In this paper, we investigate the problem of minimizing the long-term energy consumption of mobile edge computing (MEC) systems combining reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) techniques, based on the dynamic scenarios with sequential task arrival and time-varying channels. We jointly optimize the transmission power, transmission time, offloading ratio, local computing frequency, MEC server’s computing resource allocation, and phase shift of RIS elements. The problem is formulated as a challenging time-dependent non-linear programming problem with a large number of variables. By theoretically deriving the optimal expression of server computing frequency and local computing frequency, we simplify the original problem and decompose it into two nested subproblems, namely, the joint offloading ratio and communication process optimization (JORCPO) subproblem and the RIS’s phase shift optimization (RPSO) subproblem. Then, we propose a two-level approach for the solution. In the outer level, we formulate the RPSO subproblem as a deep reinforcement learning (DRL) problem and adopt the proximal policy optimization (PPO) algorithm to obtain the real-time decision of RIS’s phase shift. In the inner level, based on the given RIS’s phase shift, we first simplify the JORCPO subproblem by deriving the closed-form expression of transmission power and then adopt the Lagrange dual (LD) method to obtain the near-optimal solution of transmission time and offloading ratio. Simulation results testify the performance advantage of the proposed solution.

Learning-based policy optimization methods have shown great potential for building general-purpose control systems. However, existing methods still struggle to achieve complex task objectives while ensuring policy safety during learning and execution phases for black-box systems. To address these challenges, we develop data-driven safe policy optimization (D2SPO), a novel reinforcement learning (RL)-based policy improvement method that jointly learns a control barrier function (CBF) for system safety and a linear temporal logic (LTL) guided RL algorithm for complex task objectives. Unlike many existing works that assume known system dynamics, by carefully constructing the data sets and redesigning the loss functions of D2SPO, a provably safe CBF is learned for black-box dynamical systems, which continuously evolves for improved system safety as RL interacts with the environment. To deal with complex task objectives, we take advantage of the capability of LTL in representing the task progress and develop LTL-guided RL policy for efficient completion of various tasks with LTL objectives. Extensive numerical and experimental studies demonstrate that D2SPO outperforms most state-of-the-art (SOTA) baselines and can achieve over 95% safety rate and nearly 100% task completion rates. The experiment video is available at https://youtu.be/2RgaH-zcmkY.

While reinforcement learning (RL) attracts increasing research attention, maximizing the return while keeping the agent safe at the same time remains an open problem. Motivated to address this challenge, this work proposes a new Fast and Safe Policy Optimization (FSPO) algorithm, which consists of three steps: the first step involves reward improvement update, the second step projects the policy to the neighborhood of the baseline policy to accelerate the optimization process, and the third step addresses the constraint violation by projecting the policy back onto the constraint set. Such a projection-based optimization can improve the convergence and learning performance. Unlike many existing works that require convex approximations for the objectives and constraints, this work exploits a first-order method to avoid expensive computations and high dimensional issues, enabling fast and safe policy optimization, especially for challenging tasks. Numerical simulation and physical experiments demonstrate that FSPO outperforms existing methods in terms of safety guarantees and task completion rate.

In this article, a dynamic multiuser multiaccess mobile-edge computing (MEC) network where users split their tasks into multiple parts and offload them concurrently to multiple MEC servers via NOMA is investigated. In order to reduce the long-term energy, a complicated time-dependent nonconvex problem with many deeply coupled variables is formulated. We employ a novel approach that integrates the theoretical derivation and the deep reinforcement learning (DRL) algorithm to solve the problem in real time. In particular, we decompose the considered energy minimization problem into a computational resource and transmission power allocation subproblem as the offloading decision and offloading time of each user are known, and an offloading decision and offloading time allocation subproblem. Then, the first subproblem is decomposed into many independent problems, and we theoretically solve them in parallel and derive the optimal solution. For the second subproblem, in order to obtain the real-time allocation of offloading time and offloading decision, we apply a DRL algorithm that adjusts the reward function with a penalty mechanism while the total offloading ratio constraint is unsatisfied or the transmission power and computational resource allocation subproblem does not have a solution. Via simulations, we verify the optimality of the proposed theoretical derivation-based solutions, and, demonstrate that the system performance can be significantly improved by the proposed approach.

