Power Allocation For Heterogeneous Networks Research Articles

Energy-efficient joint resource allocation in 5G HetNet using Multi-Agent Parameterized Deep Reinforcement learning

Small cells are a promising technique to improve the capacity and throughput of future wireless networks. However, user association and power allocation in heterogeneous networks is complicated by the dense deployment of small cells, resulting in non-convex and combinatorial problems. Conventionally, machine learning techniques are applied to the joint optimization problem, which has different action spaces. Gauging the continuous spaces to discrete spaces results in the loss of granularity due to discretization (e.g. potential power values in power allocation). Due to its hybrid action space, it is sub-optimal to solve joint user association (discrete spaces) and power allocation (continuous spaces) problems by applying traditional machine learning approaches. This work proposes a Multi-Agent Parameterized Deep Reinforcement Learning (MA-PDRL) approach to address the joint user association and power allocation problem efficiently. According to simulation results, the proposed multi-agent PDRL performs better in energy efficiency and QoS satisfaction than WMMSE, game theory, Q-learning, and DRL techniques.

A review of machine learning techniques for enhanced energy efficient 5G and 6G communications

Cellular technologies have evolved continuously from the 1st to the 5th generation (5G) to meet the exponentially growing needs of bandwidth, throughput and latency. However, the energy consumption experienced a proportional rise generation-wise when new hardware to support additional applications were incorporated. 5G, which already consumes four times more energy than 4G, is expected to bring about a significant spike in the conventional trajectory of energy consumption. 5G is therefore triggering a major concern for energy efficiency and with an even higher technical and network complexity, 6G will pause an unprecedented challenge to energy efficiency and sustainability. This paper focuses on the energy consumption at the base station and access network levels, which amount to around 80% of energy consumption in mobile networks. Machine learning techniques can be employed in several ways to improve the energy efficiency in these components. In this paper, efficient base station deployment strategies and adaptive operational modes as well as access network technologies such as massive MIMO and millimeter waves, which employ machine learning to enhance the energy efficiency, have been reviewed in depth. Since existing research works have focused mostly on a single optimization strategy at either the base station or access network level, this paper proposes a framework, which combines efficient base station deployment methods and machine learning-based switching between different operation modes based on traffic load. Moreover, an adaptive beamforming methodology through identification of hotspots and user association, sub-channel and power allocation in heterogeneous networks is discussed in detail.

Distributed Deep Reinforcement Learning-Based Spectrum and Power Allocation for Heterogeneous Networks

This paper investigates the problem of distributed resource management in two-tier heterogeneous networks, where each cell selects its joint device association, spectrum allocation, and power allocation strategy based only on locally-observed information without any central controller. As the optimization problem with devices’ quality-of-service (QoS) constraints is non-convex and NP-hard, we model it as a Markov decision process (MDP). Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

Distributed Beamforming and Power Allocation for Heterogeneous Networks with MISO Interference Channel.

To address the limitations of centralized resource allocation, i.e., high computational complexity and signaling overhead, a distributed beamforming and power allocation strategy is proposed for heterogeneous networks with multiple-input-single-output (MISO) interference channels. In the proposed scheme, each secondary user transceiver pair (SU TP) determines the beamforming vector and transmits power to maximize its own spectral efficiency (SE) while keeping the interference to the primary user below a predetermined threshold, and such resource management for each SU TP is updated iteratively without any information sharing until the strategies for all SU TPs converge. The simulation confirms that the proposed scheme can achieve a performance comparable to that of a centralized approach with a much lower computation time, e.g., less than 5% degradation in SE while improving computation time by more than 10 times.

Non-Cooperative Game Based Power Allocation for Energy and Spectrum Efficient Downlink NOMA HetNets

In this paper, the tradeoff between spectrum efficiency (SE) and energy efficiency (EE) is investigated in terms of interference management and power allocation for heterogeneous networks (HetNets) with non-orthogonal multiple access (NOMA). The EE and SE tradeoff is modeled as a multi-objective problem (MOP) under the maximum power and quality of service (QoS) constraints, which is non-convex. The MOP is relaxed into a convex single objective problem (SOP) by adopting a weighted sum strategy with the hypograph transformation. The SOP is solved in two steps. In the first step, we propose a power allocation technique based on non-cooperative (NC) game for EE and SE in NOMA HetNets. In the proposed NC game, the macro base station (MBS) and the small BSs (SBSs) compete with an equal priority in order to optimize their transmit powers towards maximizing the weighted sum of SE and EE. In the second step, a closed-form formula is proposed to control the power allocated to users while taking into account both QoS constraint and successive interference cancellation (SIC) condition. From simulations, the proposed technique can, in some dedicated settings, considerably improve the tradeoff between EE and SE over conventional techniques.

