Uplink Transmission Power Research Articles

Fractional Programming-Based Uplink Transmission Power Allocation for User-Centric Cell-Free Massive MIMO Systems

Fractional Programming-Based Uplink Transmission Power Allocation for User-Centric Cell-Free Massive MIMO Systems

Hybrid Multiple Access for Network Slicing Aware Mobile Edge Computing

Meeting huge traffic demand with resource constraints imposes a significant challenge for future generation wireless networks. In this work, we propose to utilize limited resources in a dense mobile edge computing (MEC) network to compute user equipment (UE) tasks through a novel hybrid multiple access (HYMA) scheme that employs both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). The main purpose of using HYMA is to reduce the co-channel interference incurred in NOMA by selectively deploying OMA while maintaining the required signal-to-interference ratio. We adopt partial offloading of computing tasks in the MEC network. We also employ network slicing to efficiently utilize the resources of the MEC network to meet different types of application requirements. We prioritize NOMA to increase spectral efficiency as well as energy efficiency. We first formulate a mixed integer nonlinear programming (MINLP) optimization problem to minimize the total energy consumption for both local computing and wireless transmission of the MEC network and propose an algorithm consisting of three parts (UE association, computing resource allocation, and wireless resource and uplink transmission power allocation) to solve the MINLP problem with less computational complexity. We demonstrate the viability of our solution via extensive simulations.

Joint Uplink Power Control, Downlink Beamforming, and Mode Selection for Secrecy Cell-Free Massive MIMO With Network-Assisted Full Duplexing

In this article, we investigate a secrecy cell-free massive multiple-input multiple-output system with network-assisted full duplexing for both noncolluding eavesdropper (Eve) and colluding Eve cases, where the access points (APs) are designed to serve downlink users and uplink users simultaneously and flexibly in the presence of Eves. To confirm the secrecy transmission, we consider the problem of jointly optimizing duplex mode selection and secrecy transceivers to maximize the overall secrecy spectral efficiency, where the information signals at APs are injected with artificial noise (AN) to prevent interception of information by Eves. Considering that downlink secure beamforming, uplink transmission power, and uplink receivers are tightly coupled in both the objective function and the constraints, we propose a two-loop strategy to solve the optimization problem. In particular, we propose a quantum-inspired tabu search algorithm to find a better duplex mode in the outer loop for each iteration. Then, for the inner loop, we propose a successive convex approximation based iterative algorithm to optimize the transceivers and AN by fixing the duplex mode selection vectors obtained from the outer loop. To address the proposed inner loop design subproblem, we resolve the subproblem by a series of nonconvex-convex approximate methods, such as the equivalent transformation and the iterative concave-convex procedure. The simulation results show that the proposed solution can achieve a higher secrecy spectral efficiency gain than the fixed-mode duplex scheme and simple greedy duplex mode selection schemes and achieve secrecy spectral efficiency performance similar to that of the optimal exhaustive search solution for both noncolluding Eve and colluding Eve cases.

Joint multiple resource allocation for offloading cost minimization in IRS-assisted MEC networks with NOMA

Effective resource allocation can exploit the advantage of intelligent reflective surface (IRS) assisted mobile edge computing (MEC) fully. However, it is challenging to balance the limited energy of MTs and the strict delay requirement of their tasks. In this paper, in order to tackle the challenge, we jointly optimize the offloading delay and energy consumption of mobile terminals (MTs) to realize the delay-energy tradeoff in an IRS-assisted MEC network, in which non-orthogonal multiple access (NOMA) and multiantenna are applied to improve spectral efficiency. To achieve the optimal delay-energy tradeoff, an offloading cost minimization model is proposed, in which the edge computing resource allocation, signal detecting vector, uplink transmission power, and IRS phase shift coefficient are needed to be jointly optimized. The optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension variables. To solve the intractable problem, we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable types. The computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource allocation. Further, the wireless transmission subproblem is solved iteratively through decoupling the residual variables. In each iteration, the closed-form solution of residual variables is obtained through different successive convex approximation (SCA) methods. We verify the proposed algorithm can converge to an optimum with polynomial complexity. Simulation results indicate the proposed method achieves average saved costs of 65.64%, 11.24%, and 9.49% over three benchmark methods respectively.