In this article, we aim to minimize the longterm energy consumption of a partial offloading mobile-edge computing (MEC) system with noncompletely overlapping nonorthogonal multiple access (NCO-NOMA) when time-varying channels and continuous tasks arrival are considered. Different from the existing NCO-NOMA-assisted MEC studies, the task data transmission, offloading decision, and task computation in users and the MEC server are jointly optimized. We describe the considered energy minimization problem as a complex nonconvex problem with lots of tightly correlated variables. To solve it, we decompose it into a set of computational and communication resource optimization subproblems and a computational resource optimization subproblem at the MEC server. For the computational and communication resource optimization subproblem, we introduce new variables to transform it into a standard difference of convex functions (DC) programming and propose the concave–convex procedure (CCCP) algorithm to solve it. To find out the real-time solution for the computational resource optimization subproblem at the MEC server, we propose a deep reinforcement learning (DRL) algorithm by adding penalty mechanisms to the reward function. Simulations demonstrate that the proposed solution converges quickly and achieves satisfying performance.

In this article, we investigate the long-term energy minimization for nonorthogonal multiple access (NOMA)-based mobile edge computing (MEC) systems with user mobility, continuous tasks arrival, and time-varying channel when the reconfiguration energy cost caused by dynamic voltage frequency scaling (DVFS) technology and the effect of imperfect successive interference cancelation (SIC) decoding in NOMA transmission are taken into account. We formulate the considered problem as a nonconvex optimization problem. To solve it, we decompose it into a computation resource and transmit power optimization subproblem, and an offloading ratio and transmission time optimization subproblem. We first show that when the offloading ratio and transmission time are given, the optimal local CPU frequency, the optimal computation resource allocation in the base station, and the optimal transmit power can be theoretically derived. Then, based on the above theoretical derivation, a soft actor–critic (SAC)-based deep reinforcement learning (DRL) algorithm is proposed to learn the near-optimal offloading ratio and transmission time for users. Simulation results show that the proposed algorithms can significantly improve the system performance.

In this paper, we investigate the resource allocation of mobile edge computing (MEC) in cognitive capacity harvesting networks (CCHNs) when non-orthogonal multiple-access (NOMA) technique is adopted. Different from traditional studies for NOMA-MEC networks, we aim at minimizing the total cost of CCHN while satisfying the quality-of-service (QoS) of secondary users (SUs). We adopt the mechanism of time division multiple access (TDMA) when several NOMA groups use the same spectrum, and consider both the waiting delay and transmission delay during data offloading with the optimization of transmission order of NOMA groups. We formulate the considered problem as a mixed integer non-linear programming (MINLP). We show that the transmit power and the allocated computing resource for each SU can be derived when the transmission time and transmission order of the NOMA groups are given. Based on this, the considered problem can be decomposed into a transmission time and order optimization subproblem, a cellular resource block (CRB) selection subproblem and a cognitive radio (CR) router selection subproblem. To solve the transmission time and order optimization subproblem, we first simplify the delay constraint via theoretic analysis, and then propose a binary segmentation (B-Seg) algorithm and a transmission order adjustment (TOA) algorithm to find the optimal transmission time and transmission order of NOMA groups, respectively. To solve the CRB selection subproblem and the CR router selection subproblem, a bigger requirement first (BRF) algorithm and a game-based iteration (GBI) algorithm are respectively proposed. Simulation results show that the proposed algorithms can significantly improve the system performance.

