Game theory and power control in ultrawideband networks

This paper describes a theoretical framework for the design and analysis of power control algorithms for high-throughput wireless networks using ultrawideband (UWB) technologies. The tools of game theory are shown to be expedient for deriving scalable, energy-efficient, distributed power control schemes to be applied to a population of battery-operated user terminals in a rich multipath environment. In particular, the power control issue is modeled as a dynamic noncooperative game in which each user chooses its transmit power so as to maximize its own utility, which is defined as the ratio of throughput to transmit power. Although distributed (noncooperative) control is known to be suboptimal with respect to the optimal centralized (cooperative) solution, it is shown via large-system analysis that the game-theoretic distributed algorithm based on Nash equilibrium exhibits negligible performance degradation with respect to the centralized socially optimal configuration. The framework described here is general enough to also encompass the analysis of code division multiple access (CDMA) systems and to show that UWB slightly outperforms CDMA in terms of achieved utility at the Nash equilibrium.     

Price-based interference control for two-tier femtocell networks

This paper presents a novel price-based interference control scheme for two-tier femtocell networks, aiming to limit the interference from femtocell users to macrocell base station (MBS). Assuming that the MBS protects itself by pricing the interference power from the femtocell users, the femtocell users set their transmission powers by competitively selecting the interference power fractions under the constraint of the total tolerable interference. The problem of femtocell users’ competitive interference occupation process is cast into a non-cooperative interference power purchase game, and the existence and uniqueness of the Nash equilibrium is proved. Then, a distributed interference power fraction iterative algorithm is developed to find the Nash equilibrium of the game, and the convergence analyses in both synchronous and asynchronous cases are presented. The distributed implementations are also shown. Simulation results show the convergence of the interference power fraction iterative algorithm and the effectiveness of the proposed interference control scheme.     

A noncooperative power control game in multi-access fading channels with quality of service (QoS) constraints

In this paper, a game-theoretic analysis for the resource allocation policies in fading multiple-access channels (MACs) in the presence of statistical quality of service (QoS) constraints in the form of limitations on the buffer length is performed. We employ effective capacity, which provides the maximum constant arrival rate that a given process can support while satisfying statistical buffer constraints, to measure the throughput for each user. We assume that the channel side information (CSI) is available at both the receiver and the transmitters, and the transmitters are selfish, rational with certain QoS and average power constraints. Without the aid of the receiver, we show that there is always a unique admissible Nash equilibrium of the noncooperative power control game, for which numerical results at low signal-to-noise ratios (SNRs) have been provided. The Nash equilibrium of the power control game is proved to be always inside the rate region where successive decoding techniques are used at the receiver.     

Leakage-based beamforming via game theory for reverse spectrum auction in cellular offloading systems

The explosion of mobile traffic and highly dynamic property often make it increasingly stressful for a cellular service provider to provide sufficient cellular spectrum resources to support the dynamic change of traffic demand in a day. In this paper, considering the dynamic characteristic of the cellular network traffic demand, we not only proposed an optimal, truthful reverse auction incentive framework, but also proposed a valuation function which is based on third-party access points’ capacity. We consider spectrum sharing in a third-party network where several secondary users (SUs) share spectrum with a primary user (PU). A leakage-based beamforming algorithm is proposed via game theory to maximize the sum utility of third-party access points subject to the signal-to-leakage-and-noise (SLNR) constraint of SUs and PU interference constraint. The sum throughput maximization problem is formulated as a non-cooperative game, where the SUs compete with each other over the resources. Nash equilibrium is considered as the solution of this game. Simulation results show that the proposed algorithm can achieve a high sum throughput and converge to a locally optimal beamforming vector.     

Quantum Prisoners’ Dilemma in Fluctuating Massless Scalar Field

Quantum systems are easily affected by external environment. In this paper, we investigate the influences of external massless scalar field to quantum Prisoners’ Dilemma (QPD) game. We firstly derive the master equation that describes the system evolution with initial maximally entangled state. Then, we discuss the effects of a fluctuating massless scalar field on the game’s properties such as payoff, Nash equilibrium, and symmetry. We find that for different game strategies, vacuum fluctuation has different effects on payoff. Nash equilibrium is broken but the symmetry of the game is not violated.     

