unified solution for AoI minimization that transitions seamlessly between model-aware and model-agnostic configurations, addressing critical challenges in real-time Internet of Things (IoT) networks and status update systems requiring freshness guarantees.

In vehicular networks, maintaining information freshness is essential for road safety, which can be quantified by the Age of Synchronization (AoS). With the rapid development of Vehicle-to-Everything (V2X) communication technologies, the diversity of communication service modes, such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), makes mode selection and resource allocation increasingly complex. To address this issue, we propose an AoS-aware mode selection and resource allocation algorithm based on a multi-agent Double Deep Q-Network (DDQN) framework. Our approach jointly optimizes subchannel allocation, transmission power control, and communication mode selection to minimize average AoS, enhancing both the timeliness and reliability of information dissemination in dynamic vehicular environments. Simulation results demonstrate that our method outperforms baseline algorithms in reducing both average AoS and transmission time.

In recent years, there has been an increasing focus on real-time mobile applications, such as news updates and weather forecast. In these applications, data freshness is of significant importance, which can be measured by age-of-synchronization (AoS). At the same time, the reduction of carbon emission is increasingly required by the communication operators. Thus, how to reduce energy consumption while keeping the data fresh becomes a matter of concern. In this paper, we study the age-energy trade-off in a multi-source single-server system, where the server can turn to sleep mode to save energy. We adopt the stochastic hybrid system (SHS) method to analyze the average AoS and power consumption with three wake-up policies including N-policy, single-sleep policy and multi-sleep policy, and three packet preemption strategies, including Last-Come-First-Serve with preemption-in-Service (LCFS-S), LCFS with preemption-only-in-Waiting (LCFS-W), and LCFS with preemption-and-Queueing (LCFS-Q). The trade-off performance is analyzed via both closed-form expressions and numerical simulations. It is found that N-policy attains the best trade-off performance among all three sleep policies. Among packet management strategies, LCFS-S is suitable for scenarios with high requirements on energy saving and small arrival rate difference between sources. LCFS-Q is suitable for scenarios with high requirements on information freshness and large arrival rate difference between sources.

With the rapid proliferation of mobile devices, a growing number of intelligent applications are being deployed at the network edge, placing immense strain on the processing capabilities of edge computing. Therefore, resourceconstrained edge servers frequently experience overload due to highly dynamic workloads. To address this, one approach involves forwarding user requests to the cloud or other edge servers, albeit at the cost of increased transmission latency. Alternatively, dynamic scaling of edge clusters can be employed to enhance processing capacity, thereby mitigating latency but at the expense of additional service configuration and hosting expenses. By integrating their complementary benefits, we study the joint optimization problem of dynamic scaling and request routing within a multi-edge cluster collaborative framework, which fully exploits cluster resources to manage the temporal and spatial varying edge workloads. This collaborative framework aims to minimize overall request latency while satisfying an acceptable time-averaged budget cost. However, the complex coupling between scaling and routing decisions, along with the uncertainty of future system information (e.g., user request workloads) impedes the derivation of an optimal offline policy over the long term. Thus, considering the different decision granularities, we employ the two-timescale Lyapunov optimization technique to decouple the original problem into a series of independent online optimization problems with the current system state. In particular, we make cluster scaling decisions in each large timescale and request routing decisions in each small timescale. Given that the decoupled large-timescale subproblems involve NP-hard mixed-integer linear programming, we design an edge resource-aware greedy rounding algorithm to efficiently produce approximate optimal solutions. Finally, both rigorous theoretical analysis and extensive trace-driven evaluations demonstrate the superiority of our proposed algorithm over its counterparts.

With the prevalence of real-time Internet of Things (IOT) applications, mobile edge computing (MEC) has emerged as a paradigm to ensure low-latency computation. Hierarchical MEC system, which deploy computation resources in a hetero-geneous way, is more applicable than traditional MEC system in scenarios with various types of offloading tasks. On the other hand, the boom of AI applications has led to an increasing energy consumption of MEC servers. Energy harvesting technologies effectively reduce the grid energy consumption by leveraging renewable energy sources. To this end, we propose a task offloading scheme in the scenario with hierarchical task offloading and green energy harvesting. Specifically, We formulate the offloading problem as minimizing the weighted sum of execution latency and energy consumption. To obtain the optimal offloading decisions, we propose a computing offloading algorithm based on deep reinforcement learning (DRL). Simulation results demonstrate that our proposed scheme efficiently reduces the sum cost compared with other approaches.

In recent years, semantic communication is progressively emerging as an effective means of facilitating intelligent and context-aware communication. However, current researches seldom simultaneously consider the reliability and timeliness of semantic communication, where scheduling and resource allocation (SRA) plays a crucial role. In contrast, conventional age-based approaches cannot seamlessly extend to semantic communication due to their oversight of semantic importance. To bridge this gap, we introduce a novel metric: Age of Semantic Importance (AoSI), which adaptly captures both the freshness of information and its semantic importance. Utilizing AoSI, we formulate an average AoSI minimization problem by optimizing multi-source SRA. To address this problem, we proposed a AoSI-aware joint SRA algorithm based on Deep Q-Network (DQN). Simulation results validate the effectiveness of our proposed method, demonstrating its ability to facilitate timely and reliable semantic communication.

Age of Information (AoI), which measures the time elapsed since the generation of the last received packet at the destination, is a new metric for real-time Internet of Things (IoT) applications. In many applications, status information needs to be extracted through computation, which can be processed at an edge server enabled by mobile-edge computing (MEC). In this article, we consider a status update system with MEC in an offline scenario, where transmission and computation need to be jointly scheduled to minimize AoI. Usually, long queuing delay and large packet generation interval will increase age in the queuing system. Therefore, a reasonable scheduling policy is the no-wait policy, which achieves zero queuing delay and a low generation interval. However, the no-wait policy is not always optimal. We propose an interval-wait policy that allows nonzero queuing delay and study the average age minimization problem in this policy. Theoretical results show that the optimal interval-wait policy has a special structure: the queuing delay is either zero or a fixed value that is determined by the transmission and computation time duration of the packet itself and its adjacency. Based on this, we propose an efficient enumerating-based algorithm to compute the optimal interval-wait policy. Our experimental results show that: 1) the interval-wait policy achieves optimal performance in most cases and 2) our proposed efficient algorithm can find the optimal interval-wait policy.

