Resource Allocation Scheme For Downlink Research Articles

Cross-Modal Stream Transmission: Architecture, Strategy, and Technology

Since the multi-modal services, which elaborately integrate audio, video, and haptic streams, are able to substantially improve the users' immersive experience, they are gradually becoming the mainstream applications. However, the existing solutions fail to simultaneously satisfy the diverse transmission requests of multi-modal services in terms of lower latency, higher reliability, and higher throughput, especially in the resource-constrained and dynamic environment. To circumvent this dilemma, this work constructs a cross-modal stream transmission architecture by exploring the intra-modal redundancy and the inter-modal correlation. Specifically, we propose an adaptive stream scheduling strategy, which can not only substantially decrease the stream traffics, but also dramatically reduce the impact of the channel uncertainty and the haptic signals' stochastic arrival on the delay jitter and reliability. Moreover, through prioritizing the resource provision of haptic signals, we design a joint uplink and downlink resource allocation scheme based on the prediction model. In particular, the transmitter sends the predicted haptic signals via the allocated resource blocks in advance according to the optimal window size, so as to offset the latency and remain the symmetry during the bilateral haptic transmission. Meanwhile, we develop an audio and video stream transmission scheme in a hierarchical and reusing structure by integrating the power-domain NOMA with LMDC-FEC, which fully utilizes the remaining limited resources to maximize the robustness of audio-video transmission.

Robust Sum-Rate Maximization for Underlay Device-to-Device Communications on Multiple Channels

Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-state-information (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentralized setting, the computational load is distributed among the D2D pairs and the base station, keeping also a low communication overhead. Moreover, we also provide a theoretical convergence analysis, including also the rate of convergence to stationary points. The proposed algorithms have been experimentally tested in a simulation environment, showing their favorable performance, as compared with the state-of-the-art alternatives.

Delay aware downlink resource allocation scheme for future generation tactical wireless networks

<span lang="EN-US">For a very long time protecting physical border integrity is considered to be a challenging thing. Government organizations must provide trade operations for economic growth and at the same time must prevent malicious activity. A different resource such as drones, sensors, and radars are used for monitoring border areas which must be communicated to the remote border security force. Efficient wireless communication is required for communicating information. However, these devices cannot connect to a centralized network directly; thus, are connected in an ad-hoc fashion to connect centralized server. Different tactical network applications require different quality of service (QoS); hus efficient resource scheduling plays a very important role. Existing resource scheduling adopting deep learning and reinforcement techniques fails to meet the quality of experience (QoE) of the user and doesn’t assure access fairness among contending users. Further, require network information in prior and induce high training time. For overcoming research issues, this paper presents a delay-aware downlink resource scheduling (DADRA) technique for future generation networks. The optimization problem of reducing buffer overflow and improving scheduling QoS performance is solved using a genetic algorithm with an improved crossover function. Experiment outcome shows DADRA achieves much better throughput, slot utilization, and packet failure performance when compared with standard resource allocation technique.</span>

Joint rate maximization of downlink and uplink in NOMA SWIPT systems

The combination of Non-orthogonal multiple access (NOMA) and Simultaneous wireless information and power transfer (SWIPT) is an emerging technology that can meet the requirements of spectral efficiency and energy efficiency. In this paper, we study the achievable rate maximization for jointly downlink and uplink network with power splitting-based SWIPT. We propose two different models. By using Single-Input Single-Output (SISO)-NOMA in conjunction with SWIPT, a joint uplink and downlink resource allocation scheme is proposed with the optimization objective to maximize the sum rate. We prove the objective function is strictly concave and found the optimal power splitting factor of each user. By using Multiple-Input Multiple-Output (MIMO)-NOMA in conjunction with SWIPT, we add timeslot allocation and propose a power splitting control and a timeslot allocation scheme. The optimization problem is divided into two subproblems to reduce solving complexity. In more detail, we first design the power splitting control scheme to maximize both the achievable rate and harvested energy in the downlink. Then timeslot optimization method is designed to improve the sum throughput. The simulation results show that a relatively high downlink rate can be achieved, while the required energy is simultaneously harvested by the users for the uplink.