In this paper, we investigate the energy minimization problem for a cognitive capacity harvesting network (CCHN), where secondary users (SUs) without cognitive radio (CR) capability communicate with CR routers via device-to-device (D2D) transmissions, and CR routers connect with base stations (BSs) via CR links. Different from traditional D2D networks that D2D transmissions share the resource of cellular transmissions in the same cell, we consider the scenario that D2D transmissions share the uplink cellular frequency bands (CFBs) of neighbor cells. To ensure that the transmissions from SUs do not affect the transmissions for the cellular users (CUs) in the neighbor cells, an inter-cell handshake process is proposed. We formulate the energy minimization problem for SUs as a mixed integer non-linear programming (MINLP). To solve this problem, we decompose it into two nested subproblems: a transmit power optimization subproblem and a CR router and uplink CFB selection subproblem. For the first subproblem, it is proved to be convex, and thus can be efficiently solved. For the second subproblem, we propose a two-level nested game theoretic approach to finding its solution. Simulation results show that the proposed algorithms can significantly improve the performance. With the help of CR routers/the neighbor resource sharing, the energy consumption for SUs can be saved around 30%-37% on average.

In this paper, we investigate the sum-rate maximization in the Device-to-Device (D2D) communication underlaying cellular networks, where many cellular users (CUs) share the uplink resource with the D2D pairs. We show that the system sum- rate maximization problem can be formulated as a mixed integer non-linear programming problem, which is NP-hard in general. We circumvent this difficulty by applying the optimization decomposition: 1) Given a resource allocation policy, we derive the optimal Signal to Interference plus Noise Ratio (SINR) threshold to maximize the system sum-rate. 2) We then propose a coalition game approach to further optimize the resource allocation policy, and prove that the proposed coalition game approach can converge to the Nash-stable partition in finite time. Simulation results show that 1) the performance of the coalition game is close to the exhaustive search, but its run-time is much shorter than the exhaustive search; 2) compared with several other resource allocation policies, the coalition game can achieve an average sum-rate improvement of 13%-173%, and has the best resource sharing fairness.

In this paper, we investigate the energy minimization problem with and without network coding (NC) while satisfying the transmission rate requirements in a bidirectional cellular relay network, where a group of mobile users communicate with a base station across a relay node. In particular, we consider the Rayleigh fading channel model and adopt a comprehensive power consumption model in the radio frequency transmission. We show that the problem of minimizing the energy consumption of the bidirectional cellular relay network in NC and non-NC (NNC) schemes can be formulated as a unified sum of fractional programming problem, which is of high complexity to solve in general. Fortunately, we derive the sufficient condition under which the problem is a convex optimization problem, and thus can be solved quite efficiently. In the case that the energy minimizing problem is not convex, we decompose it into two subproblems, and propose an iterative algorithm to solve it. Simulation results show that in NNC and NC schemes, under all configurations of power parameters, the performance of the iterative algorithm is close to the exhaustive search method; but its running time is much shorter than the exhaustive search method. Furthermore, compared with the maximum power transmission policy, the iterative algorithm achieves a maximum energy reduction of 75%-82%. Last but not least, we compare the energy performance of NNC and NC schemes and discuss the effect of the iteration number and the relay node placement.

In this paper, we investigate the throughput capacity of physical-layer network coding (PNC) in a non-all-inclusive carrier-sensing network with IEEE 802.11 distributed coordination function (DCF). In particular, we consider the many-to/from-one bidirectional networks in which a common center node exchanges packets with many other nodes through multihop transmissions. We first analyze the canonical networks with equal-link-length (ELL) and variable-link-length (VLL), respectively, and derive the corresponding analytical network capacity. Simulations show that the throughput capacities are reasonably tight. We further maximize the network capacity by properly selecting the signal-to-interference-plus-noise ratio (SINR) threshold/transmission rate through numerical calculation. Last but not least, we identify the optimal number of hops that has the maximum network throughput. In particular, the four-hop canonical networks have the maximum network throughput, which indicates that in a many-to/from-one network with five or more hops, it is preferable to transmit the packets across the four-hop nodes to make full use of the PNC scheme. Simulation results show that the throughput gain of PNC scheme with and without considering the synchronization cost can, respectively, reach upto 291.7% and 340.6%, compared with the traditional IEEE 802.11 multihop networks without network coding.