Joint task offloading and resource allocation in mobile edge computing with energy harvesting

Mobile edge computing (MEC) is considered to be a promising technique to enhance the computation capability and reduce the energy consumption of smart mobile devices (SMDs) in the sixth-generation (6G) networks. With the huge increase of SMDs, many applications of SMDs can be interrupted due to the limited energy supply. Combining MEC and energy harvesting (EH) can help solve this issue, where computation-intensive tasks can be offloaded to edge servers and the SMDs can also be charged during the offloading. In this work, we aim to minimize the total energy consumption subject to the service latency requirement by jointly optimizing the task offloading ratio and resource allocation (including time switching (TS) factor, uplink transmission power of SMDs, downlink transmission power of eNodeB, computation resources of SMDs and MEC server). Compared with the previous studies, the task uplink transmission time, MEC computation time and the computation results downloading time are all considered in this problem. Since the problem is non-convex, we first reformulate it, and then decompose it into two subproblems, i.e., joint uplink and downlink transmission time optimization subproblem (JUDTT-OP) and joint task offloading ratio and TS factor optimization subproblem (JTORTSF-OP). By solving the two subproblems, a joint task offloading and resource allocation with EH (JTORAEH) algorithm is proposed to solve the considered problem. Simulation results show that compared with other benchmark methods, the proposed JTORAEH algorithm can achieve a better performance in terms of the total energy consumption.

Energy Efficient QoS-Based Access Point Selection in Hybrid WiFi and LiFi IoT Networks

Ensuring Quality of Service (QoS) in next-generation networks would be challenging because of the expected manifolds increase in network traffic volume and the massive number of connected Internet of Things (IoT) devices. The increased resources on end-devices have made it possible to make decisions on the client-side for Access Point (AP) selection. On the other hand, due to power constraints on the client-side, energy efficiency is also essential. This paper presents a client-side energy-efficient AP selection approach for QoS provisioning in hybrid Wireless Fidelity (WiFi) and Light Fidelity (LiFi) networks. Compared to conventional WiFi and LiFi approaches, the proposed technique outperforms them in energy efficiency at any data rate and in QoS provisioning at data rates higher than 10 Mbps for a network of 50 nodes. It is also shown that adapting the uplink transmission power improves the IoT node’s energy efficiency by transmitting at minimal transmission power required to satisfy the throughput QoS constraint. This paper’s contributions include the proposed algorithm, complexity and convergence analyses of the algorithm, simulation-based evaluation, and analysis of the uplink transmission power adaption. The results show that the presented technique could be integrated into next-generation networks for QoS provisioning and improving energy efficiency.

Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

The edge computing paradigm has become extremely popular over the past years, as a means of offloading computationally intensive tasks by users of resource and battery-constrained devices. Nevertheless, the edge networks’ overexploitation by the ever-increasing number of task-offloading users, gradually leads to their performance degradation. In this paper, we leverage on the different levels of available computing capabilities across the network, and we design an incentive mechanism that aims to shift the selfish users’ preference from the edge to the upper fog computing layer, accounting for their level of delay tolerance. To deal with the users’ heterogeneity in terms of their applications’ multi-dimensional distinctive features (including their delay tolerance/sensitivity), a multi-dimensional contract theory modeling is adopted, according to which the edge server determines the bundles of the users’ provided efforts and corresponding offered rewards. In this respect, each user’s effort represents the amount of its initially offloaded task at the edge that is allowed to be further forwarded and processed at the fog. Considering that the users-to-edge server offloading is performed under Non-Orthogonal Multiple Access (NOMA), the problem of joint computation task offloading and uplink transmission power allocation is subsequently addressed via a Stackelberg game, where the edge server and the users are treated as leader and followers, respectively. The aim of the game is to minimize the end-to-end network’s energy consumption and increase its resource utilization efficiency. The incentive mechanism and resource allocation framework is evaluated via modeling and simulation regarding its operation and efficiency under different scenarios.

Joint UL/DL Resource Allocation for UAV-Aided Full-Duplex NOMA Communications

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different directions in FD-NOMA systems. Firstly, a joint optimization problem is formulated for the uplink and downlink resource allocation of transceivers and UAV relay. The receiver determination is performed using an access-priority method. Based on the results of the receiver determination, the initial power of ground users (GUs), UAV, and base station is calculated. According to the minimum sum of the uplink transmission power, the Hungarian algorithm is utilized to pair the users. Secondly, the subchannels are assigned to the paired GUs and the UAV by a message-passing algorithm. Finally, the transmission power of the GUs and the UAV is jointly fine-tuned using the proposed access control methods. Simulation results confirm that the proposed method achieves higher performance than state-of-the-art orthogonal frequency division multiple-access method in terms of spectrum efficiency, energy efficiency, and access ratio of the ground users.

