Collaborative multiple access and energy-efficient resource allocation in distributed maritime wireless networks

To improve the operation lifetime of devices and the anti-interference capability of complex electromagnetic environments in device-to-device (D2D) networks, an energy-efficient maximization resource allocation algorithm was proposed for simultaneous wireless information and power transfer (SWIPT)-based D2D networks. Considering the constraints of users' minimum rate, minimum harvested energy, power splitting, and channel allocation, a robust energy-efficient resource allocation model was formulated by jointly optimizing the transmit power, subchannel coefficients, and the power splitting factor. The original NP-hard problem was converted into a deterministic and convex problem by using Dinkelbach's method and the worst-case approach, where the closed-form solutions were obtained by using the matching theory and Lagrange dual theory. Simulation results demonstrated that the proposed algorithm exhibited better robustness and higher energy efficiency.

In this paper, single- and multi- user Resource Allocation (RA) optimization problems considering transmit power and minimum rate constraint of Mobile Station (MS) for maximizing MS' energy efficiency, measured as bits-per-joule (bpj), are addressed. Assume channel state information of all MSs is known by base station. We propose uplink RA algorithms, performing subcarrier assignment and power allocation, for optimizing bpj of MS in a single-cell OFDMA-based cellular network for both single- and multi- user scenarios. In the single-user case, we propose RA algorithms, which utilize the closed-form solution derived by applying Lambert-W function and an iterative approach based on Karush---Kuhn---Tucker conditions respectively to achieve optimal bpj of MS. In the multi-user case, centralized iterative multi-user RA algorithms for maximizing sum of MS' bpj, performing joint subcarrier assignment and power allocation iteratively, are proposed by utilizing the proposed single-user RA schemes. In particular, tradeoffs between energy efficiency and spectral efficiency are fully investigated, and the influence of MS' power and minimum rate constraints on bpj performance is also studied. The effectiveness of proposed algorithms is presented by numerical experiments. Numerical results demonstrate the proposed algorithms can enhance bpj significantly with limited loss of total throughput compared to the sum-rate maximization algorithm (in Moretti et al., IEEE Trans Veh Technol 60(4):1788---1798, 2011).

In this paper we propose a resource allocation algorithm for uplink sparse code multiple access (SCMA) networks to maximize the energy efficiency (EE). Due to the joint optimization of factor graph matrix and power allocation matrix, the EE maximization is a non-convex mixed-integer non-linear programming (MINLP) problem. After transforming the non-convex form of the uplink sum rate to a convex one and separating subcarrier assignment and power allocation, we propose an energy efficient subcarrier assignment algorithm. By applying the fractional programming theory based on the Dinkelbach method, we then propose power allocation algorithm to maximize EE. Finally, the simulation results show that the proposed resource allocation algorithm can significantly increase the EE of the uplink SCMA network.

Energy efficient resource allocation based on relay selection and subcarrier pairing with channel uncertainty in cognitive radio network

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macro-cell and multiple pico-cells. Considering a heterogeneous real-time and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

With the rapid advancement of wireless network technologies and the constant increase in the number of mobile devices, enormous amount of data is generated from a set of user equipments (UEs), such as mobile phones and wearable devices. How to power these devices wirelessly becomes a challenging issue. Meanwhile, data and energy integrated communication networks (DEINs) are emerging as a new type of wireless networks emphasizing on the cooperation between wireless information and energy transfer. This means that the DEINs can be a solution to tackle with powering UEs wirelessly and further achieving a higher data rate. However, there are no researches that have been studied about how to obtain the optimal scheme in DEINs under the consideration of data traffic. Hence, we propose a fair energy-efficient resource allocation algorithm for DEINs in this paper. Our objective is to optimize the energy efficiency with the consideration of providing fair queuing for data traffic by minimizing the queue length difference among the UEs' buffers. Besides, when designing the downlink energy beamforming and the downlink-uplink power-and-time allocation scheme, power sensitivity of conversion circuits and system stability are taken into account. Simulation results illustrate that the proposed resource allocation algorithm does not only improve the fairness among UEs by queue balancing, but also achieve close-to-optimal system energy efficiency by the beamforming design and resource allocation.

With the explosive growth of wireless data traffic, the energy-efficient design in wireless networks is becoming an inevitable trend. In this paper, we study the low-complexity design issue of energy-efficient radio resource allocation in downlink OFDMA networks. Distinguished from the existing works, we focus on the tradeoff between the energy efficiency and the total transmit power. The closed-form optimal solution is obtained by solving the first-order optimality condition of the energy efficiency maximization problem. Furthermore, based on the analytical solutions, we develop a low-complexity optimal algorithm for the energy-efficient resource allocation. Simulation results show that the proposed algorithm achieves the optimal energy efficiency with significantly lower computational complexity compared with the conventional iterative methods.

