Fairness-Driven Energy Efficient Resource Allocation in Uplink MIMO Enabled HetNets

In this paper, downlink joint user association, codebook assignment and power allocation for a Sparse Code Multiple Access (SCMA)‐based in Cloud Radio Access Network (C‐RAN) are investigated. The main aim of this paper is to accomplish a resource allocation to achieve the maximum total sum rate subject to SCMA limitation, total available power in Radio Remote Head (RRH), fronthaul constraint, users association, user power constraint, and Quality of Service requirements for each user with low complexity. To solve the recommended problem an iterative algorithm considering Successive Convex Approximation is utilized. The main problem is separated into two subproblems of joint user association and codebook assignment and power allocation. By defining a new binary variable for user association and codebook assignment and a new constraint for the user association, the joint user association and codebook assignment subproblem becomes an Integer Linear Programming. Then user association and codebook assignment subproblem solved with the help of the CVX toolbox and MOSEK solver. Furthermore, to solve the power allocation subproblem, Successive Convex Approximation with Low Complexity is applied. Then the power allocation subproblem solved by subgradient method for the Lagrange dual problem. In the simulation results, the effectiveness of the recommended optimization algorithm and resource allocation method are shown and compared to Non‐Orthogonal Multiple Access and Orthogonal Frequency Division Multiple Access.

With the proliferation of smart devices, machine-to-machine communication, and wearable, data and traffic is increasing enormously. Ultra-densification has been considered a promising technique to achieve substantial advantages in capacity, data rate, and coverage. However, the extensive deployment of devices to support huge data traffic results in high interference and energy issues. This is due to the unplanned deployment of small cells with varying power and coverage. Due to small coverage, users will frequently be changing associations with the base station. In this regard, user association plays a critical role in interference management, load balancing, energy consumption, and spectral efficiency. This paper proposes an energy-efficient load-aware user association technique, which assists the user equipment in selecting an optimal base station. The proposed technique leads to a stable user association considering load (traffic) while improving energy efficiency. The proposed user association operation ends when all user equipment gets associated with base stations by achieving the equilibrium state. Simulation results show that the proposed user association scheme outperforms significantly in load balancing, sum rate, and energy efficiency compared to max-SINR based user association.

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.

This paper studies the problem of energy efficiency (EE) maximization via user association, power, and backhaul (BH) flow control in the downlink of millimeter wave BH heterogeneous networks. This problem is mathematically formulated as a mixed-integer non-linear program, which is non-convex. To get a tractable solution, the initial problem is separated into two sub-problems and optimized sequentially. The first is a joint user association and power control sub-problem for the access network (AN) (AN sub-problem). The second is a joint flow and power control sub-problem for the BH network (BH sub-problem). While the BH sub-problem is a convex optimization problem and hence can be efficiently solved, the AN sub-problem assumes the form of a generalized assignment problem, which is known to be NP-hard. To that end, we utilize Lagrangian decomposition to propose two polynomial time solution techniques that obtain a high-quality solution for the AN sub-problem. The first, referred to as Technique A, uses dynamic programming, the subgradient method, and a heuristic. The second, named Technique B, uses the multiplier adjustment method, the sorting algorithm, and a heuristic. Simulation results are used to demonstrate the effectiveness of the proposed energy efficient user association, power, and BH flow control algorithms as compared with benchmark user association schemes that incorporate the BH sub-problem algorithm, in terms of the total AN power, BH power, and overall network (AN plus BH) EE. The computational complexity and practical implementation of the proposed algorithms are discussed.

In this paper, we propose an energy efficient user association scheme for uplink heterogeneous networks with machine-to-machine (M2M) and human-to-human (H2H) coexistence. A group based random access protocol is designed for massive number of machine-type-communications (MTC) user equipments' (UEs) transmissions. A user association problem for UEs' energy efficiency maximization is formulated considering the HTC UEs' quality of service (QoS) guarantees and load balance among multiple BSs, simultaneously. A distributed iterative algorithm is developed to solve the optimization problem. In addition, the convergence of the proposed algorithm is proved. Simulation results show that our proposed scheme outperforms other schemes in terms of energy efficiency and QoS guarantees.

Long Term Evolution-Advanced (LTE-Advanced) and other advanced technologies like IEEE802.16e and IEEE802.11g base their physical layer transmission on multiple input multiple output (MIMO). The concept of multiple antennas at both receiver and transmitter ends is attributed with advantages of spatial multiplexing and diversity, however, the Implementation on small size UEs has its own limitations. Hence a concept of virtual multiple input multiple output (VMIMO) has evolved to accommodate higher throughput for LTE-Advanced system. This paper formulates a scheme that provides maximum uplink throughput in LTE-ADVANCED environment keeping minimum level of fairness within grouped user equipment (UEs) in VMIMO. Groups/Pairs are formed from single antenna UEs to obtain multiplexing gain. Round Robin (RR), Proportional Fair (PF) and Random Pairing (RP) are Conventional pairing scheme. Fairness is prime consideration in RR and PF but RP ignores both fairness and throughput. In this paper, a Modified Random Pair (M-RP) has been introduced that maximizes throughput 2.10%, 2.07% & 2.02% in Lower group VMIMO and .96%, .96% & .95% Higher group VMIMO from RP, RR and PF respectively while keeping minimum level of fairness in the legal UEs within Femtocell scenario.

