Distributed resource allocation for wireless virtualized energy harvesting small cell networks

Offloading data traffic from the conventional macro- cell base stations to densely deployed small-cell base stations (SBSs) has been emerging as a promising technique to support the explosion of data traffic with reduced energy consumption and improved quality of service provision. In this paper, we study the joint spectrum allocation and power allocation problem for the data offloading in energy-harvesting downlink small-cell networks. First, we formulate the resource allocation problem under the revenue maximization criterion, which is expressed as the difference between the total utility across users and the total power payment. By proving the convexity of the problem, we can compute the solution efficiently. Numerical results show that the energy-efficiency can be improved while alleviating the burden of macro-cell base station through using the resource allocation scheme proposed for data offloading.

The concept of energy harvesting small cell networks (EH-SCNs) has attracted a great deal of attention due to their potential to meet the exponential growth of mobile data traffic with low capital expenditure and operating expenditure. In addition, they are expected to work as a postdisaster wireless access network since exploiting green energy sources to power small cell base stations can overcome the power disruption caused by disasters. However, since the amount of harvested power is not enough for the stable network operation, the networks need to equipped with energy-efficient networking techniques. Additionally, in order to share the accurate postdisaster information, the networks should satisfy a certain quality of experience (QoE) of users in disaster areas. To deal with these challenges, we propose a user location maneuvering method, which advisory controls user's movement to improve the service time of EH-SCNs while guaranteeing a certain QoE level. We also propose an algorithm based on particle swarm optimization, which is configured with appropriate settings to find the best location of users efficiently. Extensive computer simulations show that the proposed maneuvering method can improve the QoE guaranteed service ratio, compared with the existing network operation approaches.

This paper develops a unified framework to maximize the network sum-rate in a multi-user, multi-BS downlink terahertz (THz) network by optimizing user associations, number and bandwidth of sub-bands in a THz transmission window (TW), bandwidth of leading and trailing edge-bands in a TW, sub-band assignment, and power allocations. The proposed framework incorporates multi-connectivity and captures the impact of molecular absorption coefficient variations in a TW, beam-squint, molecular absorption noise, and link blockages. To make the problem tractable, we first propose a convex approximation of the molecular absorption coefficient using curve fitting in a TW, determine the feasible bandwidths of the leading and trailing edge-bands, and then derive closed-form optimal solution for the number of sub-bands considering beam-squint constraints. We then decompose joint user associations, sub-band assignment, and power allocation problem into two sub-problems: 1) joint user association and sub-band assignment, and 2) power allocation. To solve the former problem, we analytically prove the unimodularity of the constraint matrix which enables us to relax the integer constraint without loss of optimality. To solve power allocation sub-problem, a fractional programming (FP)-based centralized solution as well as an alternating direction method of multipliers (ADMM)-based light-weight distributed solution is proposed. The overall problem is then solved using alternating optimization until convergence. Complexity analysis of the algorithms and numerical convergence are presented. Numerical findings validate the effectiveness of the proposed algorithms and extract useful insights about the interplay of the density of base stations (BSs), Average order of multi-connectivity (AOM), molecular absorption, hardware impairment, imperfect CSI, and link blockages.

To improve the users' satisfaction of downloading files, a cache based resource allocation algorithm in energy-harvesting small cell networks is proposed, which is established as a coalitional game and users try to switch between coalitions with satisfaction as utility function. The switch is accepted once the utilities of user and coalition after switching satisfies the switch orders. This algorithm also allows multi users to be served by a same small cell sharing the same channel using Non-orthogonal Frequency Division Multiple Access (NOMA). The simulation results show that our novel algorithm performs better on user satisfaction.