A DEC-MDP model for joint uplink/downlink resource management in OFDMA-based networks

With the advent of mobile services with asymmetric and symmetric quality of service (QoS) requirements, traditional single link resource allocation techniques have started to show some limitations in handling the complex requirements. To address these issues, joint uplink/downlink resource management approaches were recently introduced where both communications links are jointly considered in the resource management process. One direct consequence of this coupling is a modification of the underlying queueing behavior since the decision making process in one direction in terms of transmission rate now depends on the performance achieved in the opposite direction. In this paper, we present a modeling approach of the decision making process that takes place under the joint uplink/downlink resource management framework. Using decentralized Markov decision processes (DEC-MDP) as a model and gradient ascent methods as an optimization technique, we formulate and solve the joint uplink/downlink decision making process. The uplink and downlink of each user are considered as agents. Assuming certain subcarrier and power allocation schemes, we investigate the resource usage in the uplink and downlink to achieve a certain delay balancing constraint where the total delay in the uplink and downlink is bound by a pre-determined threshold. The approach followed starts by modeling the problem in hand using DEC-MDPs. After discussing the different aspects of the model, the solution using gradient ascent is described. Simulation results illustrate the different dimensions of the problem and their impact on the resource management process.     

基于博弈论的认知无线电网络跨层资源分配

针对多跳认知无线电网络的多层资源分配问题,提出了协作去耦合方法和跨层联合方法,协作去耦合方法首先单独完成路径选择任务,随后进行信道与功率的博弈分配;跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配,两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则,引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择,设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择,设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能,从促进收敛角度,选择序贯博弈并设计了具体的博弈过程,此外还分析了博弈的纳什均衡,讨论了两种算法的复杂度,仿真结果表明,协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法。     

Transmit power and bits/channel use adaption in competitive cognitive radio networks

In this paper, we propose an optimization framework to determine the distribution of power and bits/channel use to secondary users in a competitive cognitive radio networks. The objectives of the optimization framework are to minimize total transmission power, maximize total bits/channel use and also to maintain quality of service. An upper bound on probability of bit error and lower bound on bits/channel use requirement of secondary users are considered as quality of service. The optimization problem is also constrained by total power budget across channels for a user. Simulating the framework in a centralized manner shows that more transmit power is required to allocate in a channel with higher noise power. However, allocation of bits/channel use is directly proportional to signal to interference plus noise power ratio. The proposed framework is more capable of supporting high bits/channel use requirement than existing resource allocation framework. We also develop the game theoretic user based distributed approach of the proposed framework. We see that user based distributed solution also follows centralized solution.     

Joint uplink and downlink scheduling and UAV trajectory design in the presence of multiple unfriendly jammers and eavesdroppers

In this paper, we investigate secure uplink and downlink communications between an unmanned aerial vehicle (UAV) and multiple user equipments (UEs) in the presence of multiple ground-based eavesdroppers (EVs) and unfriendly jammers. In order to guarantee the secure uplink and downlink transmissions, we consider a novel secure transmission scheme, which involves a power splitting based downlink transmission and scheduling of uplink and downlink transmission. Explicitly, we aim to maximize the average secrecy rate (ASR) by optimizing the UAV trajectory, the transmit power of the UAV and UEs, and scheduling of uplink and downlink transmission. Although the formulated problem is nonconvex, we propose an efficient solution by jointly applying the techniques of block coordinate descent and successive convex approximation. Numerical results show that the proposed scheme achieves a better ASR than the benchmark schemes.     

Delay-Sensitive NOMA-HARQ for Short Packet Communications

This paper investigates the two-user uplink non-orthogonal multiple access (NOMA) paired with the hybrid automatic repeat request (HARQ) in the finite blocklength regime, where the target latency of each user is the priority. To limit the packet delivery delay and avoid packet queuing of the users, we propose a novel NOMA-HARQ approach where the retransmission of each packet is served non-orthogonally with the new packet in the same time slot. We use a Markov model (MM) to analyze the dynamics of the uplink NOMA-HARQ with one retransmission and characterize the packet error rate (PER), throughput, and latency performance of each user. We also present numerical optimizations to find the optimal power ratios of each user. Numerical results show that the proposed scheme significantly outperforms the standard NOMA-HARQ in terms of packet delivery delay at the target PER.     

