Joint power and spectrum allocation for D2D communication overlaying cellular networks

This paper investigates the application of multi-agent reinforcement learning (MARL) algorithm to solve the joint spectrum and power allocation problem (JSPAP) in wireless network. The objective of JSPAP is to optimize the subband selection and transmit power levels for links, with the aim of maximizing the sum-rate utility function. To address the JSPAP with discrete subband selection and continuous power allocation, most existing algorithms rely on a centralized optimizer and the instantaneous global channel state information, which can be challenging to implement in large wireless networks with time-varying subbands. To conquer such limitation, a two-stage MARL algorithm is proposed, which comprises a top layer network for selecting subbands across all links and a bottom layer network for determining the transmit power levels for all transmitters. By utilizing the value decomposition technique in the top layer network, the links can cooperatively select transmission subbands, effectively resolving non-stationarity issues in wireless network. Additionally, in the bottom layer network of the proposed two-stage MARL algorithm, each transmitter selects the transmit power level based solely on the local information, thereby effectively reducing computational burden. Empirical experiments demonstrate the effectiveness of the proposed two-stage MARL algorithm by comparison with the state-of-the-art RL algorithms and fractional programming algorithms.

In this paper, we consider cooperative D2D communications in cellular networks. More precisely, a cellular user leases part of its spectrum to facilitate the D2D communication with a goal of improving the energy efficiency of a D2D pair. However the D2D pair is untrusted to the cellular user, such resource sharing may result in the information of this cellular user unsecured. In order to motivate the cellular user’s generosity, this D2D pair needs to help the cellular user maintain a target secrecy rate. To address this issue, we formulate a joint spectrum and power allocation problem to maximize the energy efficiency of the D2D communication while guaranteeing the physical layer security of the cellular user. Then, a theorem is proved to indicate the best resource allocation strategy, and accordingly, an algorithm is proposed to find the best solution to this resource allocation problem. Numerical results are finally presented to verify the validity and effectiveness of the proposed algorithm.

In heterogeneous cellular networks (HCNs), the growing demand of small cell data traffic puts great pressure on the capacity limited backhaul links. Resource allocation is an efficient approach to mitigate the severe interference and improve the network performance in HCNs. However, differentiated backhaul capacity constraints introduce the inherent nonconvexity to the resource optimization problem, which hinders the development of the optimal solutions. In this article, we investigate the joint resource block (RB) and power allocation problem under two different backhaul capacity limitations. First, we consider the small cell backhaul limitation scenario where the backhaul capacity constraint is imposed on each small cell. Then, we extend the resource optimization to the macrocell base station backhaul limitation scenario which considers the overall backhaul capacity constraint on the whole small cell network. Besides, the quality of service (QoS) requirements of users and the cross‐tier interference mitigation are also considered. This resource allocation problem is formulated as a nonconvex mixed‐integer nonlinear programming (MINLP) problem. By variable substitution and constraints relaxation, we convert the original optimization problem into a convex one and obtain the optimal solutions. Accordingly, joint RB and power allocation algorithms are proposed. Numerical results verify the effectiveness of the proposed algorithms and evaluate the impact of different types of backhaul capacity limitations on system performance.

A novel cross-layer joint relay selection and power allocation (CL-JRSPA) scheme over physical layer and data-link layer is proposed for cooperative relaying system in this paper. Our goal is finding the optimal relay selection and power allocation scheme to maximize system achievable rate when satisfying total transmit power constraint in physical layer and statistical delay quality-of-service (QoS) demand in data-link layer. Using the concept of effective capacity (EC), our goal can be formulated into an optimal joint relay selection and power allocation (JRSPA) problem to maximize the EC when satisfying total transmit power limitation. We first solving optimal power allocation (PA) problem with Lagrange multiplier approach, and then solving optimal relay selection (RS) problem. Simulation results demonstrate that CL-JRSPA scheme gets larger EC than other schemes when satisfying delay QoS demand. In addition, the proposed CL-JRSPA scheme achieves the maximal EC when relay located approximately halfway between source and destination, and EC becomes smaller when the QoS exponent becomes larger.