In this paper, we investigate the throughput performance of the network coding (NC) schemes under the slotted ALOHA protocol. We consider the all-inclusive-interfering unbalanced network in which two client groups with different numbers of nodes communicate with each other through a relay node. We derive the closed-form expressions of the network throughput under the physical-layer network coding (PNC), traditional high-layer network coding (HNC), and non-network-coding (NNC), respectively. We also show the necessary and sufficient condition to make the relay node unsaturated. From the analytical results, we find that although PNC has better transmission efficiency in the two-way relay channel (TWRC); it does not always have better network throughput when the network has multiple client nodes. To further improve the network throughput, we propose the hybrid NC scheme, which allows the relay node to turn to HNC scheme if it fails to explore the PNC transmission. We further obtain the closed-form expression of the network throughput and the necessary and sufficient condition to make the relay node unsaturated in the hybrid NC scheme. Simulation results show that the hybrid NC scheme has better throughput performance than the PNC, HNC, and NNC schemes. Moreover, we optimize the network throughput of the hybrid NC scheme in terms of the transmission probability of the relay node. Last but not least, we evaluate the throughput performance of hybrid NC scheme through simulations.

Abstract In this paper, we investigate the carrier sensing range (CSR) of a general 802.11 network with physical‐layer network coding (PNC). We aim to derive a sufficient CSR that can prevent the hidden‐node collisions in a general 802.11 PNC network. The analysis includes two steps. First, we analyze the six link‐to‐link interference cases in an 802.11 PNC network to show that the mutual interference will be most severe when each node in the network initiates a two‐hop end node link. Second, we consider the worst interference case that all concurrently transmitting links in the network are two‐hop end node links and placed in the densest manner and develop a closed‐form expression of a sufficient CSR that prevents the hidden‐node collisions in a PNC network. From the analysis results, we find that to prevent the hidden‐node collisions, the CSR in PNC network should be bigger than the one in traditional non‐network‐coding network. Furthermore, we carry out extensive simulations to find out the throughput gain of PNC scheme in a general wireless network when considering the impact of CSR. Simulation results show that compared with the non‐network‐coding scheme, PNC scheme has throughput gain when a large proportion (i.e., 90%) of links in the network are two‐hop links and the link density has little effect on the throughput gain of PNC scheme. Copyright © 2014 John Wiley & Sons, Ltd.

The time‐domain maximum likelihood estimation of chirp signal parameters is investigated in this study. Three amplitude weighted phase‐based estimators and two phase‐unwrapping algorithms, that is, phase prediction and unwrapping algorithm (PP‐UA) and differenced‐phase prediction and unwrapping algorithm (DPP‐UA) were proposed. The DPP‐UA and PP‐UA have their merits and drawbacks. The authors combine the merits of these two methods and propose a new phase‐unwrapping algorithm, which outperforms PP‐UA and DPP‐UA under low signal‐to‐noise ratio (SNR) conditions. The authors’ further study on the optimal number of initial data sample show that it is SNR‐related and can be determined according to the ‘3 σ ’ rule and the Cramer–Rao lower bound.

In this paper, we investigate the throughput performance of \rev{physical-layer network coding} (PNC) under the IEEE 802.11 distributed coordination function (DCF). We consider the wireless network that two client groups communicate with each other across one relay node, and focus on the unsaturated network case. The difficulty in modeling the relay systems under the IEEE 802.11 DCF is that the minimum contention window sizes of the client nodes and the relay node may be different, which makes the traditional throughput analysis methods for the non-relay wireless networks inapplicable. Fortunately, we find that the relay system can be decomposed into four parts and respectively modeled. Analytical results show that the throughput gain of PNC scheme is heavily affected by the probability that a transmitted network-coding (NC) packet contains the information of two packets. The implication is that the throughput benefit of PNC is more significant for bidirectional isochronous traffic with rate requirements. \rev{We further derive an approximate closed-form solution of the optimal transmission probability of client nodes that maximizes the PNC network throughput.} We validate our analytical model through extensive simulations and discuss the relationship between the PNC network throughput and other system parameters, such as the minimum contention window sizes of both the client nodes and the relay node.