The freshness of information in real-time monitoring systems has received increasing attention, with Age of Information (AoI) emerging as a novel metric for measuring information freshness. In many applications, update packets need to be computed before being delivered to a destination. Mobile edge computing (MEC) is a promising approach for efficiently accomplishing the computing process, where the transmission process and computation process are coupled, jointly affecting freshness. In this paper, we aim to minimize the average peak AoI (PAoI) in an MEC system. We consider the generate-at-will source model and study when to generate a new update in two edge server setups: 1) computing preemption, where the packet in the computing process will be preempted by the newly arrived one, and 2) non-preemption, where the newly arrived packet will wait in the queue until the current one completes computing. We prove that the fixed threshold policy is optimal in a non-preemptive system for arbitrary transmission time and computation time distributions. In a preemptive system, we show that the transmission-aware threshold policy is optimal when the computing time follows an exponential distribution. Our numerical simulation results not only validate the theoretical findings but also demonstrate that: 1) in our problem, preemptive systems are not always superior to non-preemptive systems, even with exponential distribution, and 2) as the ratio of the mean transmission time to the mean computation time increases, the optimal threshold increases in preemptive systems but decreases in non-preemptive systems.

In edge Internet of Things (IoT) scenarios, it is important to maintain the freshness of information. The information freshness can be measured by Age of Information (AoI) and Age of Synchronization (AoS). To collect sensor node (SN) information in a flexible and efficient manner, unmanned aerial vehicles (UAVs) can be deployed. However, planning UAV trajectory, especially when SN updates are unpredictable and the energy consumption of UAV is limited, can be highly challenging. To address this issue, we propose an AoI-assisted trajectory planning for AoS and Energy minimization (AAE-TP) algorithm that leverages a deep reinforcement learning (DRL) framework. Our AAE-TP algorithm incorporates AoS in trajectory planning, allowing the UAV to obtain the AoS of the SN during information updates to compensate for the limitations of AoI. Simulation results demonstrate that the proposed algorithm can significantly improve the freshness of SN data collected by UAV while minimizing energy consumption.

Edge computing can reduce response time and communication pressure by caching data in edge server which is closed to the user. The development of 5G applications has posed strict requirements on various performance metrics of communication, which brings more and more challenges to the design of edge networks. In this paper, we focus on a multi-hop cache update system with multiple caches in series, which models a real-world system consisting of cloud, macro base stations, small base stations, and users. The base stations cache data and respond to users’ request. We derived closed-form expressions of average information freshness and average transmission delay for fetching data from the given level of cache and jointly optimize them. Then, we propose an algorithm based on alternating maximization to solve the optimization problem. The experimental results show that the proposed method can always achieve the optimum under different parameter settings. In addition, we also analyze the variation of latency and information freshness under the influence of weight parameters, which illustrates the importance of joint optimization of latency and information freshness.

With the requirement of timeliness increasing, data processing policy should be carefully designed to tackle arrivals. This paper mainly studies the Age of Information (AoI) in data processing system, where packets are generated by a source and processed by a server with known packets' length upon arrival. We aim to minimize the average AoI by deciding either to preempt the current packet or not when a new packet arrives. For the given distributions of inter-arrival time and packets' length, the problem is formulated by Markov Decision Process (MDP) and solved via value iteration. Without prior knowledge of the distributions, we apply Reinforcement Learning (RL) algorithms to learn the policy online. Through simulation experiments, it is revealed that the obtained optimal strategy by MDP greatly reduces the average AoI compared with baseline policies. Further, the RL algorithms have a good performance in solving this problem. The average AoI of RL policies are just slightly higher than those of MDP.

With the explosive development of Internet of Things (IoT) devices, edge sensors are deployed densely to monitor the environment. The status data can be sampled by the edge sensors and be transmitted to the mobile end device, such as an unmanned aerial vehicle (UAV), for further execution or offloading. To make an alert decision on time, the UAV requires to frequently collect and rapidly process the status data. The data freshness can be measured by the age of information (AoI). The UAV needs to design a flying trajectory to collect data from a large area. In addition, to satisfy the real-time requirement of the computation-intensive tasks, the UAV may offload the tasks to nearby edge servers and migrate service due to its mobility. In this paper, there are several challenges in the AoI-optimal UAV-assisted edge scenario: the freshness challenge, the collection challenge, the offloading, and the migration challenge. To minimize the time between two adjacent sampling for all sensors, we propose the AoI-optimal UAV Collection Control solution (AUCC). Specifically, to minimize round collection time, we propose the Dynamic programming-based Task Collection algorithm (DTC); to minimize round execution time, we propose the Lyapunov optimization-based Accuracy queue Control algorithm (LAC). Simulation results demonstrate that our AUCC solution achieves superior performance from the perspective of AoI, overtime task number, and accuracy.

This paper focuses on the real-time status update in a multiaccess vehicular network, in which multiple vehicles transmit not only the payload data (e.g., monitoring data) but also the extra data (e.g., driving intention) to a road side unit for scheduling vehicles to improve traffic efficiency and safety. A free-ride code is implemented, where the extra data is encoded by a random but fixed generator matrix and is delivered by superposition on the low-density parity-check (LDPC) coded payload data, consuming neither extra bandwidth nor extra transmit power. Considering the time slotted ALOHA random access protocol, we derive the closed-form expression for average age of information (AoI), and evaluate the AoI for both payload data and extra data. Numerical simulations demonstrate that free-ride codes can not only transmit extra data without extra transmit power, but also reduce the average AoI of extra data without affecting the average AoI of payload data.