Machine-to-Machine (M2M) communications have received considerable attentions in recent years. By allowing machine type communications devices (MTCDs) to communicate with each other or with infrastructures under reduced human intervention, M2M communications are expected to offer a large variety of MTCD-related applications. While the vision of M2M communications is quite promising, the diverse quality of service (QoS) requirements and the complicated network architecture pose challenges and difficulties to resource allocation, cell selection and random access schemes, etc. In this paper, we take into account the impacts of random access on the transmission performance of the MTCDs and examine the energy efficiency of each MTCD. Applying the concept of utility function, we define network utility function and formulate the problem of joint cell selection and power allocation as a network utility function maximization problem. By applying iterative method and Lagrange dual method, the formulated optimization problem is solved and the optimal joint cell selection and power allocation strategy can be obtained. Simulation results demonstrate the effectiveness of the proposed algorithm.

The explosive demands for mobile broadband service bring a major challenge to 5G wireless networks. Device-to-device communication, adopting side links for user-direct communication, is regarded as a main technical source for offloading large volume of mobile traffic from cellular base station. This article investigates the joint power and subcarrier allocation scheme for device-to-device communication in 5G time division duplex systems. In time division duplex system, instead of utilizing an exclusive portion of the precious cellular spectrum, device-to-device pairs reuse the subcarriers occupied by cellular users, thus producing harmful interference to cellular users in both uplink and downlink communication, and strongly limiting the spectrum efficiency of the system. To this end, we focus on the maximization of device-to-device throughput while guaranteeing both uplink and downlink channel quality of service of cellular users as well as device-to-device pairs. The problem is formulated as a mixed integer non-linear programming (MINLP) problem. To make it tractable, we separate the original MINLP problem into two sub problems: power allocation and sub-carrier reusing. The former is to develop optimal power allocation for each device-to-device pair and each cellular user, with the constraints of maximum power and quality of service. It is solved by geometric programming technique in convex optimization method. The latter is derived as a one-to-many matching problem for scheduling multiple subcarriers occupied by cellulars to device-to-device pairs. It is solved by Hungarian method. Simulation results show that the proposed scheme significantly improves system capacity of the device-to-device underlay network, with quality of service of both device-to-device users and cellular users guaranteed.

Network localization is a key feature in many wireless services and applications. In typical rang-based localization techniques, “agents” try to perform position estimation through ranging with respect to “anchors” with known positions. Based on the definition of squared positional error bound (SPEB), the localization accuracy can be determined by the transmit power, carrier frequency and signal bandwidth, etc. This paper analyzes the joint power and spectrum allocation (JPSA) optimization problems in resource restricted wireless localization systems. We first formulate both interference-free and interference-limited JPSA problems. Since both problems are non-convex, we then develop two approximation algorithms based on single condensation and Taylor linearization. Both algorithms are able to find solutions close the global optimum. Numeric results validate our analysis, and show that, we are able to find optimal resource deployment in wireless localization networks based on the proposed frameworks.

Device-to-Device (D2D) communication is envisioned as an energy-efficient technology in the (5G) cellular standard. This paper addresses the channel and power allocation for heterogeneous cellular network-supported D2D during downlink transmission. We propose an energy-efficient scheme in terms of a joint resource block (RB) and power allocation. The energy efficiency of D2D (EE-D2D) is maximized without jeopardizing the quality of service (QoS) requirements of the other tier users. The optimization scheme decomposes into two subproblems. First, the Sequential Max Search (SMS) resource block allocation algorithm is applied to D2D users. Second, a genetic optimization approach (GA) is used to optimize the power of the D2D transmitter and base stations. Through simulation, we evaluate the proposed scheme (SMS-GA) under different QoS requirements. The SMS-GA scheme achieved a higher energy efficiency of D2D as compared to the max power scheme (SMS-Max).

Massive machine-type communications (mMTC) requires a vast number of machine-type communication devices (MTCDs) to communicate in the limited band and under limited energy budget, simultaneously. Therefore, in this paper, we make a use of user equipments (UEs) to manage MTCDs, and investigate the energy consumption minimization of MTCDs and UEs problem for mMTC with Pattern Division Multiple Access (PDMA) under quality of service (QoS) requirement by PDMA pattern assignment and power allocation. To solve this non-convex problem, we decouple PDMA pattern assignment and power allocation and firstly obtain the optimal PDMA pattern assignment. As the PDMA pattern matrix confirmed, we propose a reversed water-filling algorithm (RWFA) to obtain the optimal power allocation. The simulation results prove that the energy consumption is reduced through our proposed joint PDMA pattern assignment and power allocation scheme.