Device-to-Device (D2D) communication underlaying cellular networks is expected to bring significant benefits for resource utilization and cellular coverage. However, the resource allocation and power control needs elaborate coordination, otherwise it may cause severe interference between D2D user equipment (DUE) and cellular user equipment (CUE). In this paper, we study the joint radio resource allocation and power control problem with energy efficiency as our optimization goal. To improve the system energy efficiency with low computational complexity, we propose an iterative combinatorial auction algorithm with flexible power control method, where the CUEs are considered as bidders, DUEs as goods and the cellular network plays a role as the auctioneer controlling the auction process. We also analyze the properties of the proposed algorithm and present numerical results to verify that our algorithm can significantly improve energy efficiency.

Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE.

Multistream carrier aggregation (MSCA) is a promising technique to boost data rates by enabling users to connect with multiple base stations (BSs) simultaneously. In this paper, we investigate joint user association and resource allocation for MSCA systems to achieve energy efficiency (EE) balance among different BSs. We aim at maximizing the weighted summation of EE values for different BSs, which is modeled as a nonconvex combinatorial sum-of-ratios optimization problem. To develop an upper bound algorithm, we first relax the combinatorial variables and then transform the problem into a series of convex optimization problems by the successive convex approximation (SCA) method. We also propose a low-complexity suboptimal algorithm, which divides the original optimization problem into two steps: user association and channel allocation, as well as power allocation. Numerical results show that the proposed algorithms can achieve flexible EE tradeoff among BSs, and the EE can be significantly improved with MSCA.

5G and the next generation networks are expected to support diverse quality of service (QoS) requirements while ensuring network energy efficiency in the face of increasing mobile data traffic. However, wireless networks are resource-constrained, and ensuring QoS guarantees and maintaining energy efficiency is a challenging task. Rapid fluctuation of signal quality due to the mobility of the user equipment (UE) makes the task more complicated. Nevertheless, user mobility is not entirely random (e.g., UE with vehicle speed traveling along a highway) and results in channel gain that significantly varies with time. Since mobility factors (speed, location, direction of movement) and received signal strength are correlated, user mobility can be exploited to enhance resource allocation decisions and thus improve both UE QoS and network energy efficiency. In this paper, we propose a deep reinforcement learning (DRL)-based mobility and QoS-aware downlink scheduling algorithm that takes advantage of user mobility information to minimize network energy consumption. Using reference signal received power (RSRP) measurement reports, the proposed algorithm incorporates mobility awareness into the scheduling rule to decide whether to serve UE in the current time slot or future slots, within the packet delay budget. By jointly considering packet remaining due-time and mobility information, the algorithm schedules transmissions of packets at lower power, improving energy efficiency while meeting QoS requirements. Simulation results demonstrate that the proposed approach reduces energy consumption by 18.2% and improves QoS satisfaction compared to existing methods.

10 - Improving energy efficiency in next-generation IoT networks with beam adjustment technique

User association is a crucial factor that affects the performance of wireless networks. In current cellular networks, user association is typically coupled, which means an user equipment (UE) must associate with the same base station (BS) in uplink (UL) and downlink (DL). For single-tier wireless networks, such mechanism is simple and effective. However, in heterogeneous ultra-dense networks (UDNs), there are distinct differences in transmission power, data traffic and channel quality etc., for which coupled association could restrict the performance of system. To cope with it, the concept of decoupled UL-DL (DUDe) association has been introduced recently, which enables a UE to associate with different BSs in UL and DL. In this paper, we investigate decoupled UL-DL user association in UDNs. Considering the existence of asymmetric information (i.e., channel gains and intercell interferences), which can be seen as the private information for UE, we propose a contract-theoretic user association approach. Particularly, we model the decoupled association process as a monopoly labor market, where BSs act as employers and offer contracts to employees (i.e., UEs). The contract items cover the available associated bandwidths, transmitted powers and corresponding prices. Then BS broadcasts these drafted contract information, and UE selects to sign the optimal contract by considering her own demands. Numerical results show significant superiorities of DUDe than coupled UL-DL association in perspective of nodes utilities and social surplus, and compared with the existing user association methods, contract-theoretic approach shows a certain improvement in performance.

5G massive MIMO deployments up to 64T64R (64 Transmit and 64 Receive) antenna arrays are gaining traction, in part due to the smaller size of such arrays suitable for mid-band spectrum (2-5 GHz). A scheduling algorithm that can aggressively pair many UEs (User Equipment) on the same time-frequency resources is essential to fully derive the benefits of such an array. In this paper, an uplink scheduler design is proposed based on RME (Recursive Maximal Expansion) that ensures contiguous resource allocation and power constraint for each UE, while maximizing MU (Multi-User) MIMO (Multiple-Input Multiple-Output) gains by selecting and pairing UE groups based on the sum-PF (Proportional Fair) metric. We show, using realistic system level simulations, that up to 100% average cell-throughput gains can be achieved by MU-MIMO over SU (Single-User) MIMO in dense urban micro cell deployment scenarios presenting an attractive proposition for massive MIMO array deployments. It is observed that power control parameters play a critical role and should be optimized considering scheduler behavior, especially for cell-edge throughput improvement.

A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Thenwe solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.

Heterogeneous networks (HetNets) are regarded as a key technology to increase system capacity and improve spectrum efficiency through spatial reuse. Along with the introduction of Hetnets, many user association schemes based on different metrics, such as QoS, fairness, spectrum efficiency and so on, are proposed to solve the user association problem. In this paper, taking load balancing and user equipment (UE) energy efficiency into consideration, a centralized user association scheme is proposed via Lagrange multiplier. The presented scheme is envisioned to acquire a better load balancing and UE energy efficiency. The effectiveness of our scheme is validated by numerical results.