Spectrum efficient power allocation schemes for OFDM cognitive radio with statistical interference constraints

In this paper, we study the power allocation problem for an orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) system. In a departure from the conventional power allocation schemes available in the literature for OFDM-based CR, we propose power allocation schemes that are augmented with spectral shaping. Active interference cancellation (AIC) is an effective spectral shaping technique for OFDM-based systems. Therefore, in particular, we propose AIC-based optimal and suboptimal power allocation schemes that aim to maximize the downlink transmission capacity of an OFDM-based CR system operating opportunistically within the licensed primary users (PUs) radio spectrum in an overlay approach. Since the CR transmitter may not have the perfect knowledge about the instantaneous channel quality between itself and the active PUs, the interference constraints imposed by each of the PUs are met in a statistical sense. We also study an optimal power allocation scheme that is augmented with raised cosine (RC) windowing-based spectral shaping. For a given power budget at the CR transmitter and the prescribed statistical interference constraints by the PUs, we demonstrate that although the on-the-run computational complexity of the proposed AIC-based optimal power allocation scheme is relatively higher, it may yield better transmission rate for the CR user compared to the RC windowing-based power allocation scheme. Further, the AIC-based suboptimal scheme has the least on-the-run computational complexity, and still may deliver performance that is comparable to that of the RC windowing-based power allocation scheme. The presented simulation results also show that both the AIC-based as well as the RC windowing-based power allocation schemes lead to significantly higher transmission rates for the CR user compared to the conventional (without any spectral shaping) optimal power allocation scheme.     

An approximation for the boundary optimal control problem of a heat equation defined in a variable domain

In this paper, we consider a numerical approximation for the boundary optimal control problem with the control constraint governed by a heat equation defined in a variable domain. For this variable domain problem, the boundary of the domain is moving and the shape of theboundary is defined by a known time-dependent function. By making use of the Galerkin finite element method, we first project the original optimal control problem into a semi-discrete optimal control problem governed by a system of ordinary differential equations. Then, based on the aforementioned semi-discrete problem, we apply the control parameterization method to obtain an optimal parameter selection problem governed by a lumped parameter system, which can be solved as a nonlinear optimization problem by a Sequential Quadratic Programming （SQP） algorithm. The numerical simulation is given to illustrate the effectiveness of our numerical approximation for the variable domain problem with the finite element method and the control parameterization method.     

Bandwidth allocation strategy for traffic systems of scale-free network

In this Letter, the bandwidth resource allocation strategy is considered for traffic systems of complex networks. With a finite resource of bandwidth, an allocation strategy with preference parameter α is proposed considering the links importance. The performance of bandwidth allocation strategy is studied for the local routing protocol and the shortest path protocol. When important links are slightly favored in the bandwidth allocation, the system can achieve the optimal traffic performance for the two routing protocols. For the shortest path protocol, we also give a method to estimate the network traffic capacity theoretically.     

Reinforcement Learning-Based Multihop Relaying: A Decentralized Q-Learning Approach

Conventional optimization-based relay selection for multihop networks cannot resolve the conflict between performance and cost. The optimal selection policy is centralized and requires local channel state information (CSI) of all hops, leading to high computational complexity and signaling overhead. Other optimization-based decentralized policies cause non-negligible performance loss. In this paper, we exploit the benefits of reinforcement learning in relay selection for multihop clustered networks and aim to achieve high performance with limited costs. Multihop relay selection problem is modeled as Markov decision process (MDP) and solved by a decentralized Q-learning scheme with rectified update function. Simulation results show that this scheme achieves near-optimal average end-to-end (E2E) rate. Cost analysis reveals that it also reduces computation complexity and signaling overhead compared with the optimal scheme.     

Joint optimization of component carrier selection and resource allocation in 5G carrier aggregation system

In this paper, we consider joint optimization of Component Carrier (CC) selection and resource allocation in 5G Carrier Aggregation (CA) system. Firstly, the upper-bound system throughput with determined number of CCs is derived and it is proved by using graph theory that the throughput optimization problem is NP hard. Then we propose a greedy based algorithm to solve this problem and prove that the proposed algorithm can achieve at least 1/2 of the optimal performance in the worst case. At last, we evaluate the throughput and computational complexity performance through a variety of simulations. Simulation results show that the proposed algorithm can obtain better performance comparing with existing schemes while keeping the computation complexity at an acceptable level.     

