Optimal Power Control Problem Research Articles

Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing

The fifth generation and beyond wireless communication will support vastly heterogeneous services and user demands such as massive connection, low latency and high transmission rate. Network slicing has been envisaged as an efficient technology to meet these diverse demands. In this paper, we propose a dynamic virtual resources allocation scheme based on the radio access network (RAN) slicing for uplink communications to ensure the quality-of-service (QoS). To maximum the weighted-sum transmission rate performance under delay constraint, formulate a joint optimization problem of subchannel allocation and power control as an infinite-horizon average-reward constrained Markov decision process (CMDP) problem. Based on the equivalent Bellman equation, the optimal control policy is first derived by the value iteration algorithm. However, the optimal policy suffers from the widely known curse-of-dimensionality problem. To address this problem, the linear value function approximation (approximate dynamic programming) is adopted. Then, the subchannel allocation Q-factor is decomposed into the per-slice Q-factor. Furthermore, the Q-factor and Lagrangian multipliers are updated by the use of an online stochastic learning algorithm. Finally, simulation results reveal that the proposed algorithm can meet the delay requirements and improve the user transmission rate compared with baseline schemes.

DIYA: Tactile Internet Driven Delay Assessment NOMA-Based Scheme for D2D Communication

Device-to-device (D2D) two-hop cooperative communication improves the network coverage and throughput to provide the quality of service and quality of experience to the end users. Nonorthogonal multiple access (NOMA) can be used at the D2D transmitter to improve the spectral efficiency of the network. But, two-hop transmission with NOMA suffers from delay and interference from the neighboring nodes. To resolve the aforementioned issues, in this paper, we propose Tactile Internet (TI) driven delay assessment for D2D communication (DIYA) scheme, which works in two phases. In the first phase, a full duplex communication at relays (intermediate nodes) is used to have the first- and second-hop transmission simultaneously in the same time slot. Then, TI-based communication is used at D2D transmitter to increase the speed of transmission. In the second phase, pricing-based three-dimensional (3-D) matching is proposed to improve the throughput of the cell edge users along with the mitigation of cochannel interference. Also, the power of the D2D transmitter is optimized using successive convex approximation with low complexity, which converts the nonconvex optimization problem of subchannel allocation and power control into convex problem. Numerical results demonstrate that DIYA achieves higher throughput with reduced delay in comparison to other existing orthogonal multiple access (OMA) and NOMA-based schemes.

An Anti-Jamming Hierarchical Optimization Approach in Relay Communication System via Stackelberg Game

In this paper, we study joint relay selection and the power control optimization problem in an anti-jamming relay communication system. Considering the hierarchical competitive relationship between a user and jammer, we formulate the anti-jamming problem as a Stackelberg game. From the perspective of game, the user selects relay and power strategy firstly which acts as the leader, while the jammer chooses power strategy then that acts as follower. Moreover, we prove the existence of Stackelberg equilibrium. Based on the Q-learning algorithm and multi-armed bandit method, a hierarchical joint optimization algorithm is proposed. Simulation results show the user’s strategy selection probability and the jammer’s regret. We compare the user’s and jammer’s utility under the proposed algorithm with a random selection algorithm to verify the algorithm’s superiority. Moreover, the influence of feedback error and eavesdropping error on utility is analyzed.

NOMA Aided Narrowband IoT for Machine Type Communications With User Clustering

To support machine type communications (MTCs) in next generation mobile networks, Narrowband-Internet of Things (NB-IoT) has been released by the third generation partnership project (3GPP) as a promising solution to provide extended coverage and low energy consumption for low cost MTC devices. However, the existing orthogonal multiple access (OMA) scheme in NB-IoT cannot provide connectivity for a massive number of MTC devices. In parallel with the development of NB-IoT and non-OMA (NOMA), introduced for the fifth generation wireless networks, is deemed to significantly improve the network capacity by providing massive connectivity through sharing the same spectral resources. To leverage NOMA in the context of NB-IoT, we propose a power domain NOMA scheme with user clustering for an NB-IoT system. In particular, the MTC devices are assigned to different ranks within the NOMA clusters where they transmit over the same frequency resources. Then, we formulate an optimization problem to maximize the total throughput of the network by optimizing the resource allocation of MTC devices and NOMA clustering while satisfying the transmission power and quality of service requirements. We prove the NP-hardness of the proposed optimization problem. We further design an efficient heuristic algorithm to solve the proposed optimization problem by jointly optimizing NOMA clustering and resource allocation of MTC devices. Furthermore, we prove that the reduced optimization problem of power control is a convex optimization task. Simulation results are presented to demonstrate the efficiency of the proposed scheme.