Age-of-Information (AoI), or simply age, which measures the data freshness, is essential for real-time Internet-of-Things (IoT) applications. On the other hand, energy saving is urgently required by many energy-constrained IoT devices. This paper studies the energy-age tradeoff for status update from a sensor to a monitor over an error-prone channel. The sensor can sleep, sense and transmit a new update, or retransmit by considering both sensing energy and transmit energy. An infinite-horizon average cost problem is formulated as a Markov decision process (MDP) with the objective of minimizing the weighted sum of average AoI and average energy consumption. By solving the associated discounted cost problem and analyzing the Markov chain under the optimal policy, we prove that there exists a threshold optimal stationary policy with only two thresholds, i.e., one threshold on the AoI at the transmitter (AoIT) and the other on the AoI at the receiver (AoIR). Moreover, the two thresholds can be efficiently found by a line search. Numerical results show the performance of the optimal policies and the tradeoff curves with different parameters. Comparisons with the conventional policies show that considering sensing energy is of significant impact on the policy design, and introducing sleep mode greatly expands the tradeoff range.

This paper focuses on the age and energy tradeoff of a generalized m-th best relay selection scheme in a dual-hop status update system, where the destination associates to the relay with the m-th smallest number of receptions. By considering the short blocklength packet and retransmission, the expressions of the average age of information (AoI) and the average energy cost (EC) are derived and analyzed. Then, the weighted sum of average AoI and average EC is introduced and is minimized to tradeoff the average AoI and average EC. Numerical results show that for the generalized m-th best relay selection scheme, the optimal block length can be found to achieve the age and energy tradeoff. In addition, the best relay is not always the most suitable one to update the status especially when the weight coefficient of EC is much larger than that of AoI.

Real-time and energy-efficient transmissions are two critical demands for status update systems, however it is difficult to meet these requirements simultaneously. This paper focuses on the tradeoff between the average age of information (AoI) and the average energy cost (EC) for two-hop decode-and-forward (DF) relaying networks with short packet transmissions. Both the partial relay selection (PRS) and max-min relay selection (MMRS) schemes are considered. The expressions for the average AoI and the average EC of two-hop relaying networks are derived, and the age-energy tradeoff is also achieved by minimizing the weighted sum of them. Numerical simulations show that both PRS and MMRS schemes have their own advantages to minimize the age and energy cost under different channel conditions, where MMRS is a fairness scheme that takes the channel conditions of two hops into account. In addition, an optimal packet length can always be found to tradeoff the average AoI and average EC in two-hop relaying networks.

The freshness of status information is of great importance for time-critical Internet-of-Things (IoT) applications. A metric measuring status freshness is the Age of Information (AoI), which captures the time elapsed from the status being generated at the source node (e.g., a sensor) to the latest status update. However, in intelligent IoT applications such as video surveillance, the status information is revealed after some computation-intensive and time-consuming data processing operations, which would affect the status freshness. In this article, we propose a novel metric, Age of Processing (AoP), to quantify such status freshness, which captures the time elapsed of the newest received processed status data since it is generated. Compared with AoI, AoP further takes the data processing time into account. Since an IoT device has limited computation and energy resources, the IoT device can choose to offload the data processing to the nearby edge server under constrained status sampling frequency. We aim to minimize theaverageAoP in a long-term process by jointly optimizing the status sampling frequency and processing offloading policy. We first formulate this online problem as an infinite-horizon constrained Markov decision process (CMDP) with an average reward criterion. We then transform the CMDP problem into an unconstrained Markov decision process (MDP) by leveraging a Lagrangian method, and accordingly propose a Lagrangian transformation framework for the original CMDP problem. Furthermore, we integrate the framework with a perturbation-based refinement mechanism for achieving the optimal policy of the CMDP problem. Our investigation shows that to minimize the average AoP: 1) for processing offloading: the policy exploits good channel state to offload processing to the edge server and 2) for status sampling: the waiting time presents a threshold structure. Extensive numerical evaluations show that the proposed algorithm outperforms the benchmarks, with an average AoP reduction up to 30%.

Age of information (AoI) and energy efficiency (EE), as two important performance metrics in status update systems, usually cannot be simultaneously optimized. This paper investigates the age and energy tradeoff for multicast networks with retransmissions, where each sensed status update is encoded as a short blocklength packet and is broadcasted to multiple destinations via independent and identically distributed error-prone channels. By considering the stopping threshold, the average AoI and EE expressions for stopping at earliest-l, stopping at preselected-l, and wait-for-all schemes are derived as a function of the packet length. On this basis, the average AoI-EE ratio is introduced and is minimized to trade off age and energy by optimizing the packet length. Numerical results indicate that among the three schemes, earliest-lscheme attains the minimum AoI when the packet length is small, and preselected-lscheme attains the maximum EE when the packet length is large. An optimal packet length can always be found for each scheme to minimize age-energy ratio. Moreover, the selection of stopping thresholdlwill also impact the age and energy performance.

Age of Information (AoI), defined as the time elapsed since the generation of the latest received update, is a promising performance metric to measure data freshness for real-time status monitoring. In many applications, status information needs to be extracted through computing, which can be processed at an edge server enabled by mobile edge computing (MEC). In this paper, we aim to minimize the average AoI within a given deadline by jointly scheduling the transmissions and computations of a series of update packets with deterministic transmission and computing times. The main analytical results are summarized as follows. Firstly, the minimum deadline to guarantee the successful transmission and computing of all packets is given. Secondly, a no-wait computing policy which intuitively attains the minimum AoI is introduced, and the feasibility condition of the policy is derived. Finally, a closed-form optimal scheduling policy is obtained on the condition that the deadline exceeds a certain threshold. The behavior of the optimal transmission and computing policy is illustrated by numerical results with different values of the deadline, which validates the analytical results.

The joint iterative decoding assisted (JIDA) algorithm is different from the pilot assisted algorithm. It does not use the pilot signal, but uses the decoding output of the decoder to estimate the carrier frequency offset. Because the decoder can effectively reduce the impact of noise exists in the received signal, the JIDA algorithm has a good performance in satellite IoT transmission system which is in a low signal noise ratio (SNR) environment. However, the JIDA algorithm is difficult to apply in the case of short frame length because of poor estimation accuracy. In this paper, we propose a method that can effectively improve the estimation accuracy of the JIDA algorithm which is an important consideration in narrowband system by using multi frame accumulation while keeping the estimation range. At the same time, we use discrete Fourier transform (DFT) to simplify the estimation expression and reduce the complexity of the algorithm. The simulation results show that the proposed improved JIDA algorithm has higher estimation accuracy than the pilot assisted algorithm at low SNR.