A fully distributed learning algorithm for power allocation in heterogeneous networks

In this work, we present a fully distributed Learning algorithm for power allocation in HetNets, referred to as the FLAPH, that reaches the global optimum given by the total social welfare. Using a mix of macro and femto base stations, we discuss opportunities to maximize users global throughput. We prove the convergence of the algorithm and compare its performance with the well-established Gibbs and Max-logit algorithms which ensure convergence to the global optimum. Algorithms are compared in terms of computational complexity, memory space, and time convergence.

A QEE-Oriented Fair Power Allocation for Two-tier Heterogeneous Networks

In future wireless network, user experience and energy efficiency will play more and more important roles in the communication systems compared to their roles at present. Quality of experience (QoE) and Energy Efficiency (EE) become the widely used metrics. In this paper, we study a combinatorial problem of QoE and EE and investigate a fair power allocation in heterogeneous networks. We first design a new metric, QoE-aware EE (QEE) to reflect the relationship of QoE and energy. Then, the concept of Utopia QEE is introduced, which is defined as the achievable maximum QEE in ideal conditions, for each user. Finally, we transform the power allocation process to an optimization of ratio of QEE and Utopia QEE and use invasive weed optimization (IWO) algorithm to solve the optimization problem. Numerical simulation results indicate that the proposed algorithm can get converged and efficiently improve the system energy efficiency and the QoE for each user.

A Novel Approach for QoS-Aware Joint User Association, Resource Block and Discrete Power Allocation in HetNets

We consider joint optimization of user-to-base-station (BS) association, and time-frequency resource block (RB) and power allocation in heterogeneous networks (HetNets). The objective is to develop a design: 1) that maximizes the number of users accommodated in the network while satisfying their quality of service demands and 2) that minimizes usage of the resources required to meet these demands. We investigate two novel instances of HetNets with opportunistic RB-reuse. In the first instance, user-to-BS associations and power allocations can be time-shared, and the RBs can be reused during the signaling interval. For this instance, it is shown that the design problem can be approximated by a problem that yields tight convex upper and lower bounds on the objective. In contrast, the second instance represents a case in which the RBs can be reused, but the user-to-BS associations and power allocations are not time-shared, and hence, fixed throughout the signaling interval. The latter case gives rise to a combinatorial optimization problem, which we provide an approximate solution for by using a polynomial-complexity two-phase approach based on semidefinite relaxation with randomization.

Joint Sub-Channel Assignment and Power Allocation in Heterogeneous Networks: An Efficient Optimization Method

This letter is concerned with joint sub-channel and power allocation in a heterogeneous network. We consider the uplink of a co-channel framework where a macrocell shares the orthogonal frequency division multiple access (OFDMA) sub-channels with a number of small cells (SCs). We maximize the weighted sum-rate of the OFDMA SC network, while a target constraint on the cross-tier interference and a minimum data rate requirement for the high data rate SC users are satisfied. The optimization problem is a highly non-convex mixed integer non-linear problem. We first reformulate the problem into a more mathematically tractable form. Then, we relax the integer variables by adding a penalty term to the objective function. Finally, we rewrite the objective function as the difference of two concave (D.C.) functions and solve this problem using the successive convex approximation method. We show that our method outperforms the state-of-the-art schemes.

Dormancy Mechanism Based Power Allocation in Heterogeneous Networks: A Stackelberg Game Approach

In two-tier heterogeneous networks (HetNets) that consist of a central macrocell underlaid with spectrum-sharing dense smallcells, one primary concern is the cross-tier interference limits the network capacity. On the other hand, dormancy mechanism of smallcells is an emerging technique that can improve energy efficiency and mitigate interference. Therefore in this paper, we propose a power optimization oriented Stackelberg game considering dormancy mechanism for the downlink of two-tier HetNets. First, the smallcell dormancy mechanism is introduced to control the smallcells’ operation. Then the Stackelberg game framework is developed. A sub game based on a potential theory is further formulated to optimize the transmit power of the active smallcells, which is demonstrated to converge to a pure and unique Nash equilibrium. In this sub game, the interference pricing mechanism is employed to price the cross-tier interference caused by smallcells and hence restricts the utility function of smallcells. Simulation results show that the proposed Stackelberg game based on dormancy mechanism with interference pricing can improve the smallcell network throughput and meanwhile reduce the average transmit power of smallcells.