It is proposed that resource allocation algorithm considers rate proportion among the multicast groups in multicast orthogonal frequency division multiplexing (OFDM) downlink systems in this paper. However, it is minor significance to enforce the rate proportion strictly in actual mobile communication system. To solve this problem, the resource allocation algorithm is proposed to satisfy the rate proportion approximately in this text, including sub carrier allocation algorithm and power allocation algorithm. The former is achieved by modifying the existing unicast sub carrier assignment algorithm, the latter is Water-filling algorithm, which is capable of maximize the capacity when sub carrier assignment is determined. Simulation results show that compared to conventional algorithms, the proposed resource allocation algorithm has a good performance in capacity as well as rate proportion, which is a realistic method to solve the resource allocation in actual mobile communication system. In addition, we also discussed the capacity has a close relation with the number of multicast group.

This paper considers the network resource allocation problem in multi-service MEC networks, and in particular considers joint spectrum and computing resource allocation in an edge network service system. The resource allocation problem under consideration is formulated as an optimization problem, which takes into account users’ minimum service requirements in terms of the service delay, transmission rate, and computation rate, with an objective to minimize the average service delay of a user’s service request. To solve the problem, we propose a deep-Q-network (DQN) based joint spectrum and computing resource allocation algorithm, which attempts to optimize the resource allocation for each service request by learning a more efficient allocation scheme so as to better adapt to dynamic traffic arrivals, diverse service requirements, and complex environmental conditions. Simulation results show that the proposed resource allocation algorithm can efficiently reduce the average service delay compared with two benchmark algorithms.

This thesis presents novel resource allocation algorithms for energy and power efficient transmissions in future mobile wireless networks. Algorithms presented in thesis incorporate a combination of practical conditions: mixed services, rate fairness, the density of deployment of femtocells and non-linear power consumption. The resource allocation problem of sparse and dense deployment of Orthogonal Frequency Division Multiple Access based heterogeneous femtocell networks is solved by optimising spectral efficiency, energy efficiency and spectral and energy efficiency trade-off respectively. In general, this research proposes several resource allocation algorithms that are practical, efficient and lower in computational complexity.

With more and more stringent requirements of large number of connections and high scalability in the next generation wireless network, a novel multiple access method, pattern division multiple access (PDMA), has become one of the promised technologies and provides higher efficiency in the spectrum resource utilization. In this work, we propose a joint resource allocation and traffic offloading scheme in the PDMA-based satellite/terrestrial network. The proposed resource allocation algorithm is based on two convex optimization methods, which iteratively solves the spectrum splitting and subcarrier allocation. Moreover, numerical simulation results demonstrate that the proposed algorithm in PDMA performs better in network throughput, energy efficiency and traffic offloading rate than in the traditional network.

An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.

Multiple-input multiple-output (MIMO) wireless communication systems in combination with orthogonal frequency division multiple access and space division multiple access are flexible, spectrally efficient and of high capacity. These systems can allocate frequency, time, and space resources adaptively among mobile stations. Optimal resource allocation (RA) for maximizing the sum rate is usually too complex for practical applications, hence suboptimal algorithms are required. The performance of RA algorithms are degraded due to imperfections in channel state information at transmitter (CSIT). Thus, techniques that make the RA algorithm, robust against CSIT errors are favorable. In this paper, two novel suboptimal grouping algorithms are introduced that are shown to achieve better sum rate-complexity and fairness-complexity performances. A novel index is proposed to measure the efficiency of RA algorithms, which takes into account sum rate and complexity simultaneously. Moreover, the effect of erroneous CSIT on sum rate is investigated and it is shown that performance of the proposed RA algorithms with erroneous CSIT are superior to the existing schemes. Finally, fairness performance of the proposed RA algorithms are evaluated, which are shown to be the same if not superior to other existing algorithms.

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme.

In this paper, an energy efficient resource allocation is investigated to enhance the network performance of cellular networks. Specifically, the small cells (SCs) using both green and on-grid energy and the power splitter-enabled wireless charging for user equipments (UEs) are considered. A quality-of-service (QoS)-constrained problem is then designed to maximize energy efficiency (EE) through the joint strategies of power allocation, resource block (RB) assignment, and power splitting ratio adjustment. By exploiting some mathematical transformations, the non-convex optimization problem can be solved by adopting the proposed joint wireless charging and hybrid power based resource allocation (JWHRA) algorithm. The efficiency of proposed algorithm is validated via simulation. Numerical results show that the proposed JWHRA scheme can provide higher EE with its joint design on wireless charging and hybrid energy source.