Interleave-Division Multiple Access (IDMA) is a multi-user scheme, in which user-specific interleavers are the only means for user separation. Its receiver involves a chip-by-chip iterative multi-user detector (MUD). In IDMA-based satellite systems, due to the high distance from the satellite to the ground and the large coverage area, the distances between the user ends (UEs) and the satellites are greatly different, which will cause serious asynchronous transmission. In this case, the complexity of MUD is approximately linear with the square of the maximum of users chip delays. Some simplified algorithms were proposed, such as the Simplified Gaussian Chip Detector (sGCD), the MUD with Probabilistic Data Association (PDA) algorithm and the simplified ESE algorithms. But these algorithms are all based on the assumption that the IDMA system is synchronous (without users chip delays). In this paper, two novel reduced complexity MUDs, based on the simplified ESE algorithms and the PDA algorithm, will be proposed for the asynchronous IDMA. We compare the performance of our detectors with the sGCD, the MUD with PDA algorithm and tow simplified ESE algorithms, in terms of Bit Error Rate (BER) and complexity with respect to the number of operations of these detectors for (Additive White Gaussian Noise) AWGN channel. The proposed detectors presents the effective trade-off between performance and complexity. Simulation results show that the proposed detectors have better BER performance than simplified ESE algorithms. Further, results show that one of our detectors outperforms the sGCD when large users chip delays exist in a satellite communication system.

Age of information (AoI) and energy efficiency (EE) are two critical metrics for real-time status update systems. However, these two metrics may not be optimized simultaneously. This paper focuses on the tradeoff between the average AoI and EE of short packets based transmissions in a multicast network with automatic repeat request. The fixed redundancy coding scheme is employed, and the encoded packet is broadcasted to destinations over additive white Gaussian noise channels. The expressions of average AoI and EE are derived and analyzed. In particular, the average AoI-EE ratio is proposed, which is minimized to achieve a tradeoff between average AoI and EE. The numerical results show that there exists an optimal packet length to achieve a compromise between average AoI and EE.

Age of Information (AoI) is a fundamentally important metric to characterize the freshness of information in real-time Internet-of-Things (IoT) monitoring systems. Another important metric is the energy cost for information sensing and transmission. In this article, we investigate the average AoI and energy cost for low-density parity-check coded status update with and without automatic repeat request (ARQ), where the fixed redundancy scheme is employed. The non-ARQ, classical ARQ, truncated ARQ, and truncated hybrid ARQ with chase combining (HARQ-CC) schemes are analyzed and compared. By using the renewal processes theory, the expressions for the average AoI as well as the average energy cost of each considered scheme are derived. Both the lower bound of age and the upper bound of energy are provided. It is shown through simulation results that the average AoI and energy cost are mainly influenced by network parameters in the low signal-to-noise ratio (SNR) region. With short code, the smaller average AoI can be obtained at the cost of more energy consumption. Compared with other schemes, the truncated HARQ-CC achieves the best average AoI and the moderate average energy cost, which is a compromise between the age and energy.

Age of information (AoI) is an important metric in real-time status update communication system to assess the freshness of information. Different from previous works, this paper focuses on the average AoI over additive white Gaussian noise (AWGN) channels. A fixed redundancy (FR) coding scheme is considered, which encodes each k-bits update as an n-bits packet by a low-density parity-check (LDPC) code. By using the renewal-reward theory, a closed-form expression of the average AoI under the FR scheme is derived. Simulation results show that the average AoI relies on Eb/N0, especially in the low Eb/N0region. For different Eb/N0, an optimal code length always exists to minimize the average AoI. In addition, the same average AoI can be achieved with different code lengths in the high Eb/N0region. Hence, in the high Eb/N0region, short codes are preferred especially when the transmission delay is taken into account.

Age-of-information is a novel performance metric in communication systems to indicate the freshness of the latest received data, which has wide applications in monitoring and control scenarios. Another important performance metric in these applications is energy consumption, since monitors or sensors are usually energy constrained. In this paper, we study the energy-age tradeoff in a status update system where data transmission from a source to a receiver may encounter failure due to channel error. As the status sensing process consumes energy, when a transmission failure happens, the source may either retransmit the existing data to save energy for sensing, or sense and transmit a new update to minimize age-of- information. A threshold-based retransmission policy is considered where each update is allowed to be transmitted no more than M times. Closed- form average age-of-information and energy consumption is derived and expressed as a function of channel failure probability and maximum number of retransmissions M. Numerical simulations validate our analytical results, and illustrate the tradeoff between average age-of-information and energy consumption.

In this paper, we study the problem of data collection by an unmanned aerial vehicle (UAV) from a set of sensors located on a straight line. The objective is to minimize the UAV's total aviation time while allowing each of the sensors to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping intervals, in each of which one sensor is served by the UAV. The division of the intervals, the UAV speed and the sensors' power allocation policy are sequentially optimized. We show that the optimal power allocation follows the classical water-filling policy, the optimal UAV speed can be obtained by bisection search, and the optimal division of the intervals can be determined by employing the dynamic programming (DP) approach. Numerical results show that for a single sensor case, the optimal transmission interval is symmetric over the location of the sensor. For multiple sensors, the optimal UAV speed is proportional to the given energy and inversely proportional to the data upload requirement.