Resource allocation scheme based on game theory in heterogeneous networks

As an effective solution for indoor coverage and service offloading from the conventional cellular networks, femtocells have attracted a lot of attention in recent years. This study investigates the resource block (RB) and power allocation in heterogeneous networks (HetNets). Specifically, the concern here is to maximize the signal to interference-plus-noise ratio (SINR) of macrocell and energy efficiency of femtocell while providing the finite interference. In this paper, the system model is divided to two layers, in which the macro base station and clusters constitute the first layer network; femtocells in cluster make up the second layer network. Because of the different model structures, different game theories are used in different layers. Stackelberg game is used in the first layer, and non-cooperation game is used in the second layer. Meanwhile RB and power levels stand for the actions that are associated with each player in the game. The problem of resource allocation is formulated as a mixed integer programming problem. In order to minimize the complexity of the proposed algorithm, the resource allocation task is decomposed into two sub problems: a RB allocation and a power allocation. The result is compared with the traditional methods, the analysis illustrates the proposed algorithm has a better performance regarding SINR and energy efficiency of the heterogeneous networks.

Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints

This work proposes a distributed power allocation scheme for maximizing energy efficiency in the uplink of OFDMA-based HetNets where a macro-tier is augmented with small cell access points. Each user equipment (UE) in the network is modeled as a rational agent that engages in a non-cooperative game and allocates its available transmit power over the set of assigned subcarriers to maximize its individual utility (defined as the user's throughput per Watt of transmit power) subject to a target rate requirement. In this framework, the relevant solution concept is that of Debreu equilibrium, a generalization of the concept of Nash equilibrium. Using techniques from fractional programming, we provide a characterization of equilibrial power allocation profiles. In particular, Debreu equilibria are found to be the fixed points of a water-filling best response operator whose water level is a function of rate constraints and circuit power. Moreover, we also describe a set of sufficient conditions for the existence and uniqueness of Debreu equilibria exploiting the contraction properties of the best response operator. This analysis provides the necessary tools to derive a power allocation scheme that steers the network to equilibrium in an iterative and distributed manner without the need for any centralized processing. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters.

Optimal power allocation for heterogeneous network capacity maximization

We investigate the optimal joint power allocation in Heterogeneous Networks (HetNets) to maximise its capacity. Considering frequency reuse in the network, we study two power-constraint cases, i.e., per-cell power constraint case and per-tier power constraint case. We formulate the capacity maximization problem by allowing each subcarrier of Marco eNodeB (MeNB) to be shared by users from multiple Picos. We mathematically demonstrate that the optimal power allocation in the per-cell power constraint case has a remarkably simple nature: each Pico transmits to its user with maximum power, while MeNB either selects only one user to jointly transmit with maximum power or does not transmit to any user. In the per-tier power constraint case, the difference is that the power allocation between two Picos takes the form of water-filling. Numerical results verify that our proposed schemes outperform the conventional interference coordination schemes.

Power allocation in heterogeneous networks: Limited spectrum-sensing ability and combined protection

In this paper, we investigate the problem of power allocation in a heterogeneous network that is composed of a pair of cognitive users (CUs) and an infrastructure-based primary network. Since CUs have only limited effective spectrum-sensing ability and primary users (PUs) are not active all the time in all locations and licensed bands, we set up a new multi-area model to characterize the heterogeneous network. A novel combined interference-avoidance policy corresponding to different PU-appearance situations is introduced to protect the primary network from unacceptable disturbance and to increase the spectrum secondary-reuse efficiency. We use dual decomposition to transform the original power allocation problem into a two-layer optimization problem. We propose a low-complexity joint power-optimizing method to maximize the transmission rate between CUs, taking into account both the individual power-transmission constraints and the combined interference power constraint of the PUs. Numerical results show that for various values of the system parameters, the proposed joint optimization method with combined PU protection is significantly better than the opportunistic spectrum access mode and other heuristic approaches.