Macrocell–femtocells resource allocation with hybrid access motivational model

Femtocell technology has emerged as an efficient cost-effective solution not only to solve the indoor coverage problem but also to cope with the growing demand requirements. This paper investigates two major design concerns in two tier networks: resource allocation and femtocell access. Base station selection together with dual bandwidth and power allocation among the two tiers is investigated under shared spectrum usage. To achieve fair and efficient resource optimization, our model assumes that the hybrid access mode is applied in the femtocells. The hybrid access mode is beneficial for system performance as (1) it lessens interference caused by nearby public users, (2) it allows public users to connect to near femtocells and get better Quality of Service (QoS) and (3) it increases system capacity as it allows the macrocell to serve more users. However, femtocells’ owners can behave selfishly by denying public access to avoid any performance reduction in subscribers’ transmissions. Such a problem needs a motivation scheme to assure the cooperation of femtocells’ owners. In this paper, we propose a game-theoretical hybrid access motivational model. The proposed model encourages femtocells’ owners to share resources with public users, thus, more efficient resource allocation can be obtained. We optimize the resource allocation by means of the Genetic Algorithm (GA). The objective of the formulated optimization problem is the maximization of network throughput that is calculated by means of Shannon’s Capacity Law. Simulations are conducted where a modified version of the Weighted Water Filling (WWF) algorithm is used as a benchmark. Our proposed model, compared to WWF, achieves more efficient resource allocation in terms of system throughput and resources utilization.     

Intelligent radio resource management in reconfigurable IRS-enabled NOMA networks

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyze the proposed approach against a new performance metric called computational efficiency (CE).     

Power allocation for multi-user OFDM-DCSK system in frequency selective fading channel

In this paper, we introduce a grouping approach for power allocation in the multi-user OFDM-DCSK (MU-OFDM-DCSK) system under the frequency selective fading channels. The suggested procedure is convenient also for the other comb-type non-coherent schemes with similar structure. Furthermore, we derive analytical bit error rate (BER) expression for the grouped scheme and offer an optimal power distribution policy for both the single- and multi-user scenarios. This power assignment strategy is formulated by a min–max problem with the target of the worst group BER minimization incorporating total power and interference constraints. Simulation results confirm the advantages of the proposed power allocation scheme.     

Channel indicator-based intelligent network selection scheme for device-to-device communication

Device-to-device (D2D) communication has captured the researchers attention working in data-intensive applications. It has various benefits, such as low communication latency, load balancing, high spectral efficiency, and many more. However, despite these benefits, it has significant issues like efficient resource allocation, device discovery, and interference mitigation. Various solutions have given by the researchers to tackle these issues and the research community accepts them well. Here, we are targeting the issues associated with the device discovery, i.e., the base station assisted discovery. The initial step for D2D communication is the device discovery that the base station can perform. But, if the channel indicator parameters of the base station are not good, then the device discovery and further data sharing will be affected. Thus, there is a need for the best base station selection that improves the efficiency of the overall network. Many network selection solutions (for cellular networks) are available in the literature, but none of it talked about in the D2D communication scenario. So, motivated by this, this paper proposes an AI-based intelligent and efficient network selection scheme for D2D users to improve the device discovery experience and overall system’s sum rate. We then evaluate the performance of the proposed scheme using various evaluation metrics, such as accuracy, precision, recall, receiver operating curve (ROC), computation time, and sum rate.     

Adaptive extremal optimization by detrended fluctuation analysis

Global optimization is one of the key challenges in computational physics as several problems, e.g. protein structure prediction, the low-energy landscape of atomic clusters, detection of community structures in networks, or model-parameter fitting can be formulated as global optimization problems. Extremal optimization (EO) has become in recent years one particular, successful approach to the global optimization problem. As with almost all other global optimization approaches, EO is driven by an internal dynamics that depends crucially on one or more parameters. Recently, the existence of an optimal scheme for this internal parameter of EO was proven, so as to maximize the performance of the algorithm. However, this proof was not constructive, that is, one cannot use it to deduce the optimal parameter itself a priori. In this study we analyze the dynamics of EO for a test problem (spin glasses). Based on the results we propose an online measure of the performance of EO and a way to use this insight to reformulate the EO algorithm in order to construct optimal values of the internal parameter online without any input by the user. This approach will ultimately allow us to make EO parameter free and thus its application in general global optimization problems much more efficient.     