Statistical physics of the spatial Prisoner’s Dilemma with memory-aware agents

We introduce an analytical model to study the evolution towards equilibrium in spatialgames, with ‘memory-aware’ agents, i.e., agents that accumulate their payoff over time. Inparticular, we focus our attention on the spatial Prisoner’s Dilemma, as it constitutes anemblematic example of a game whose Nash equilibrium is defection. Previous investigationsshowed that, under opportune conditions, it is possible to reach, in the evolutionaryPrisoner’s Dilemma, an equilibrium of cooperation. Notably, it seems that mechanisms likemotion may lead a population to become cooperative. In the proposed model, we map agentsto particles of a gas so that, on varying the system temperature, they randomly move. Indoing so, we are able to identify a relation between the temperature and the finalequilibrium of the population, explaining how it is possible to break the classical Nashequilibrium in the spatial Prisoner’s Dilemma when considering agents able to increasetheir payoff over time. Moreover, we introduce a formalism to study order-disorder phasetransitions in these dynamics. As result, we highlight that the proposed model allows toexplain analytically how a population, whose interactions are based on the Prisoner’sDilemma, can reach an equilibrium far from the expected one; opening also the way todefine a direct link between evolutionary game theory and statistical physics.     

认知无线网络中基于Sigmoid函数的功率控制算法研究

为了实现认知无线网络中频谱分配公平性以及契合现代化绿色通信的需求,根据非合作博弈论和干扰温度,引入信道状态概念,设计出一种新型功率控制算法,分析了该算法的收敛性、纳什均衡解的存在性和唯一性。该算法不仅可以快速收敛,符合实时通信,而且分布式实施,简单实用。仿真结果表明,相比其他算法,该算法系统干扰小,能源消耗低,具有抗干扰性能,而且在日益多用户网络的情况下,具有低功率、低干扰,提高网络的整体效益,更加符合现代化的绿色通信的需求。     

Cooperative diversity using per-user power control in the MAC channel

We consider a multiple access MAC fading channel with two users communicating with a common destination, where each user mutually acts as a relay for the other one as well as wishes to transmit its own information as opposed to having dedicated relays. We wish to evaluate the usefulness of relaying from the point of view of the system’s throughput (sum rate) rather than from the sole point of view of the user benefiting from the cooperation as is typically done. We do this by allowing a trade-off between relaying and fresh data transmission through a resource allocation framework. Specifically, We propose a cooperative transmission scheme allowing each user to allocate a certain amount of power for its own transmitted data while the rest is devoted to relaying. The underlying protocol is based on a modification of the so-called non-orthogonal amplify-and-forward (NAF) protocol Azarian et al. [18]. We develop capacity expressions for our scheme and derive the rate-optimum power allocation, in closed form for centralized and distributed frameworks. In the distributed scenario, partially statistical and partially instantaneous channel information is exploited.The centralized power allocation algorithm indicates that even in a mutual cooperation setting like ours, on any given realization of the channel, cooperation is never truly mutual, i.e. one of the users will always allocate zero power to relaying the data of the other one, and thus act selfishly. But in a distributed framework, our results indicate that the sum rate is maximized when both mobiles act selfishly.     

Latency optimization for Federated Learning over Wireless Power Transfer

Federal Learning (FL) is an emerging technology in the field of machine learning (ML). Compared with traditional ML, FL is an attractive method to deal with data security issues of the user-side. So that FL can realizes its full potential in terms of low latency and high energy efficiency (EE), this paper introduces a new framework: In the wireless communication network scenario, we propose an FL architecture based on Wireless Power Transfer (WPT). By combining WPT technology and FL, we can realize green wireless communication under the premise of ensuring user privacy. We formulate a joint calculation and communication optimization problem to optimize the latency of local calculation, uplink and downlink transmission without consuming user-side energy. The problem formulas listed according to the optimization problem are non-convex. They are first transformed into convex form, and then a low-complexity iterative algorithm is used to solve them optimally. Simulations show that our proposed FL method design has achieved a significant performance improvement over other benchmark tests.     