Integrated sensing and communication (ISAC) has been envisioned to play a more important role in future wireless networks. However, the design of ISAC networks is challenging, especially when there are multiple communication and sensing (C&S) nodes and multiple sensing targets. We investigate a multi-base station (BS) ISAC network in which multiple BSs equipped with multiple antennas simultaneously provide C&S services for multiple ground communication users (CUs) and targets. To enhance the overall performance of C&S, we formulate a joint user association (UA) and multi-BS transmit beamforming optimization problem with the objective of maximizing the total sum rate of all CUs while ensuring both the minimum target detection and parameter estimation requirements in terms of the radar signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB), respectively. To efficiently solve the highly non-convex mixed integer nonlinear programming (MINLP) optimization problem, we propose an alternating optimization (AO)-based algorithm that decomposes the problem into two sub-problems, i.e., UA optimization and multi-BS transmit beamforming optimization. Inspired by the huge potential of large language models (LLMs) for prediction and inference, we propose a unified framework integrating LLMs with convex-based optimization methods to benefit from the theoretical rigor and convergence guarantees of convex-based methods, and the adaptability and flexibility of LLMs. First, we propose a comprehensive design of prompt engineering based on in-context, few-shot, chain of thought, and self-reflection techniques to guide LLMs in solving the binary integer programming UA optimization problem. Second, we utilize convex-based optimization methods to handle the non-convex beamforming optimization problem based on fractional programming (FP), majorization minimization (MM), and the alternating direction method of multipliers (ADMM) with an optimized UA from LLMs. Numerical results demonstrate that our proposed LLM-enabled AO-based algorithm achieves fast convergence and near upper-bound performance with the GPT-o1 model, outperforming various benchmark schemes, which shows the advantages of integrating LLMs into convex-based optimization for wireless networks.

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

User association and resource allocation for downlink orthogonal frequency division multiplexing (OFDM) should be jointly optimized in small cells networks with limited backhaul in order to maximize the network utility. In general, the joint user association and per-band resource allocation problem is known as an integer NP-hard problem requiring exponential order of computational complexity to find the global optimal point. To obtain a solution with feasible computational complexity, most previous works solve this problem by relaxing the integer problem of the per-band resource allocation to a real-valued problem by assuming that channel gain is averaged out over all the frequency bands and that base stations allocate a real-valued proportion of the bandwidth. We propose two sub-optimal algorithms to solve the original integer problem with polynomial order of computational complexity in pursuit of maximizing the network utility with limited backhaul constraints. The network utility and geometric mean of receiving rates for the two proposed schemes are evaluated via numerical simulations. The computational complexity of the two schemes are also represented, respectively, showing the trade-off between the network utility and complexity.

In this paper, the problem of joint task, power, and spectrum allocation is studied for a mobile edge computing (MEC) based small cell network (SCN). In the considered network, each small base station (SBS) that is connected with an MEC server provides commutation service to users while each MEC server provides computation service to users, who will request various types of computational tasks including: collaboration computational task performed at both the user and the MEC sever, edge computational task processed at the MEC server, and local computational task implemented by user itself. As the data size of each computational task requested by each user varies, one must reconsider resource (transmit power and subcarrier) allocation and task partition to effectively service the users. We formulate this problem as an optimization problem aiming to minimize the transmission and computational delay. To solve this problem, a multiple stack Q-learning method is proposed. The proposed method can use multiple stacks to record the wireless environment and users' information so as to avoid repeatedly learning the same information and, thus improving the learning efficiency and speeding up the convergence. Simulation results show that the proposed resource allocation scheme provides 79\% and 63\% gains in terms of sum delay compared to cases that fully computed at user and fully computed at the MEC server, respectively.

Wireless network virtualization is a promising solution to improve spectrum efficiency. For a wireless downlink communication system with multiple mobile virtual network operators (MVNOs), we propose a decentralized blockchain-based dynamic spectrum acquisition scheme. Our proposed scheme aims to minimize the sum transmit power at all MVNOs while satisfying the average data transmission rate thresholds. For each MVNO, the required wireless spectrum to provide customized services to the mobile users (MUs) is predicted using the half-range Gauss-Hermite quadrature. Based on the predicted values, all the MVNOs carry out a blockchain-based distributed alternative direction method of multipliers to obtain the global optimal solution to the aforementioned sum transmit power minimization problem. To examine the effectiveness of our proposed scheme, with known system parameters, we also theoretically derive the semi-closed-form solution to the actually required sum transmit power minimization problem subject to data transmission rate constraints. Simulation results illustrate that our proposed dynamic spectrum acquisition scheme achieves almost the same minimum sum power as the non-causal scheme, which assumes the number of active MUs in all cells and all the channels are known non-causally for the optimal dynamic spectrum allocation.