Enhanced energy-efficient downlink resource allocation in green non-orthogonal multiple access systems

Non-orthogonal multiple access (NOMA) technique has the ability to support multiple transmissions simultaneously using the same physical resource, the idea of which achieves much enhanced performance for a system compared to the conventional orthogonal multiple access (OMA) techniques. Despite numerous advantages, this phenomenon of NOMA technique can bring additional interference for the neighboring ultra-dense networks if the power consumption of the system is not properly optimized. While targeting on the green communication concept, in this paper, we propose an energy-efficient downlink resource allocation scheme for a NOMA-equipped cellular network. The objective of this work is to allocate subchannels and power of the base station among the users so that the overall energy efficiency is maximized. Since this problem is NP-hard, we attempt to find an elegant solution with reasonable complexity that provides good performance for some realistic applications. To this end, we decompose the problem into a subchannel allocation subproblem followed by a power loading subproblem that allocates power to each user’s data stream on each of its allocated subchannels. We first employ a many-to-many matching model under the assumption of uniform power loading in order to obtain the solution of the first subproblem with reasonable performance. Once the subchannel–user mapping information is known from the first solution, we propose a geometric programming (GP)-based power loading scheme upon approximating the energy efficiency of the system by a ratio of two posynomials. The techniques adopted for these subproblems better exploit the available multi-user diversity compared to the techniques used in an earlier work. Having observed the computational overhead of the GP-based power loading scheme, we also propose a suboptimal computationally-efficient algorithm for the power loading subproblem with a polynomial time complexity that provides reasonably good performance. Extensive simulation has been conducted to verify that our proposed solution schemes always outperform the existing work while consuming much less power at the base station.

Joint Uplink and Downlink Resource Allocation for D2D Communications System

In cellular networks, device-to-device communications can increase the spectrum efficiency, but some conventional schemes only consider uplink or downlink resource allocation. In this paper, we propose the joint uplink and downlink resource allocation scheme which maximizes the system capacity and guarantees the signal-to-noise-and-interference ratio of both cellular users and device-to-device pairs. The optimization problem is formulated as a mixed integer nonlinear problem that is usually NP hard. To achieve the reasonable resource allocation, the optimization problem is divided into two sub-problems including power allocation and channel assignment. It is proved that the objective function of power control is a convex function, in which the optimal transmission power can be obtained. The Hungarian algorithm is developed to achieve joint uplink and downlink channel assignment. The proposed scheme can improve the system capacity performance and increase the spectrum efficiency. Numerical results reveal that the performance of the proposed scheme of jointly uplink and downlink is better than that of the schemes for independent allocation.

Joint Uplink and Downlink Resource Allocation for the Internet of Things

High-efficiency utilization of resources has always been the major concern and design objective in the Internet of Things (IoT). In this paper, an orthogonal frequency division multiplexing-based wireless system model for IoT is proposed, where the simultaneous wireless information and power transfer and the non-orthogonal multiple access (NOMA) are jointly used to improve the energy and spectrum utilization. Specifically, terminals can decode information and harvest energy simultaneously in the downlink and transmit signals to the access point (AP) in the uplink. Then, the AP decodes terminals’ information based on NOMA. A joint uplink and downlink resource allocation scheme is proposed with the optimization objective to maximize the sum information decoding rate in the uplink under the condition that the target sum information decoding rate in the downlink is guaranteed. To solve the optimization problem, a two-step algorithm is proposed. For the first step, a joint subcarrier and power allocation algorithm is designed to maximize the harvested energy of terminals in the downlink. For the second step, a subslot optimization algorithm is designed to increase the sum information decoding rate in the uplink. Simulation results demonstrate that, compared with the conventional algorithms, the proposed algorithm can not only achieve the maximization of harvested energy but also support higher sum information decoding rate in the uplink.

Energy-Adaptive Downlink Resource Allocation in Wireless Cellular Systems

Mobile devices have increasingly been used to run multimedia applications which are extremely downlink-intensive. The conventional rate adaptive and/or margin adaptive approach for radio resource allocation may result in unnecessary energy consumption on mobile devices, which will not be energy efficient for mobile multimedia applications. In this paper, we develop an energy adaptive approach and design an energy-efficient downlink resource allocation scheme to support multimedia applications. The objective is to minimize the total energy consumption of mobile devices for data reception while meeting the data rate requirements at mobile devices and the transmit power constraint at the base station. We show that the optimization problem is $\mathcal {NP}$ -hard and then propose an efficient algorithm that has a provable performance guarantee under a certain condition. We have conducted extensive simulations to evaluate the efficacy of the proposed algorithm and our results provide useful insights into the design of energy-efficient resource allocation for wireless systems.