Real-time dual-link transmission control for minimizing power and link switching cost

This paper considers a problem of real-time control of uplink transmissions over dual links. In the problem, a UE is configured with dual links. We propose a joint control algorithm to choose a primary link and activate or deactivate each link in every slot. The activation of a link incurs uplink transmission power. Switching a primary link also incurs cost. The control algorithm aims to jointly minimize the transmission power and primary link switching cost, while minimizing transmission queues to reduce delays. The problem introduces several constraints on transmission power allocation and link activation. In particular, the secondary link can be activated only when it has non-empty transmission queues and the primary link is activated. For the primary link, no such restriction is imposed. These heterogeneous transmission constraints require the decisions to treat a time-varying decision set. Furthermore, the primary link switching cost may enforce the previous decisions to skew the current decision. To tackle such difficulties, we use Lyapunov optimization techniques and develop a control algorithm. We investigate the algorithm structure from the decisions made for special cases. We reveal that the primary link selection has a Shortest-Queue with Hysteresis structure. We also show that link activation control largely relies on transmission efficiency. We analyze the performance bound of our algorithm in terms of cost and queues to show that our algorithm can achieve an optimal target value. Finally, we present simulation results to verify the performance of our algorithm.

Dynamic Computation Offloading With Energy Harvesting Devices: A Graph-Based Deep Reinforcement Learning Approach

We study a joint partial offloading and resource allocations (JPORA) problem for the mobile edge computing (MEC) with energy harvesting (EH), where the number of MDs, computation task, and harvested energy of mobile device (MD) are highly dynamic. It is critical to acquire an algorithm that optimally adapts JPORA decisions. Recent studies employ deep deterministic policy gradient (DDPG) agent to tackle the JPORA problem. However, traditional DDPG cannot generalize well to different MEC network scale, due to the deep neural networks in DDPG can only extract latent representation from Euclidean data, with the characteristics of MEC network structural information ignored. To this end, by taking advantage of the graph-based relationship deduction ability form graph convolutional networks (GCN) and the self-evolution ability from the experience training of DDPG, we propose a centralized GCN-DDPG agent to learn making decisions for MDs, including offloading ratio, local computation capacity, and uplink transmission power. Experimental results show that the proposed GCN-DDPG provides significant performance improvement over a number of state-of-the-art DRL agents.

Critical power analysis for control path of a CAT-M based edge device

LTE Machine Type Communication Category M1 [LTE CAT M (or M1)] is a 3GPP Low Power Wide Area Network most suitable for IoT devices that need low data rates, deep coverage, low-cost, and, most importantly, power-efficient devices. As the number of IoT system devices (called things) is growing massively, there is a shift needed from the traditional LTE network to these low power networks. Power consumption should be minimum for all stages of device functionality to keep the devices working for longest possible time. We performed a power consumption analysis of CAT-M technology under various control path scenarios using a standard CAT-M modem. Due to the absence of a live CAT-M network in India, we use a network simulator to emulate live network scenario by varying network parameters. We measured the power consumed by the CAT-M modem using a standard tool and present simulation results of a variety of network configurable parameters allowed by 3GPP. These parameters are configurable both in scope of user equipment and network deployment. This paper covers some important power measurements related to Power Saving Mode (PSM), extended Discontinuous Reception Mode (eDRX), Uplink Transmission Power, Channel Repetition, Bandwidth and Sub-carrier Spacing. Data path (user payload) considerations are not scope of this document. By the end of the paper, we identified the impact of each parameter on the performance of the CAT-M modem.