In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of sensors on a straight line. The UAV can either cruise or hover while communicating with the sensors. The objective is to minimize the UAV's total flight time from a starting point to a destination while allowing each sensor to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping data collection intervals, in each of which one sensor is served by the UAV. The data collection intervals, the UAV's speed, and the sensors' transmit powers are jointly optimized. The formulated flight time minimization problem is difficult to solve. We first show that when only one sensor is present, the sensor's transmit power follows a water-filling policy and the UAV's speed can be found efficiently by bisection search. Then, we show that for the general case with multiple sensors, the flight time minimization problem can be equivalently reformulated as a dynamic programming (DP) problem. The subproblem involved in each stage of the DP reduces to handle the case with only one sensor node. Numerical results present the insightful behaviors of the UAV and the sensors. Specifically, it is observed that the UAV's optimal speed is proportional to the given energy of the sensors and the inter-sensor distance, but it is inversely proportional to the data upload requirement.

In this paper, the transmission optimization of a dual-hop decode-and-forward relaying system is investigated, where the relay capable of energy harvesting from ambient environment can work in hybrid half-duplex (HD) and/or full-duplex (FD) mode. To maximize the throughput from source to destination, the relay's working mode is optimized under the constraint of random energy arrival. In particular, upon the availability of channel state information (CSI), two cases are sequentially studied: one is that CSI is unavailable to the transmitter and the other means CSI is available to the transmitter. In the former case, a dynamic programming (DP) algorithm is proposed to find the optimal working mode of the relay; moreover, to reduce the computational complexity, a linear programming (LP)-based heuristic algorithm is developed, which performs similar to the DP algorithm. In the latter case, the optimal mode of the relay is also obtainable by the DP algorithm and an approximate DP algorithm is further developed for lower computational complexity. Simulation results demonstrate that the hybrid mode outperforms pure HD and FD modes given that self-interference is efficiently suppressed.

Massive MIMO technique is expected to greatly improve spectrum efficiency as well as energy efficiency of a communication system. However, to obtain the benefits, the system needs to get the ideal channel state information (CSI). In practice, however, a pilot-based channel estimation scheme needs to be applied to obtain the CSI, which may cause a serious pilot contamination problem for multi-cell massive MIMO systems. To deal with this issue, an Iterative Interference Cancellation based MMSE Channel Estimation Algorithm is proposed in this paper, by iteratively eliminating the inter-cell interference. Compared to conventional channel estimation algorithms, the proposed algorithm can effectively improve the channel estimation accuracy of a target cell with low computational complexity. Simulation results are provided to demonstrate the advantage of the proposed algorithm.

Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme in 5G systems for the simultaneous access of tremendous number of wireless nodes. On the other hand, RF-enabled wireless energy harvesting is a promising technology for self-sustainable wireless nodes. In this paper, we consider a NOMA system where the near user harvests energy from the strong radio signal to power-on the information decoder. A generalized energy harvesting framework is proposed by combining the conventional time switching and power splitting scheme, and the achievable rate regions for time switching and power splitting are characterized in closed-form. Numerical results demonstrate the relationship among generalized scheme, time switching scheme and power splitting scheme.

In wireless communication systems powered by harvested energy, besides the channel fading, there is another dimension of dynamics induced by energy arrival variations, which makes the design of wireless transmission policies nontrivial. In this paper, we propose a framework for analyzing the energy harvesting powered wireless transmissions where the channel fading and the energy arrival variations are of different timescales. We define the duration between two consecutive changes of energy arrival rate as an energy harvesting frame, which consists of N channel fading slots. The power allocation problem can be formulated as an Markov decision process (MDP), and can be decoupled into two sub-problems. The inner problem deals with the power allocation in channel fading timescale in every N slots where the energy arrival rate keeps constant, and the outer problem deals with the energy management in energy harvesting timescale among frames. The two sub-problems can be solved by finite horizon dynamic programming (DP) and infinite horizon DP, respectively. Numerical simulations show that the average rate decreases slightly as N increases, and the rate under i.i.d. channel is higher than that under Markov channel.

Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme in 5G systems to simultaneously accommodate tremendous number of wireless nodes. On the other hand, RF-enabled wireless energy harvesting is a promising technology for self-sustained wireless devices. In this paper, we study a NOMA system where the near user harvests energy from the strong radio signal to power the information decoder. Both constant and dynamic decoding power consumption models are considered. For the constant decoding power model, the achievable rate regions for time switching and power splitting are characterized in closed-form. A generalized scheme is proposed by combining the conventional time switching and power splitting schemes, and its achievable rate region can be found by solving two convex optimization subproblems. For the dynamic decoding power model where the decoding power consumption is proportional to data rate, the achievable rate region can be found by a low-complexity search algorithm. Numerical results show that the achievable rate region of the generalized scheme is larger than those of the time switching scheme and power splitting scheme, and rate-dependent decoder design helps to enlarge the achievable rate region.

Motivated by the rapid development of energy harvesting technology and content-aware communication in access networks, this paper considers the push mechanism design in small-cell base stations (SBSs) powered by renewable energy. A user request can be satisfied by either push or unicast from the SBS. If the SBS cannot handle the request, the user is blocked by the SBS and is served by the macro-cell BS instead, which typically consumes more energy. We aim to minimize the ratio of user requests blocked by the SBS to total number of user requests. With finite battery capacity, Markov decision process-based problem is formulated, and the optimal policy is found by dynamic programming (DP). Two threshold-based policies are proposed: the push-only threshold-based policy and the energy-efficient threshold-based policy, and the closed-form blocking probabilities with infinite battery capacity are derived. Numerical results show that the proposed policies outperform the conventional non-push policy if the content popularity changes slowly or the content request generating rate is high, and can achieve the performance of the greedy optimal threshold-based policy. In addition, the performance gap between the threshold-based policies and the DP optimal policy is small when the energy arrival rate is low or the request generating rate is high.

In this paper, we consider a renewable energy powered wireless relay node which can work in either full- duplex (FD) or half-duplex (HD) mode. It decodes and stores data bits sent from source node, and then transfers them to destination node. We aim to maximize the average throughput from source to destination by optimizing the relay's working mode under the random energy arrival constraint. Optimal and sub-optimal policies are obtained by dynamic programming algorithm and linear programming based heuristic algorithm, respectively. It is found that pure FD/HD mode can achieve the optimal at high energy arrival rate regime or low rate regime. With moderate energy arrival rate, hybrid FD/HD mode is preferred, and the proposed heuristic algorithm performs close to the optimal. In addition, HD is optimal at low SNR regime as the strong self-interference greatly degrades the performance of FD.