In this paper, the problem of joint task, power, and spectrum allocation is studied for a mobile edge computing (MEC) based small cell network (SCN). In the considered network, each small base station (SBS) that is connected with an MEC server provides commutation service to users while each MEC server provides computation service to users, who will request various types of computational tasks including: collaboration computational task performed at both the user and the MEC sever, edge computational task processed at the MEC server, and local computational task implemented by user itself. As the data size of each computational task requested by each user varies, one must reconsider resource (transmit power and subcarrier) allocation and task partition to effectively service the users. We formulate this problem as an optimization problem aiming to minimize the transmission and computational delay. To solve this problem, a multiple stack Q-learning method is proposed. The proposed method can use multiple stacks to record the wireless environment and users' information so as to avoid repeatedly learning the same information and, thus improving the learning efficiency and speeding up the convergence. Simulation results show that the proposed resource allocation scheme provides 79\% and 63\% gains in terms of sum delay compared to cases that fully computed at user and fully computed at the MEC server, respectively.

This paper studies the case of an overlay cognitive radio network where the primary user leases spectral resources to the secondary user in exchange for cooperation, considering that both type of users have specific quality of service requirements. We investigate the problem of joint power and time allocation for the secondary access during the cooperative phase, with a view to optimizing the effective capacity of the primary user given an average energy constraint for the secondary user. Afterwards, the optimal power allocation of the secondary user for its own transmission phase is investigated in order to maximize the effective capacity of the secondary link. The proposed joint power and time allocation mechanism is compared with an optimal time/constant power allocation scheme and a less sophisticated baseline allocation scheme, i.e. power allocation under constant time and its superiority is proven for various network parameters. The reference model of one primary---one secondary user is extended to a general multi user cognitive radio network through the proposed pairing mechanism based on matching theory. Particularly, considering the remarks of the reference scenario, we propose two different matching schemes (with/without consideration of primary users' quality of service requirements) and we confirm their superiority compared to other matching mechanisms.

Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI) performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI) threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance.

We consider two-way amplify-and-forward relaying in a multichannel system with two end nodes and a single relay, using a two-slot multi-access broadcast (MABC) relaying strategy. We investigate the problem of joint subchannel pairing and power allocation to maximize the achievable sum-rate in the network, under an individual power budgets at each node. To solve this challenging joint optimization problem, an iterative approach is proposed to decompose the problem into pairing optimization and joint power allocation optimization, and solve them iteratively. For the joint power allocation at the relay and the two end nodes, we propose another iterative optimization procedure to optimize the power at the two end nodes and at the relay iteratively. By using different forms of optimization parameters, the sum-rate maximization problem turns out to be convex and the optimal solutions can be obtained for each subproblem. The simulation performance demonstrates the gain of joint optimization approach over other pairing-only or power-allocation-only optimization approaches.

Joint power control and spectrum allocation for cognitive radio networks via pricing

Integration of distributed energy resources (DERs) into microgrid makes the power supply more reliable and reduces the cost. However, the connection of a large number of DERs among the load on middle-voltage/low-voltage feeders can result in severe voltage regulation problems. These challenges motivate the application of wireless sensor networks into microgrid. In this paper, several sensor selection schemes are heuristically proposed to improve the voltage measurement performance, prolong the sensor network lifetime, and guarantee the real-time communication between the distributed sensors and the intelligent control center. First, aiming to accurately monitor the real-time voltage level, we propose an opportunistic sensor selection scheme under equal power allocation and investigate the asymptotic behaviors of the voltage measurement performance. Furthermore, we address the sensor selection scheme under optimal power allocation and derive a reminiscent of “water-filling” solution. The proposed sensor selection schemes are applied and verified in the context of voltage regulation. In addition, we present the studies on the tradeoff between the voltage measurement and the sensor power consumption. Finally, we explore the joint power and spectrum allocation schemes to maximize the transmission rate between the sensors and the control center based on sensor selection. The theoretical analysis and proof are instrumental to the future wireless network design in microgrid.