Entanglement and dynamic stability of Nash equilibria in a symmetric quantum game

We study the evolutionary stability of Nash equilibria (NE) in a symmetric quantum game played by the recently proposed scheme of applying ‘identity’ and ‘Pauli spin-flip’ operators on an initial state with classical probabilities. We show that in this symmetric game dynamic stability of a NE can be changed when the game changes its form, for example, from classical to quantum. It happens even when the NE remains intact in both forms.     

Adaptive scheduling algorithms for multimedia traffic in wireless OFDMA systems

This paper deals with the problem of finding an optimal subcarrier allocation strategy for uplink and downlink communications in an OFDMA metropolitan wireless system. Three different resource allocation algorithms are proposed by taking into account the Quality of Service (QoS) constraints and the user’s channel conditions. Moreover, a simple strategy for choosing the most appropriate Modulation and Coding Scheme (MCS) according to the channel conditions of the assigned subcarriers is also considered. Finally, this paper studies the possibility of applying an adaptive Time Division Duplexing (TDD) separation of the uplink and downlink frame, based on the instantaneous load of the network. The performance of the proposed algorithms have been derived and compared in terms of error rate and throughput. All proposed methods show a significant improvement of the overall system performance in comparison with the static subcarrier allocation in which channel information has not taken into account. Moreover, additional improvements have been obtained by applying to these schemes the proposed adaptive TDD frame separation strategy.     

The rock–paper–scissors game

Rock–Paper–Scissors (RPS), a game of cyclic dominance, is not merely a popular children’s game but also a basic model system for studying decision-making in non-cooperative strategic interactions. Aimed at students of physics with no background in game theory, this paper introduces the concepts of Nash equilibrium and evolutionarily stable strategy, and reviews some recent theoretical and empirical efforts on the non-equilibrium properties of the iterated RPS, including collective cycling, conditional response patterns and microscopic mechanisms that facilitate cooperation. We also introduce several dynamical processes to illustrate the applications of RPS as a simplified model of species competition in ecological systems and price cycling in economic markets.     

Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network(WSN), the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption(PCCAGM). This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption(PCCAA) is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.     

Waveform contenders for 5G: Description,analysis and comparison

5G will have to cope with a high degree of heterogeneity in terms of services and requirements. Among these latter, flexible and efficient use of all available non-contiguous spectra for different network deployment scenarios is one challenge for the future 5G. To maximize spectrum efficiency, a flexible 5G air interface technology capable of mapping various services to the best suitable combinations of frequency and radio resources will also be required. In this work, a fair comparison of several 5G waveform candidates (UFMC, FBMC-OQAM, and FBMC-QAM) is proposed under a common framework. Spectral efficiency, power spectral density, peak to average power ratio and performance in terms of bit error rate under various realistic channel conditions are assessed. The waveforms are then compared in an asynchronous multi-user uplink transmission. Based on these results, in order to increase the spectral efficiency, a bit loading algorithm is proposed to cope with the non-uniform distribution of the interference across the carriers. The benefits of these new waveforms for the foreseen 5G use cases are clearly highlighted. It is also stressed that some concepts still need to be improved to achieve the full range of expected benefits of 5G.     

Evolutionarily stable strategies in quantum games

Evolutionarily stable strategy (ESS) in classical game theory is a refinement of Nash equilibrium concept. We investigate the consequences when a small group of mutants using quantum strategies try to invade a classical ESS in a population engaged in symmetric bimatrix game of prisoner's dilemma. Secondly we show that in an asymmetric quantum game between two players an ESS pair can be made to appear or disappear by resorting to entangled or unentangled initial states used to play the game even when the strategy pair remains a Nash equilibrium in both forms of the game.