Optimal energy-efficient transmission for hybrid spectrum sharing in cooperative cognitive radio networks

In order to improve the energy efficiency (EE) in cognitive radio (CR), this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing, meanwhile considering the maximum transmit power, user quality of service (QoS) requirements, interference limitations, and primary user protection. The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem. The Dinkelbach's method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction. Then, an iterative power allocation algorithm is proposed to solve the optimization problem. The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.

Power control for multi-sensor remote state estimation over interference channel

In this paper, we investigate a multi-sensor transmission power control problem for remote state estimation over interference channel. There are N sensors measuring a dynamic process at the same time and the sensors send their measurements to a remote state estimator for data fusion through a shared packet-dropping channel, where the packet drop rate is related to their transmission power. Traditionally, the transmission power design problem does not consider the situation where the communication process of each sensor may be affected by the interference power from other sensors. To address this issue, we consider a signal-to-interference-and-noise-ratio (SINR)-based channel model to describe the simultaneous transmission process and formulate such a power control problem into a stochastic programming problem. A certainty-equivalent problem is proposed to tackle the difficulties brought by the implicit expression of the objective function and the non-linearity/non-convexity of the original problem. By utilizing an approximated SINR quantity, a sub-optimal solution to the optimal power control problem is also obtained by solving a standard convex optimization problem. An event-based power controller, a heuristic power controller, and the power control problem with a threshold-based channel model are also discussed. Performance analysis of the sub-optimal controller and the comparison with other power controllers are demonstrated to show the effectiveness of our methods.

Energy-Efficient Resource Allocation for Machine-Type Communications in Smart Grid Based on a Matching With Externalities Approach

Machine-type communication plays a critical role in the reliable operation of the smart grid. To overcome the problem of spectrum scarcity, the massive number of machine-type devices (MTDs) are allowed to reuse the spectrum resource allocated to the cellular users (CUs) opportunistically. However, the complicated interference caused by channel reusing and the energy efficiency (EE) issues pose new challenges on resource allocation. In this paper, we investigate the problem of EE maximization for MTDs via jointly optimizing channel selection and power control. A two-stage optimization algorithm is proposed based on many-to-one matching with externalities. In the first stage, each MTD is temporally allocated to predefined transmission power, and then the channel selection optimization problem is solved by matching with externalities to derive the matching relationship between MTDs and resource blocks (RBs). In the second stage, the power control optimization problem is solved by the nonlinear fractional programming algorithm based on the established matching relationship obtained in the first stage. The simulation results demonstrate that the proposed algorithm outperforms the other two heuristic algorithms distinctly.

Scheduling and Power Control for V2V Broadcast Communications With Co-Channel and Adjacent Channel Interference

This paper investigates how to mitigate the impact of both co-channel interference and adjacent channel interference (ACI) on vehicle-to-vehicle (V2V) broadcast communication by scheduling and power control. The optimal joint scheduling and power control problem, with the objective to maximize the number of connected vehicles, is formulated as a mixed integer programming problem with a linear objective and a quadratic constraint. From the joint formulation, we derive (a) the optimal scheduling problem for fixed transmit powers as a Boolean linear programming problem and (b) the optimal power control problem for a fixed schedule as a mixed integer linear programming problem. Optimal schedules and power values can, for smaller-size instances of the problem, be computed by solving first (a) and then (b). To handle larger-size instances of the problem, we propose heuristic scheduling and power control algorithms with reduced computational complexity. Simulation results indicate that the heuristic scheduling algorithm yields significant performance improvements compared to the baseline block-interleaver scheduler and that performance is further improved by the heuristic power control algorithm. Moreover, the heuristic algorithms perform close to the optimal scheme for small instances of the problem.