Control Efficient Power Allocation of Uplink NOMA in UAV-Aided Vehicular Platooning

Power allocation in non-orthogonal multiple access (NOMA) systems is essential to avoid multi-user detection failure. However, controlling the uplink transmission power brings extra signaling overhead, especially in dynamic environments. In this paper, we investigate the uplink transmission power allocation problem in an unmanned aerial vehicle (UAV)-aided platooning NOMA system, where the UAV acts as a relay to assist inter-vehicle communications. Platoon vehicles are admitted to the same vehicle-to-UAV (V2U) channel via our proposed sequence-based power allocation (SPA) strategy. Instead of achieving the optimal energy efficiency, our goal is to design a simple yet effective power control scheme. Specially, in SPA strategy, vehicles determine their transmission power according to their positions in the platoon. Considering platoon dynamics due to vehicle mobility, a power-reserved mechanism is further incorporated in the proposed strategy to pre-allocate power resources for newly joined vehicles. In addition, a power-moving mechanism is further employed to guarantee the uplink signal-to-interference-plus-noise ratio (SINR) of vehicles by changing vehicle transmission power into higher levels during vehicle leaving a platoon. As the system only needs to update vehicle positions for the SPA strategy, only a small amount of signaling is required in our power allocation strategy. Extensive simulation results are provided to demonstrate that the proposed SPA strategy ensures the required SINR of platoon communication in different dynamic scenarios.

5G Multi-Band Numerology-Based TDD RAN Slicing for Throughput and Latency Sensitive Services

This paper extensively examines the impact of the numerology schemes on a sliced TDD radio access network. The slices are multiplexed over both the sub-6 GHz and mmWave bands. The incorporation of numerology schemes into network slicing, the impact of which has yet to be explored, will be highly instrumental in realizing the future 5G and 6G networks. Thus, this work demonstrates the enhanced capabilities of sliced networks utilizing numerology. To that end, two optimization problems are formulated, one to maximize the downlink average spectral efficiency of a throughput-sensitive slice, and the other to minimize the uplink transmission power of the users of the latency-sensitive slice. This work optimizes key parameters such as duplex ratio, numerology scheme and optimally allocates power and bandwidth under numerology-enabled slicing. The comparative analyses highlight that for high-throughput applications, lower numerology schemes are significantly more spectrally efficient and can often achieve throughputs characteristic of higher schemes. On the other hand, the higher schemes are the best choice for the minimization of user transmission power and latency, which are crucial in energy-efficient communications and edge computing applications, respectively.

Two-way relaying channel with lattice forwarding and microwave power transfer

In this paper, we consider a two-way relaying channel with simultaneous wireless information and power transfer. The intermediate relay station forwards lattice codes under transmit power constraint. Meanwhile, it charges user terminals for static circuit consumption and uplink transmission power. We novelly propose an iterative algorithm to maximize the sum-rate with joint beam-forming and power splitting design in MPT assisted TWRC. The method achieves the highest achievable rate compared with separate optimization designs and time switching method. Moreover, an extremely small gap between the proposed method and the outer cut-set bound is observed.

Optimizing a Secure Two-Way Network with Non-Linear SWIPT, Channel Uncertainty, and a Hidden Eavesdropper

In this paper, the optimization of downlink beamforming vectors, uplink transmission power, and power-splitting factors is investigated for a secure two-way SWIPT network in the presence of a hidden eavesdropper and non-linear energy harvesting circuits over both perfect and imperfect channels. The eavesdropper is inactive, so its channel information is not available at the base stations (BSs). The purpose of artificial noise is to create downlink interference with the hidden eavesdropper as much as possible, while satisfying the quality of service for two-way communications. For perfect downlink channels, the semidefinite relaxation (SDR) technique is exploited, and the optimal matrices are proven to satisfy rank-1 conditions, thus providing the optimal beamforming vectors. For imperfect downlink channel state information, we propose an iterative algorithm with a penalty function to obtain the approximate rank-1 matrices. On uplink, we attain the optimal transmission power for users receiving maximum ratio transmission beamforming at the BSs. Eventually, the numerical experiments show the superiority of the proposed scheme, compared to a conventional scheme, in terms of signal-to-interference-plus-noise ratio at the eavesdropper.

Scheduling for Mobile Edge Computing With Random User Arrivals—An Approximate MDP and Reinforcement Learning Approach