In this paper, we explore the content information to design the joint caching and push mechanism in the small-cell base stations (SBSs) powered by renewable energy. The problem is formulated as a Markov decision process by exploring the features of content popularity and renewal and by taking into consideration the energy consumption for both content fetch from core network and push to the users. The objective is to minimize the number of requests which cannot be met by the SBSs. We adopt the policy iteration algorithm to obtain the optimal caching and push policy. According to the numerical results, the performance gain with large SBS cache size is marginal due to the limited energy. We also find that the optimal policy reveals noticeable performance gain compared with the greedy fetch policy and the non-push policy. In addition, simulations shows the tradeoff between the number of cached contents in the SBS and the available energy for content push.

In order to fully enjoy high rate broadband multimedia services, prolonging the battery lifetime of user equipment is critical for mobile users, especially for smartphone users. In this paper, the problem of distributing cellular data via a wireless power transfer enabled collaborative mobile cloud (WeCMC) in an energy efficient manner is investigated. WeCMC is formed by a group of users who have both functionalities of information decoding and energy harvesting, and are interested for cooperating in downloading content from the operators. Through device-to-device communications, the users inside WeCMC are able to cooperate during the downloading procedure and offload data from the base station to other WeCMC members. When considering multi-input multi-output wireless channel and wireless power transfer, an efficient algorithm is presented to optimally schedule the data offloading and radio resources in order to maximize energy efficiency as well as fairness among mobile users. Specifically, the proposed framework takes energy minimization and quality of service requirement into consideration. Performance evaluations demonstrate that a significant energy saving gain can be achieved by the proposed schemes.

With small cell base stations (SBSs) densely deployed in addition to conventional macro base stations (MBSs), the heterogeneous cellular network (HCN) architecture can effectively boost network capacity. To support the huge power demand of HCNs, renewable energy harvesting technologies can be leveraged. In this paper, we aim to make efficient use of the harvested energy for on-grid power saving while satisfying the quality of service (QoS) requirement. To this end, energy-aware traffic offloading schemes are proposed, whereby user associations, ON-OFF states of SBSs, and power control are jointly optimized according to the statistical information of energy arrival and traffic load. Specifically, for the single SBS case, the power saving gain achieved by activating the SBS is derived in closed form, based on which the SBS activation condition and optimal traffic offloading amount are obtained. Furthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is proposed for the multiple-SBS case, considering various operating characteristics of SBSs with different power sources. Simulation results demonstrate that the proposed scheme can achieve more than 50% power saving gain for typical daily traffic and solar energy profiles, compared with the conventional traffic offloading schemes.

This paper considers two base stations (BSs) powered by renewable energy serving two users cooperatively. With different BS energy arrival rates, a fractional joint transmission (JT) strategy is proposed, which divides each transmission frame into two subframes. In the first subframe, one BS keeps silent to store energy, while the other transmits data, and then, they perform zero-forcing JT (ZF-JT) in the second subframe. We consider the average sum-rate maximization problem by optimizing the energy allocation and the time fraction of ZF-JT separately. First, the sum-rate maximization for given energy budgets in each frame is analyzed. We prove that the optimal transmit power can be derived in closed form, and the optimal time fraction can be found via bi-section search. Second, an approximate dynamic programming algorithm is introduced to determine the energy allocation among frames. We adopt a linear approximation with the features associated with system states and determine the weights of features by simulation. We also operate the approximation several times with random initial policy, named policy exploration, to broaden the policy search range. Numerical results show that the proposed fractional JT greatly improves the performance. In addition, appropriate policy exploration is shown to perform close to the optimal.

Cloud radio access network (C-RAN) was proposed recently to reduce network cost, enable cooperative communications, and increase system flexibility through centralized baseband processing. By pooling multiple virtual base stations (VBSs) and consolidating their stochastic computational tasks, the overall computational resource can be reduced, achieving the so-called statistical multiplexing gain. In this paper, we evaluate the statistical multiplexing gain of VBS pools using a multi-dimensional Markov model, which captures the session-level dynamics and the constraints imposed by both radio and computational resources. Based on this model, we derive a recursive formula for the blocking probability and also a closed-form approximation for it in large pools. These formulas are then used to derive the session-level statistical multiplexing gain of both real-time and delay-tolerant traffic. Numerical results show that VBS pools can achieve more than 75% of the maximum pooling gain with 50 VBSs, but further convergence to the upper bound (large-pool limit) is slow because of the quickly diminishing marginal pooling gain, which is inversely proportional to a factor between the one-half and three-fourth power of the pool size. We also find that the pooling gain is more evident under light traffic load and stringent quality of service requirement.

To provide the paradigm shift of green cellular communications, Hyper Cellular Architecture (HCA), has been proposed, in which the common control functionalities are decoupled from the data service functionalities at base station (BS) level so that the traffic BSs can be more adaptive to the temporal and spatial traffic fluctuations. In this paper, we develop a system level simulator (SLS) for HCA to evaluate the HCA performance under temporal and spatial traffic fluctuations. The SLS enjoys low complexity, open interface and completed functions through the carefully tuned modeling on long-term large-scale traffic model, the separation architecture and the resource allocation strategies. Simulation results show that even with some basic BS sleeping algorithms, HCA can achieve up to 45% energy efficiency (EE) gain over conventional cellular architecture with macro BSs only or heterogeneous network during the low traffic period, and about 36% EE gain on average for a typical daily traffic pattern.

Energy harvesting (EH), which explores renewable energy as a supplementary power source, is a promising 5G technology to support the huge energy demand of heterogeneous cellular networks (HCN). However, the random arrival of renewable energy brings great challenges to network management. By adjusting the distribution of traffic load in spatial domain, traffic shaping helps to balance the cell-level power demand and supply, and thus improves the utilization of renewable energy. In this paper, we investigate the power saving performance of traffic shaping in an analytical way, based on the statistic information of energy arrival and traffic load. Specifically, an energy-optimal traffic shaping scheme (EOTS) is devised for HCNs with EH, whereby the on-off state of the off-grid small cell and the amount of offloading traffic are adjusted dynamically with the energy variation, to minimize the on-grid power consumption. Numerical results are given to demonstrate that for the daily traffic and solar energy profiles, EOTS scheme can significantly reduce the energy consumption, compared with the greedy method where users are always offloaded to the off-grid small cell with priority.