Joint Offloading and Transmission Power Control for Mobile Edge Computing

With the exploding growth of smart devices and application of 5G communications, there are more and more computation-intensive tasks which are delay-sensitive. Mobile edge computing (MEC) is a new paradigm that provides rich computing resources in close proximity to mobile users. However, how to make the decision of whether offloading or not as well as control the transmission power jointly remains a challenge. In this paper, we first study the joint multiuser offloading and transmission power control optimization problem in a multi-channel wireless interference scenario. We formulate the joint optimization problem into a system-wide computation overhead minimization problem as a mixed integer non-linear program. We then propose an efficient semi-distributed algorithm consisting of two subalgorithms of offloading scheme and transmission power control. The numerical simulation results demonstrate that the proposed algorithm can effectively reduce system-wide computation overhead and increase the number of beneficial edge computing users, compared with all local or edge computing and fixed transmission power schemes.

On the Outage Probability and Power Control of D2D Underlaying NOMA UAV-Assisted Networks

Unmanned aerial vehicle (UAV) nowadays is a promising technology for boosting wireless connectivity, especially for upcoming 5G wireless networks. Due to its aerial feature, potential mobility and flexible deployment, the UAV can provide service in some specific scenarios such as post-disaster network recovery or no-infrastructure terrains. However, the problem of interference management between deployed UAV and underlaying heterogeneous networks, which guarantees the quality of service, is still a challenging task. In this paper, we address the outage probability for UAV connected users and device-to-device (D2D) receivers simultaneously operating in D2D underlaying NOMA UAV-assisted networks, and we derive its closed-form expressions, which turns out to be very accurate. We also solve the power control optimization problem via relaxing the non-convex problem into solving successive low-complexity linear programs to obtain a sub-optimal solution to the problem. The simulation results in confirm the superiority of the proposed approach in the terms of computational complexity and its compromise in the terms of sum rate.

Performance Analysis and Power Control for Multi-Antenna V2V Underlay Massive MIMO

Device-to-device (D2D) communication has been considered a promising solution for vehicle-to-vehicle (V2V) communication. In V2V underlay massive MIMO cellular networks, V2V has the potential to increase spectral efficiency (SE) by reusing cellular spectrum. However, the interference between cellular and V2V transmissions may be serious if not designed properly. In this paper, we first derive the analytical lower bounds on multi-antenna V2V and cellular SEs under both perfect and imperfect channel state information for maximum-ratio combining and zero-forcing detection. Using these lower bounds, performance analysis is given to provide more insights and guide practice. Then, we formulate a large-scale fading-based power control problem to guarantee the SE constraints of V2V pairs while maximizing the sum SE of cellular users. Moreover, the geometric programming technique is applied to solve the optimal power control problem. Numerical results show that the proposed SE-constrained power control scheme can greatly increase the reliability and achievable SE of V2V communications while the achievable sum SE is slightly degraded, compared with the fixed maximum power scheme.

An approach of robust power control for cognitive radio networks based on chance constraints

The changing and fluctuations of channel gains are inevitable in wireless communication. In this paper, a robust power control scheme for cognitive radio networks is proposed with consideration of uncertain channel gains. With the uncertainty, an optimal power control problem is formulated, which keeps the outage probability both of cognitive and primary users below the given threshold and maximizes the sum-utility of cognitive users. The chance-constraint robust approach is applied to transform the uncertain parameters into the determining setting, which is convexity of the outage probability constraints. In order to make the optimization problem solve facilely, the optimization problem is transformed to a convex problem by a suitable relaxation and the exponential transformations. The distributed power control algorithm based on Lagrange dual decomposition is proposed further. Numerical results show the convergence and effectiveness of the proposed chance-constraint robust power control algorithm. The sum of utility is improved and the energy consumption is reduced compared with some existing algorithms.

Energy Efficient Joint Power Control and User Association Optimization in Massive MIMO Enabled HetNets

Massive MIMO enabled heterogeneous cellular networks (HetNets) have a wide application prospect in improving end-to-end performance. To increase the performance of energy efficiency (EE), we formulate the joint optimization problem of power control and user association in this paper. Unfortunately, the problem is a fractional and mixed integer nonlinear programming problem (FMINLP). Nevertheless, an energy efficient joint power control and user association optimization algorithm is proposed to solve the problem. Firstly, based on the Dinkelbach’s theorem, we transform the objective function of the problem into an integral expression and propose the optimal EE iterative algorithm. Then, with the help of the approximately iterative method and Lagrange’s decomposition dual method, the problem can be transformed into a convex optimization problem and the joint optimization algorithm is proposed correspondingly. In the simulation, the performance of EE and its influence factors are analyzed and some interesting points are discussed.