Adaptive extremal optimization by detrended fluctuation analysis

Global optimization is one of the key challenges in computational physics as several problems, e.g. protein structure prediction, the low-energy landscape of atomic clusters, detection of community structures in networks, or model-parameter fitting can be formulated as global optimization problems. Extremal optimization (EO) has become in recent years one particular, successful approach to the global optimization problem. As with almost all other global optimization approaches, EO is driven by an internal dynamics that depends crucially on one or more parameters. Recently, the existence of an optimal scheme for this internal parameter of EO was proven, so as to maximize the performance of the algorithm. However, this proof was not constructive, that is, one cannot use it to deduce the optimal parameter itself a priori. In this study we analyze the dynamics of EO for a test problem (spin glasses). Based on the results we propose an online measure of the performance of EO and a way to use this insight to reformulate the EO algorithm in order to construct optimal values of the internal parameter online without any input by the user. This approach will ultimately allow us to make EO parameter free and thus its application in general global optimization problems much more efficient.     

Intelligent Reflecting Surface Assisted Multi-User Robust Secret Key Generation for Low-Entropy Environments

Channel secret key generation (CSKG), assisted by the new material intelligent reflecting surface (IRS), has become a new research hotspot recently. In this paper, the key extraction method in the IRS-aided low-entropy communication scenario with adjacent multi-users is investigated. Aiming at the problem of low key generation efficiency due to the high similarity of channels between users, we propose a joint user allocation and IRS reflection parameter adjustment scheme, while the reliability of information exchange during the key generation process is also considered. Specifically, the relevant key capability expressions of the IRS-aided communication system is analyzed. Then, we study how to adjust the IRS reflection matrix and allocate the corresponding users to minimize the similarity of different channels and ensure the robustness of key generation. The simulation results show that the proposed scheme can bring higher gains to the performance of key generation.     

Joint MMSE designs for analog network coding and different MIMO relaying schemes: A unified approach and performance benchmarks

To gain understanding of the analog network coding and different MIMO relaying schemes and to facilitate the scheme selection, there is a need for a unified approach and performance benchmarks. In this paper, analog network coding, direct transmission, Two-Hop relaying, and cooperative relaying schemes are analyzed and compared. A unified approach, the novel generalized iterative approach, is proposed for jointly designing the MMSE MIMO processors (including precoders, relay processors and decoders) for these schemes. Numerical results show that for each scheme, there exist system and channel parameter regime(s) where it is the most desirable among the four schemes. Performance benchmarks, physical insights and some guidelines for MIMO relaying scheme selection are presented.     

Distributed spatial modulation with dynamic frequency allocation

This paper proposes a distributed implementation of spatial modulation (SM) using cognitive radios. In distributed spatial modulation (DSM), multiple relays form a virtual antenna array and assist a source to transmit its information to a destination. The source broadcasts its signal, which is independently demodulated by all the relays. Each of the relays then divides the received data in two parts: the first part is used to decide which one of the relays will be active, and the other part decides what data it will transmit to the destination. An analytical expression for symbol error probability is derived for DSM in independent and identically distributed (i.i.d.) Rayleigh fading channels. The analytical results are later compared with Monte Carlo simulations. Further, an instantaneous symbol error rate (SER) based selection combining is proposed to incorporate the direct link between the source and destination with existing DSM. Next, DSM implementation is extended to a cognitive network scenario where the source, relays, and destination are all cognitive radios. A dynamic frequency allocation scheme is proposed to improve the performance of DSM in this scenario. The frequency allocation is modeled through a bipartite graph with end-to-end SER as a weight function. The optimal frequency allocation problem is formulated as minimum weight perfect matching problem and is solved using the Hungarian method. Finally, numerical results are provided to illustrate the efficacy of the proposed scheme.     

A joint resource allocation method for hybrid NOMA MEC offloading

In this article, a joint resource allocation of power, time, and sub-channels that minimizes the total energy consumption of users for Hybrid NOMA MEC Offloading is proposed. By formulating and solving the joint optimization problem, first we propose a novel optimal Hybrid NOMA scheme referred to as Switched Hybrid NOMA (SH-NOMA) for power and time allocation. Subsequently, we address sub-channel allocation as a three-dimensional assignment problem, and propose the Total-Reward Exchange Stable (TES) algorithm to solve it. Analytically, we show that SH-NOMA is more energy efficient than the Hybrid NOMA scheme in the literature and that the TES algorithm converges to a solution with less energy consumption than the widely used two-sided exchange stable algorithm. Finally, via simulations we demonstrate that the proposed methods outperform the results in the literature.     