Motivated by the recent development of energy harvesting communications, and the trend of multimedia contents caching and push at the access edge and user terminals, this paper considers how to design an effective push mechanism of energy harvesting powered small-cell base stations (SBSs) in heterogeneous networks. The problem is formulated as a Markov decision process by optimizing the push policy based on the battery energy, user request and content popularity state to maximize the service capability of SBSs. We extensively analyze the problem and propose an effective policy iteration algorithm to find the optimal policy. According to the numerical results, we find that the optimal policy reveals a state dependent threshold based structure. Besides, more than 50% performance gain is achieved by the optimal push policy compared with the non-push policy.

The baseband-up centralization architecture of radio access networks (C-RAN) has recently been proposed to support efficient cooperative communications and reduce deployment and operational costs. However, the massive fronthaul bandwidth required to aggregate baseband samples from remote radio heads (RRHs) to the central office incurs huge fronthauling cost, and existing baseband compression algorithms can hardly solve this issue. In this paper, we propose a graph-based framework to effectively reduce fronthauling cost through properly splitting and placing baseband processing functions in the network. Baseband transceiver structures are represented with directed graphs, in which nodes correspond to baseband functions, and edges to the information flows between functions. By mapping graph weighs to computational and fronthauling costs, we transform the problem of finding the optimum location to place some baseband functions into the problem of finding the optimum clustering scheme for graph nodes. We then solve this problem using a genetic algorithm with customized fitness function and mutation module. Simulation results show that proper splitting and placement schemes can significantly reduce fronthauling cost at the expense of increased computational cost. We also find that cooperative processing structures and stringent delay requirements will increase the possibility of centralized placement.

To further improve the energy efficiency of heterogeneous networks, a separation architecture called hyper-cellular network (HCN) has been proposed, which decouples the control signaling and data transmission functions. Specifically, the control coverage is guaranteed by macro base stations (MBSs), whereas small cells (SCs) are only utilized for data transmission. Under HCN, SCs can be dynamically turned off when traffic load decreases for energy saving. A fundamental problem then arises: how many SCs can be turned off as traffic varies? In this paper, we address this problem in a theoretical way, where two sleeping schemes (i.e., random and repulsive schemes) with vertical inter-layer offloading are considered. Analytical results indicate the following facts: 1) under the random scheme where SCs are turned off with certain probability, the expected ratio of sleeping SCs is inversely proportional to the traffic load of SC-layer and decreases linearly with the traffic load of MBS-layer; 2) the repulsive scheme, which only turns off the SCs close to MBSs, is less sensitive to the traffic variations; and 3) deploying denser MBSs enables turning off more SCs, which may help to improve network energy-efficiency. Numerical results show that about 50% SCs can be turned off on average under the predefined daily traffic profiles, and 10% more SCs can be further turned off with inter-layer channel borrowing.

Facing the explosion of mobile data traffic, cloud radio access network (C-RAN) is proposed recently to overcome the efficiency and flexibility problems with the traditional RAN architecture by centralizing baseband processing. However, there lacks a mathematical model to analyze the statistical multiplexing gain from the pooling of virtual base stations (VBSs) so that the expenditure on fronthaul networks can be justified. In this paper, we address this problem by capturing the session-level dynamics of VBS pools with a multi-dimensional Markov model. This model reflects the constraints imposed by both radio resources and computational resources. To evaluate the pooling gain, we derive a product-form solution for the stationary distribution and give a recursive method to calculate the blocking probabilities. For comparison, we also derive the limit of resource utilization ratio as the pool size approaches infinity. Numerical results show that VBS pools can obtain considerable pooling gain readily at medium size, but the convergence to large pool limit is slow because of the quickly diminishing marginal pooling gain. We also find that parameters such as traffic load and desired Quality of Service (QoS) have significant influence on the performance of VBS pools.

To further improve energy efficiency from the view of the whole network, a separation architecture has been proposed, where the control plane and data plane are separated and implemented by different base stations. Under this architecture, the data base stations (DBS) can be turned off adaptively according to the traffic load while signaling base stations (SBS) provide the guarantee of coverage. A key issue of this architecture is the design of effective BS sleeping mechanisms, which should guarantee the quality of service (QoS) and minimize network power consumption. In this paper, a probabilistic DBS sleeping mechanism is proposed and optimized under the separation architecture. Users within the sleeping DBSs are offloaded to SBSs for QoS guarantee. An optimization problem is formulated, where the sleeping probability and spectrum resource allocation are jointly optimized to minimize network power consumption. The optimal BS sleeping scheme is found to be threshold-based. When the ratio of sleeping DBSs is below a certain threshold which depends on the traffic load, the lightly-loaded DBSs should be turned off first; otherwise, only the heavily loaded DBSs go into sleep. Numerical results show nearly 30% energy can be saved under a typical daily traffic profile, and there exists a tradeoff between energy saving and network capacity.

The combination of energy harvesting and large-scale multiple antenna technologies provides a promising solution for improving the energy efficiency (EE) by exploiting renewable energy sources and reducing the transmission power per user and per antenna. However, the introduction of energy harvesting capabilities into large-scale multiple antenna systems poses many new challenges for energy-efficient system design due to the intermittent characteristics of renewable energy sources and limited battery capacity. Furthermore, the total manufacture cost and the sum power of a large number of radio frequency (RF) chains can not be ignored, and it would be impractical to use all the antennas for transmission. In this paper, we propose an energy-efficient antenna selection and power allocation algorithm to maximize the EE subject to the constraint of user's quality of service (QoS). An iterative offline optimization algorithm is proposed to solve the non-convex EE optimization problem by exploiting the properties of nonlinear fractional programming. The relationships among maximum EE, selected antenna number, battery capacity, and EE-SE tradeoff are analyzed and verified through computer simulations.