A cross-layer optimization framework for congestion and power control in cognitive radio ad hoc networks under predictable contact

In this paper, we investigate the cross-layer optimization problem of congestion and power control in cognitive radio ad hoc networks (CRANETs) under predictable contact constraint. To measure the uncertainty of contact between any pair of secondary users (SUs), we construct the predictable contact model by attaining the probability distribution of contact. In particular, we propose a distributed cross-layer optimization framework achieving the joint design of hop-by-hop congestion control (HHCC) in the transport layer and per-link power control (PLPC) in the physical layer for upstream SUs. The PLPC and the HHCC problems are further formulated as two noncooperative differential game models by taking into account the utility function maximization problem and the linear differential equation constraint with regard to the aggregate power interference to primary users (PUs) and the congestion bid for a bottleneck SU. In addition, we obtain the optimal transmit power and the optimal data rate of upstream SUs by taking advantage of dynamic programming and maximum principle, respectively. The proposed framework can balance transmit power and data rate among upstream SUs while protecting active PUs from excessive interference. Finally, simulation results are presented to demonstrate the effectiveness of the proposed framework for congestion and power control by jointly optimizing the PLPC-HHCC problem simultaneously.

Discrete Power Control and Transmission Duration Allocation for Self-Backhauling Dense mmWave Cellular Networks

Wireless self-backhauling is a promising solution for dense millimeter wave (mmWave) small cell networks, the system efficiency of which, however, depends upon the balance of resources between the backhaul link and access links of each small cell. In this paper, we address the discrete power control and non-unified transmission duration allocation problem for self-backhauling mmWave cellular networks, in which each small cell is allowed to adopt individual transmission duration allocation ratio according to its own channel and load conditions. We first formulate the considered problem as a non-cooperative game ${\mathcal {G}}$ with a common utility function. We prove the feasibility and existence of the pure strategy Nash equilibrium (NE) of game $ {\mathcal {G}}$ under some mild conditions. Then, we design a centralized resource allocation algorithm based on the best response dynamic and a decentralized resource allocation algorithm (DRA) based on control-plane/user-plane split architecture and log-linear learning to obtain a feasible pure strategy NE of game ${\mathcal {G}}$ . For speeding up convergence and reducing signaling overheads, we reformulate the considered problem as a non-cooperative game ${{\mathcal {G}}'}$ with local interaction, in which only local information exchange is required. Based on DRA, we design a concurrent DRA to obtain the best feasible pure strategy NE of game ${\mathcal {G}'}$ . Furthermore, we extend the proposed algorithms to the discrete power control and unified transmission duration allocation optimization problem. Extensive simulations are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms.

Joint Uplink Base Station Association and Power Control for Small-Cell Networks With Non-Orthogonal Multiple Access

Since non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) can achieve superior spectral-efficiency and energy-efficiency, the concept of SCN using NOMA with SIC is proposed in this paper. Due to the difference in small-cell base stations’ locations, each mobile user perceives different channel gains to different small-cell base stations. Therefore, it is important to associate a mobile user with the right base station and control its transmit power for the uplink SCN using NOMA with SIC. However, the already-challenging base station association problem is further complicated by the need of transmit power control, which is an essential component to manage co-channel interference. Despite its importance, the joint base station association and power control optimization problem that maximizes the system-wide utility and at the same time minimizes the total transmit power consumption for the maximum utility has remained largely unsolved for the uplink SCN using NOMA with SIC, mainly due to its non-convex and combinatorial nature. To solve this problem, we first present a formulation transformation that captures two interactive objectives simultaneously. Then, we propose a novel algorithm to solve the equivalently transformed optimization problem based on the coalition formation game theory and the primal decomposition theory in the framework of simulated annealing. Finally, theoretical analysis and simulation results are provided to demonstrate that the proposed algorithm is guaranteed to converge to the global optimal solution in polynomial time.