基于谱线频率特征的调谐二极管激光吸收光谱参数分析

调谐激光二极管吸收光谱(TDLAS)技术因其高分辨率、高灵敏度和快速测量等优点在工业生产、环境污染监测等方面受到广泛应用。波长调制光谱（wavelength modulation spectrum, WMS）的二次谐波信号经常用作气体浓度反演的检测信号。TDLAS检测性能与系统参数,如锁相放大器的时间常数、扫描幅度、扫描频率、调制幅度、调制频率等的选取紧密相关,实际测量中各参数的选择多以谱线形态特征为依据,参数之间的关联性未能得到较好体现。由于信号的采样与处理均在频域对谱线产生作用,各参数之间的作用相互关联。然而很少有研究参数对谱线频域的影响,针对此问题,在一定的理论基础上通过实验分别观察各调制参数对二次谐波信号的影响。通过保持其他参数不变,只改变一个参数的方法,得出各个参数对信号线型、频率特征及噪声引入的影响规律,继而分析并验证了多参数联合变化对谱线频带的决定作用。与基于时域特征的传统方法相比,基于谱线频率特征分析一方面具有与谱线信号采集检测处理机理相近的优点,另一方面可以直观得到各参数对主频带的影响和不同频率信号的衰减趋势。总结出基于频率特征的各参数的基本选取方法,以谱线频带和截止频率相互关系为判定标准,截止频率的大小由锁相放大器时间常数决定。通过设置合适的时间常数和扫描参数使信号频带与截止频率相近但不相交,使谱线频带内频率分量不产生衰减,频带外噪声得到最大抑制;再根据锁相放大器的性能和信号信噪比来确定调制参数,使谱线主频幅度最大;最后根据系统需求确定采样率。单周期采样点不变时,低扫描频率时检测精度相对提高但耗时较长;反之,扫描频率提高,速度变快但检测精度下降。通过联合影响规律调整关联参数,减小硬件限制对参数最优值选取造成的影响。可在考虑系统检测需求与硬件条件限制的前提下,通过参数选择得到最优二次谐波信号,为此技术的实际应用提供了参数优化的实验依据与参考方法。     

简谐振子免疫优化算法求解异构无线网络垂直切换判决问题

本文设计了垂直切换判决方案问题的数学模型, 给出了一种基于简谐振子免疫优化算法的垂直切换判决方案, 并与文献方案进行了对比实验实验结果表明, 本文方案能够有效地平衡网络负载、增加终端电池的生存时间, 具有较好的应用价值.     

Numerical Study on the Performance in High bit rate Optical Communication Systems

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Numerical Study on the Performance in High-bit-rate Optical Communication Systems

By numerical simulations, we show that picosecond Gaussian optical pulses with a precise optimal frequency chirping can transmit stably in full-dispersion compensation optical fiber links, with not only second- but also third-order dispersion compensation, using dispersion shift fibers with opposite dispersion sign. The optimal pre-chirp is determined principally by the second-order dispersion scheme and scarcely affected by third-order dispersion scheme. It demonstrates that, to a high bit rate transmission system, the pre-chirping technology and higher-order dispersion compensation are two very efficient measures in improving performance of system.     

Deep transfer reinforcement learning for resource allocation in hybrid multiple access systems

This paper proposes a resource allocation scheme for hybrid multiple access involving both orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The proposed resource allocation scheme employs multi-agent deep reinforcement learning (MA-DRL) to maximize the sum-rate for all users. More specifically, the MA-DRL-based scheme jointly allocates subcarrier and power resources for users by utilizing deep Q networks and multi-agent deep deterministic policy gradient networks. Meanwhile, an adaptive learning determiner mechanism is introduced into our allocation scheme to achieve better sum-rate performance. However, the above deep reinforcement learning adopted by our scheme cannot optimize parameters quickly in the new communication model. In order to better adapt to the new environment and make the resource allocation strategy more robust, we propose a transfer learning scheme based on deep reinforcement learning (T-DRL). The T-DRL-based scheme allows us to transfer the subcarrier allocation network and the power allocation network collectively or independently. Simulation results show that the proposed MA-DRL-based resource allocation scheme can achieve better sum-rate performance. Furthermore, the T-DRL-based scheme can effectively improve the convergence speed of the deep resource allocation network.