We consider energy-efficient wireless resource management in cellular networks where base stations (BSs) are equipped with energy harvesting devices, using statistical information for traffic intensity and renewable energy. The problem is formulated as adapting BSs' on-off states, active resource blocks (e.g., subcarriers), and renewable energy allocation to minimize the average grid power consumption while satisfying the users' quality of service (blocking probability) requirements. It is transformed into an unconstrained optimization problem to minimize a weighted sum of grid power consumption and blocking probability. A two-stage dynamic programming algorithm is proposed to solve this problem, by which the BSs' on-off states are optimized in the first stage, and the active BSs' resource blocks are allocated iteratively in the second stage. Compared with the optimal joint BSs' on-off states and active resource blocks allocation algorithm, the proposed algorithm greatly reduces the computational complexity and can achieve the optimal performance when the traffic is uniformly distributed.

This paper studies the resource allocation problem of a single cell powered jointly by renewable energy and power grid over a given time period (e.g. 24 hours), using statistical information of traffic intensity and harvested energy. Specifically, the problem is formulated as minimizing the average grid power input while satisfying users' quality of service (outage probability) requirements. We analyze the outage probability, and solve the grid power minimization problem indirectly by obtaining a power-outage tradeoff curve using the dynamic programming (DP) approach. Some heuristic algorithms are proposed and compared with the DP algorithm by simulations. The results show that the DP algorithm greatly reduces the grid power consumption compared with the heuristic methods, among which the joint traffic-energy-aware resource allocation performs closest to the optimal solution.

This paper considers the power allocation of a single-link wireless communication with joint energy harvesting and grid power supply. We formulate the problem as minimizing the grid power consumption with random energy and data arrival in fading channel, and analyze the structure of the optimal power allocation policy in some special cases. For the case that all the packets are arrived before transmission, it is a dual problem of throughput maximization, and the optimal solution is found by the two-stage water filling (WF) policy, which allocates the harvested energy in the first stage, and then allocates the power grid energy in the second stage. For the random data arrival case, we first assume grid energy or harvested energy supply only, and then combine the results to obtain the optimal structure of the coexisting system. Specifically, the reverse multi-stage WF policy is proposed to achieve the optimal power allocation when the battery capacity is infinite. Finally, some heuristic online schemes are proposed, of which the performance is evaluated by numerical simulations.

We consider multiple base station (BS) cooperative transmission in downlink cellular networks to improve the spectral efficiency and the system capacity. Grouping BSs into clusters is a practical solution to realize BSs cooperation and reduce system complexity. However, it still suffers from inter-cluster interference, especially for the cluster-edge users. In this paper, clustering and scheduling are jointly considered to deal with the problem. The clusters are formed dynamically from users' point of view to minimize the inter-cluster interference, and are allowed to be overlapped. Accordingly, coordinated precoding scheme is designed to manage the intra-cluster interference. A greedy scheduling algorithm is proposed jointly with dynamic clustering. Simulations show that the proposed joint algorithm provides impressive average throughput gain over the non-joint ones, and the user fairness is improved significantly.

Adaptive modulation and coding (AMC) has been widely used to improve the spectral efficiency. In this paper, we take a different look at it from energy saving point of view. Specifically, we analyze the queuing behavior of AMC systems jointly with sleep mode where the wake-up process incurs time and energy cost. We formulate the optimization problem by jointly considering energy-efficiency, queuing delay and packet loss rate, and find the solution with cross-layer adjustment of the transmit power and the sleep threshold. Numerical results show that at low traffic range, when the power consumption of idle (no data transmission) mode is un-negligible, introducing sleep mode to the AMC system significantly improves the energy efficiency compared with non-sleep system. To achieve the energy-efficiency gain, the system tends to use higher-order modulation by increasing transmit power, which also reduces the number of dropped packets.

This paper focuses on quantized channel state information (CSI) feedback for downlink network MIMO systems. Specifically, we propose to quantize and feedback the CSI of a subset of BSs, namely the feedback set. Our analysis reveals the tradeoff between better interference mitigation with large feedback set and high CSI quantization precision with small feedback set. Given the number of feedback bits and instantaneous/long-term channel conditions, each user optimizes its feedback set distributively according to the expected SINR derived from our analysis. Simulation results show that the proposed feedback adaptation scheme provides substantial performance gain over non-adaptive schemes, and is able to effectively exploit the benefits of network MIMO under various feedback bit budgets.

The energy consumption of information and communication technology (ICT) industry has become a serious problem, which mostly comes from the network infrastructure, rather than the mobile terminals. In this paper, we consider densely deployed cellular networks where the coverage of base stations (BSs) overlaps and the traffic intensity varies over time and space. An energy saving algorithm is proposed by dynamically adjusting the working modes (active or sleeping) of BSs according to the traffic variation with respect to certain blocking probability requirement. In addition, to prevent frequent mode switching, BSs are set to hold their current working modes for at least a given interval. Simulations demonstrate that the proposed strategy can greatly reduce energy consumption with blocking probability guarantee, and the performance is insensitive to the mode holding time within certain range.

Multiple base station (Multi-BS) cooperation has been considered as a promising mechanism to suppress cochannel interference and boost the capacity for cellular networks. However, the large feedback and signaling overhead hinder it from practice. Therefore, limited cooperation among BSs is recognized as a good tradeoff between the performance gain and the relevant cost. In this paper, the whole network is divided into small disjointing BS cooperation groups, namely, clusters. A decentralized framework is proposed to facilitate the BS cluster formation on the downlink, in order to maximize the sum-rate of the scheduled mobile stations (MSs) under the cluster size constraint. Moreover, an efficient BS negotiation algorithm is designed for cluster formation, of which the feedback overhead per MS is irrelevant to the network size, and the number of iteration rounds scales very slowly with the network size. Simulations show that our strategy leads to significant sum-rate gain over static clustering and performs almost the same as the centralized greedy approach. With its low signaling overhead and complexity, the proposed framework is well suited for implementation in large-scale cellular networks.