Joint Mode Selection, Link Allocation and Power Control in Underlaying D2D Communication

Device-to-device (D2D) communication underlaying cellular networks can bring significate benefits for improving the performance of mobile services. However, it hinges on elaborate resource sharing scheme to coordinate interference between cellular users and D2D pairs. We formulate a joint mode selection, link allocation and power control optimization problem for D2D communication sharing uplink resources in a multi-user cellular network and consider the efficiency and the fairness simultaneously. Due to the non-convex difficulty, we propose a three-step scheme: firstly, we conduct mode selection for D2D pairs based on a minimum distance metric after an admission control and obtain some cellular candidates for them. And then, a cellular candidate will be paired to each D2D pair based on fairness. Finally, we use Lagrangian Algorithm to formulate a joint power control strategy for D2D pairs and their reused cellular users and a closed-form of solution is derived. Simulation results demonstrate that our proposed algorithms converge in a short time. Moreover, both the sum rate of D2D pairs and the energy efficiency of cellular users are improved.

Power control for packet streaming with head-of-line deadlines

We consider a mathematical model for streaming media packets (as the motivating key example) from a transmitter buffer to a receiver over a wireless link while controlling the transmitter power (hence, the packet/job processing rate). When each packet comes to the head-of-line (HOL) in the buffer, it is given a deadline D which is the maximum number of times the transmitter can attempt retransmission in order to successfully transmit the packet. If this number of transmission attempts is exhausted, the packet is ejected from the buffer and the next packet comes to the HOL. Costs are incurred in each time slot for holding packets in the buffer, expending transmitter power, and ejecting packets which exceed their deadlines. We investigate how transmission power should be chosen so as to minimize the total cost of transmitting the items in the buffer. We formulate the optimal power control problem in a dynamic programming framework and then hone in on the special case of fixed interference. For this special case, we are able to provide a precise analytic characterization of how the power control should vary with the backlog and how the power control should react to approaching deadlines. In particular, we show monotonicity results for how the transmitter should adapt power levels to the backlog and approaching deadlines. We leverage these analytic results from the special case to build a power control scheme for the general case. Monte Carlo simulations are used to evaluate the performance of the resulting power control scheme as compared to the optimal scheme. The resulting power control scheme is sub-optimal but it provides a low-complexity approximation of the optimal power control. Simulations show that our proposed schemes outperform benchmark algorithms. We also discuss applications of the model to other practical operational scenarios.

Energy-Efficient Joint Resource Allocation and Power Control for D2D Communications

In this paper, joint resource allocation and power control for energy-efficient device-to-device (D2D) communications underlaying cellular networks are investigated. The resource and power are optimized for maximization of the energy efficiency (EE) of D2D communications. Exploiting the properties of fractional programming, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. Then, an efficient iterative resource allocation and power control scheme is proposed. In each iteration, part of the constraints of the EE optimization problem are removed by exploiting the penalty function approach. We further propose a novel two-layer approach, which allows finding the optimum at each iteration by decoupling the EE optimization problem of joint resource allocation and power control into two separate steps. In the first layer, the optimal power values are obtained by solving a series of maximization problems through root finding, with or without considering the loss of cellular users' rates. In the second layer, the formulated optimization problem belongs to a classical resource-allocation problem with single allocation format, which admits a network flow formulation so that it can be solved to optimality. Simulation results demonstrate the remarkable improvements in terms of EE by using the proposed iterative resource allocation and power control scheme.

Performance Analysis of Co- and Cross-tier Device-to-Device Communication Underlaying Macro-small Cell Wireless Networks

Device-to-Device (D2D) communication underlaying macro-small cell networks, as one of the promising technologies in the era of 5G, is able to improve spectral efficiency and increase system capacity. In this paper, we model the cross- and co-tier D2D communications in two-tier macro-small cell networks. To avoid the complicated interference for cross-tier D2D, we propose a mode selection scheme with a dedicated resource sharing strategy. For co-tier D2D, we formulate a joint optimization problem of power control and resource reuse with the aim of maximizing the overall outage capacity. To solve this non-convex optimization problem, we devise a heuristic algorithm to obtain a suboptimal solution and reduce the computational complexity. System-level simulations demonstrate the effectiveness of the proposed method, which can provide enhanced system performance and guarantee the quality-of-service (QoS) of all devices in two-tier macro-small cell networks. In addition, our study reveals the high potential of introducing cross- and co-tier D2D in small cell networks: i) cross-tier D2D obtains better performance at low and medium small cell densities than co-tier D2D, and ii) co-tier D2D achieves a steady performance improvement with the increase of small cell density.