In this paper, we investigate the scheduling design of a mobile edge computing (MEC) system, where active mobile devices with computation tasks randomly appear in a cell. Every task can be computed at either the mobile device or the MEC server. We jointly optimize the task offloading decision, uplink transmission device selection and power allocation by formulating the problem as an infinite-horizon Markov decision process (MDP). Compared with most of the existing literature, this is the first attempt to address the transmission and computation optimization with random device arrivals in an infinite time horizon to our best knowledge. Due to the uncertainty in the device number and location, the conventional approximate MDP approaches addressing the curse of dimensionality cannot be applied. An alternative and suitable low-complexity solution framework is proposed in this work. We first introduce a baseline scheduling policy, whose value function can be derived analytically with the statistics of random mobile device arrivals. Then, one-step policy iteration is adopted to obtain a sub-optimal scheduling policy whose performance can be bounded analytically. The complexity of deriving the sub-optimal policy is reduced dramatically compared with conventional solutions of MDP by eliminating the complicated value iteration. To address a more general scenario where the statistics of random mobile device arrivals are unknown, a novel and efficient algorithm integrating reinforcement learning and stochastic gradient descent (SGD) is proposed to improve the system performance in an online manner. Simulation results show that the gain of the sub-optimal policy over various benchmarks is significant.

Energy-efficient user selection and resource allocation in mobile edge computing

Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user. Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption. However, how to select the best edge cloud in a dense cell to execute tasks remains challenging. To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user’s energy efficiency, defined as the ratio of user throughput to its energy consumption. We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users’ offloading selection and uplink transmission power. To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation. Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency.

Efficiency ratio optimization on uplink transmission power for Cloud-Based Radio Access Network

With the adoption of fifth generation (5G) wireless network systems into the fourth Industrial Revolution, the data transfer efficiency and speed will see a significantly improvement, which will lead to systems having the ability to provide a real-time information, and a solution to the capacity problem. Cloud-Based Radio Access Network (C-RAN) is the groundwork on which future 5G networks are based on, and it can increase the spectral efficiency and lower the transmission latency. Moreover, the used of C-RAN in future is expected to decrease the capital, operational and maintenance expenses of mobile operators. This study aims to investigate and validate the joint measures of spectral efficiency and latency optimization by using a quantization process of uplink signal at the remote radio head (RRH) of the C-RAN network. The quantization process is used to convert the received radio frequency signal from mobile user equipment into in-phase and quadrature baseband samples. The uplink transmission power and the number of quantization bits are computed in a way that the performance of spectral efficiency and latency of the RRHs are simultaneously optimized. Results shows that the proposed scheme is capable working with the different types of RRH.

Joint Resource Management With Distributed Uplink Power Control in Full-Duplex OFDMA Networks

Resource allocation problems in full-duplex orthogonal frequency division multiple access (FD-OFDMA) networks are challenging due to their combinatorial, non-convex nature. In this paper, user pairing, subcarrier and power allocation in a single cell FD-OFDMA network are considered. A joint optimization problem is formulated to maximize the network's sum rate while satisfying downlink (DL) and uplink (UL) transmission power’ constraints. Due to the sheer complexity of the proposed formulation, mainly due to its combinatorial nature, an efficient, iterative two-step solution algorithm for the joint problem is proposed. In the first step, based on defining the DL user equipment (UE) signal to noise ratio (SNR) threshold, which is the least SNR that can be detected by the DL-UE, an algorithm is proposed for user pairing and subcarrier assignment. In the second step, the power allocation problem for the assigned users’ pairs is formulated and solved using the Alternating Direction Method of Multipliers (ADMM) in the high signal-to-interference-noise ratio regime. Finally, numerical results are presented to validate the performance of the proposed algorithms. We show that the performance of our proposed computationally-efficient two-step algorithm is very close to the sum rate upper bound derived from solving the dual problem.

Energy-Efficient Orchestration in Wireless Powered Internet of Things Infrastructures

Multipurpose devices, capable of dynamically operating under various sensing modes, have emerged as a key element of the Internet of Things (IoT) ecosystem. In this paper, the holistic orchestration of an energy-efficient operational framework of such interconnected devices is investigated. A reinforcement learning technique is utilized enabling each IoT node, by acting as a learning automaton, to select a sensing operation mode in accordance with the IoT infrastructure’s provider interests. Subsequently, a coalition formation among the nodes is realized, relying on their socio-physical relations, namely nodes’ spatial proximity, energy availability, and mode correlations. The aforementioned operation is supported by a non-orthogonal multiple access wireless powered communication environment, where the nodes are able of harvesting energy from the base station. The energy efficiency of the overall system, is further improved by a utility-based optimal uplink transmission power control mechanism. The corresponding optimization problem is treated in a distributed manner as a non-cooperative game-theoretic problem, and the existence of a unique Nash equilibrium is shown, while the adoption of convex-based pricing in the utility leads to a more socially desirable Equilibrium point. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority is demonstrated.