Energy-saving centric uplink scheduling scheme for broadband wireless access networks

Abstract This study proposes an energy-saving centric uplink scheduling (ESC-US) scheme to support efficient energy usage and satisfy the quality of service (QoS) requirements of Worldwide Interoperability for Microwave Access (WiMax) networks. The uplink resource allocation is different from that of the downlink resource allocation scheme because the uplink traffic is queued at the mobile station (MS) and the base station (BS) has no information regarding it without using a polling procedure. The considered resource scheduling schemes maximize the sleep efficiency and consider the QoS requirements of individual MSs. The proposed scheduling scheme in this study considers the delay budget of MSs with real-time connections and the required minimum reserved traffic rate (MRTR) of MSs with non-real time connections when maximizing sleep efficiency. Both scheduling schemes for the traffic of real-time polling services (rtPS) and non-real-time polling services (nrtPS) apply the ‘just enough QoS’ and ‘sleep before transmission’ (SbT) concepts to achieve this energy-saving centric objective. Exhaustive simulations were conducted to examine the performance of the proposed schemes. The simulation results show that both schemes guarantee the desired QoS and achieve superior energy-savings efficiencies compared to the conventional scheme.

An efficient genetic-based downlink resource allocation scheme in LTE base stations

Long term evolution LTE base stations BS are greatly capable of improving various wireless services. As the amazingly rapid explosion of demands of various network-service applications, how to apropos design an efficient and effective resource allocation scheme for satisfying users' requirements in all perspectives in LTE BSs plays a crucial role. In this study, a state-of-the-art genetic-based resource allocation scheme has now been proposed in LTE BSs for downlink transmissions. The presented scheme supports multi-priority users and employs an aptly modified proportional fair based utility function as the performance metric. The performance evaluation of the proposed scheme is shown able to nearly achieve the optimal solution in an acceptable short time interval of few genetic generations in practice.

Downlink Resource Allocation for Next Generation Wireless Networks with Inter-Cell Interference

This paper presents a novel downlink resource allocation scheme for OFDMA-based next generation wireless networks subject to inter-cell interference (ICI). The scheme consists of radio resource and power allocations, which are implemented separately. Low-complexity heuristic algorithms are first proposed to achieve the radio resource allocation, where graph-based framework and fine physical resource block (PRB) assignment are performed to mitigate major ICI and hence improve the network performance. Given the solution of radio resource allocation, a novel distributed power allocation is then performed to optimize the performance of cell-edge users under the condition that desirable performance for cell-center users must be maintained. The power optimization is formulated as an iterative barrier-constrained water-filling problem and solved by using the Lagrange method. Simulation results indicate that our proposed scheme can achieve significantly balanced performance improvement between cell-edge and cell-center users in multi-cell networks compared with other schemes, and therefore realize the goal of future wireless networks in terms of providing high performance to anyone from anywhere.

Adaptive Resource Allocation in MIMO-OFDMA based on Subcarrier Allocation (SA) and (GA)

In recent years, the eyes of scholars has been on the road to an proficient allocation scheme of signalling dimension between users in both uplink and downlink channels, since bandwidth is usually limited and/or very expensive. This paper covenants with the rate adaptive MIMO-OFDMA (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) downlink resource allocation scheme. It has an objective of maximizing the total system capacity subject to constraints on total power, bit error rate and proportional fairness. The proposed algorithm determines the number of subcarriers for each user and then handing over the subcarrier to each user. Two optimization methods, one based on Subcarrier Allocation (SA) and the other, based on Genetic Algorithm (GA) are used for resource allocation. In the optimization technique based on SA, using SVD (Singular Value Decomposition), the MIMO fading channel of each subcarrier is transformed into an equivalent bank of parallel SISO (Single Input Single Output) subchannels, which leads to a better trade-off strategy among system capacity, computational complexity and proportional fairness. Paralleled to static allocation schemes, the new algorithm has significantly lower complexity and in simulation fruitages higher system capacity. Simulation results show that the subcarrier allocation based on GA can provide capacity improvement with the reduction in fairness.

An Effective Downlink Resource Allocation Scheme Based on MIMO-OFDMA-CDM in Cellular System

Orthogonal frequency division multiple access (OFDMA) is adopted as a multiuser access scheme in recent cellular systems such as long term evolution (LTE) and WiMAX. In those systems, the performance improvement on cell-edge users is crucial to provide high-speed services. We propose a new resource allocation scheme based on multiple input multiple output — orthogonal frequency division multiple access — code division multiplexing (MIMO-OFDMA-CDM) to achieve performance improvements in terms of cell-edge user throughput, bit error rate, and fairness among users. The proposed scheme adopts code division multiplexing for MIMO-OFDMA and a modified proportional fairness algorithm for CDM, which enables the fairness among users and a higher throughput. The performance improvements are clarified by theoretical analysis and simulations.