Topology information plays an important role in network management. The existing methods for topology inference based on end-to-end measurements need a threshold for general topologies, which is difficult to select to ensure the inference accuracy. In this paper, we propose a sort-based approach, named SBA, to infer the general topologies without using a threshold. First, a sort-based clustering algorithm, named SBC-AL, is proposed to cluster a group of nodes in which every node has at least one sibling. In the SBA, the nodes are classified into disjoint groups by a fan-out decrement mechanism. Then the SBA uses the SBC-AL to cluster the nodes group by group from the bottom up to infer the topology. We prove that the SBA is consistent and suitable for general topologies. The simulation results show that the SBA has a good performance in both accuracy and efficiency.

Network tomography has been widely used recently to obtain the network internal characteristics by end-to-end measurement. In this study, the authors consider the problem of estimating link loss rates using network tomography. The existing work based on maximum likelihood estimator (MLE) uses iterative approximation to make the inference, which requires a long execution time for large scale network. To overcome this limitation, the authors propose a fast path-based approach (FPA) by explicit estimation to infer the loss rate of links. Instead of estimating the link loss rates directly, the authors first estimate the path loss rates that are used to derive the link loss rates. In addition, the path loss rates are inferred by a new estimator which is an explicit function of loss observations. The authors evaluate the accuracy of this approach through the analysis of the loss rate estimator and simulation. The estimator is proved to be consistent and have the same asymptotic variance as that of the MLE. The simulation results show that the estimated loss rates using the FPA correctly converge to the real loss rates.

Network-on-Chip (NoC) design methodology is considered as an important trend for large System-on-Chip design because of the bandwidth and power constraints in traditional synchronous bus architecture. In the design of packet-based NoC, packetizing mechanism has great effect on communication performance, area, and energy consumption of NoC. In this paper, we carry out detailed simulation to evaluate several kinds of packetizing mechanisms of NoC based on topology of Ring and Spidergon. Simulation results show that Condition-Waiting adaptive packetizing mechanism (CW-APM) is the best trade-off packetizing mechanism in NoC design.

In this paper, a hierarchical cluster-based irregular topology customization method is proposed for Networks-on-Chip (NoC). This method contains three steps: (1) partitioning IPs into many hierarchical clusters; (2) generating a core network; (3) deleting redundant edge routers. Results show that the irregular topologies generated by our hierarchical cluster-based method consume less power when satisfying the bandwidth and port number constraints. Compared with the previous method, our method can save about 15.41% of power averagely for all benchmark applications. Particularly, for MPEG 4 decoder, our method can save 31.62% of power.

Network tomography has been widely used recently as an method to infer the network internal link-level characteristics by end-to-end measurement. In this paper, we consider the problem of estimating link loss rates using network tomography. The existing methods make the inference based on the whole tree of network, which is very complex for large scale network. To overcome this limitation, we propose a low complexity inference approach named LCIA. In the LCIA, we deploy monitors at internal nodes to reduce the complexity of inferring the link loss rates. It mainly consists of two steps. The first step is to deploy monitors at specific internal nodes to divide the original tree into several sub-trees with minimum depth. The second step is to infer the link loss rates of sub-trees by a new estimator which is an explicit function of loss measurements. The LCIA has the following features. First, it greatly reduces the inference complexity as the inference on the sub-trees is much simpler. Second, it improves the accuracy of the estimated results since the variance of loss estimator on sub-trees with lower depth is smaller than that on the original tree. The analytical and simulation results demonstrate that the LCIA outperforms the existing methods both on computation complexity and inference accuracy.