This paper studies the case of an overlay cognitive radio network where the primary user leases spectral resources to the secondary user in exchange for cooperation, considering that both type of users have specific quality of service requirements. We investigate the problem of joint power and time allocation for the secondary access during the cooperative phase, with a view to optimizing the effective capacity of the primary user given an average energy constraint for the secondary user. Afterwards, the optimal power allocation of the secondary user for its own transmission phase is investigated in order to maximize the effective capacity of the secondary link. The proposed joint power and time allocation mechanism is compared with an optimal time/constant power allocation scheme and a less sophisticated baseline allocation scheme, i.e. power allocation under constant time and its superiority is proven for various network parameters. The reference model of one primary---one secondary user is extended to a general multi user cognitive radio network through the proposed pairing mechanism based on matching theory. Particularly, considering the remarks of the reference scenario, we propose two different matching schemes (with/without consideration of primary users' quality of service requirements) and we confirm their superiority compared to other matching mechanisms.

The alternating direction method of multipliers (ADMM) has been popular for solving many signal processing problems, convex or nonconvex. In this paper, we study an asynchronous implementation of ADMM for solving a nonconvex nonsmooth optimization problem, whose objective is the sum of a number of component functions. The proposed algorithm allows the problem to be solved in a distributed, asynchronous, and incremental manner. First, the component functions can be distributed to different computing nodes, which perform the updates asynchronously without coordinating with each other. Two sources of asynchrony are covered by our algorithm: One is caused by the heterogeneity of the computational nodes and the other arises from unreliable communication links. Second, the algorithm can be viewed as implementing an incremental algorithm where at each step the (possibly delayed) gradients of only a subset of component functions are updated. We show that when certain bounds are imposed on the level of asynchrony, the proposed algorithm converges to the set of stationary solutions (resp. optimal solutions) for the nonconvex (resp. convex) problem, with a global sublinear rate.

An efficient non-convex total variation approach for image deblurring and denoising

Dense small cell networks are expected to be deployed in the next generation wireless system to provide better coverage and throughput to meet the ever-increasing requirements of high data rate applications. As the trend toward densification calls for more and more wireless links to forward a massive backhaul traffic into the core network, it is critically important to take into account the presence of a wireless backhaul for the energy-efficient design of small cell networks. In this chapter, we develop a general framework to analyze the energy efficiency of a two-tier small cell network with massive MIMO macro base stations and wireless backhaul. Our analysis reveal that under spatial multiplexing, the energy efficiency of a small cell network is sensitive to the network load, and it should be taken into account when controlling the number of users served by each base station. We also demonstrate that a two-tier small cell network with wireless backhaul can be significantly more energy efficient than a one-tier cellular network. However, this requires the bandwidth division between radio access links and wireless backhaul to be optimally designed according to the load conditions.

For 5G wireless communication networks, low latency and high spectral efficiency are required to provide ubiquitous communication for emerging applications. In this letter, we propose a novel framework which considers these requirements simultaneously by integrating the notion of effective capacity (EC) and non-orthogonal multiple access (NOMA) into virtualized wireless networks (VWNs). At first, we consider a virtual base station architecture to achieve isolation between different service providers (slices). Then, a resource allocation design for maximization of the EC is formulated as a non-convex optimization problem which takes into account the average maximum power constraints and delay quality-of-service requirements. To strike a balance between computational complexity and system performance, we apply the successive convex approximation to the non-convex optimization problem. Subsequently, an iterative low-complexity suboptimal algorithm is proposed. Numerical results illustrate that there is a non-trivial fundamental tradeoff between delay and spectral efficiency in VWNs while NOMA provides a significant improvement in terms of EC compared to conventional orthogonal multiple access.

With the deployment of small cell networks, low-cost backhaul schemes for small cell base stations (SBSs) have attracted great attentions. Self-backhaul using cellular communication technology is considered as a promising solution. Although some excellent works have been done on self-backhaul in small cell networks, most existing works do not consider the recent advances of full-duplex (FD) and massive multiple-input and multiple-output (MIMO) technologies. By jointly considering them, we propose a novel self-backhaul scheme for small cell networks in this paper, where the macro base station is equipped with massive MIMO antennas, and the SBSs have the FD communication capability. Then, we can achieve the simultaneous transmissions of the access link of users and the backhaul link of SBSs in the same frequency and then spectrum efficiency is improved. Furthermore, we formulate the power allocation problem of the MBS and SBSs as an optimization problem. Because the formulated power allocation problem is a non-convex problem, we transfer the original problem into a difference of convex program by using successive convex approximation method and variable substitution, and then solve it using a constrained concave convex procedure based iterative algorithm. Finally, extensive simulations are conducted to verify the effectiveness of the proposed scheme.