Top PDF Optimization Algorithms in Wireless and Quantum Communications

Optimization Algorithms in Wireless and Quantum Communications

Optimization Algorithms in Wireless and Quantum Communications

As we have said, the multiple antennas can be employed in the so-called point-to-point communication. Point-to-point communication assumes that there are two ends of com- munication, the transmitter and the receiver. Furthermore the assumption in the context of wireless communications is that they communicate through wireless medium. Another interesting concept of communicating that wireless medium allows is called broadcast com- munication. In broadcast communication one transmitter (say a base station) broadcasts the information that can be received by different users (see Figure 1.2). In the literature this system is often called a broadcast channel. Similar to point-to-point communication, in broadcast communication the transmitter and the receivers can be equipped with multiple antennas. The main idea is that if the transmitter is equipped with, say, m antennas, it can simultaneously serve m users. However, since the transmission medium is transparent for all signals, parts of the information intended for one of the users will reach users that are not intended to receive it. The piece of non-intended information which reaches receivers may cause them to incorrectly detect the information intended for them. This problem (commonly known as interference) is caused by the fading nature of the wireless broadcast medium and is one of the main issues in wireless broadcast systems. The problem of design- ing the information symbol vectors at the transmitter so that the problem of interference among the users is as mitigated as possible has been extensively studied during the last decade. Significant theoretical results related to the performance limits of broadcast chan- nels have been achieved. Surprisingly, the fading characteristics of the channel can in fact be utilized so that the expected increase in the overall amount of information that can be reliably transmitted is achieved.
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Resource Management for Wireless Systems under Assignment Methods and Optimization Algorithms: Survey

Resource Management for Wireless Systems under Assignment Methods and Optimization Algorithms: Survey

Optimization algorithms on channel assignment and scheduling focus on improving the quality of service by controlling media access, like wireless link access [3, 12, 13, 14, 15, 16], channel usage [1], etc. A notable scheduling policy is the well-known throughput optimal scheduling [30]: Maximal Matching Scheduling, which is used by many existing works like [2, 7, 8] and [18]. The capacity of wireless networks can be greatly extended by using multi-channel multi-radio (MC-MR) interfaces. Each radio can switch between several orthogonal channels to avoid interference. The work of [1] proposes a robust resource provision channel assignment algorithm for MR-MC wireless networks. The solution provides guaranteed QoS under channel variability and external interference. The Max-Weight algorithm [17] is a popular scheduling strategy, which determines the transmission priority based on the product of queue length and current channel rate.Max-Weight involves both user demands and channel variation, and was considered to provide throughput optimality. However, the work in [19] proves that Max-Weight is not always throughput optimal under certain flow-level dynamics.
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THE POPULARITY of wireless communications systems. Designing a Self-Optimization System for Cognitive Wireless Home Networks

THE POPULARITY of wireless communications systems. Designing a Self-Optimization System for Cognitive Wireless Home Networks

External hardware and software impact: The HCRM required alterations due to limitations or unexpected behav- ior of the COTS hardware (unstable ad-hoc link setup process for example) and features of Windows OS (e.g., there is no standard interface from which to obtain information about packet drops occurring in low layer protocol buffers/queues). Clearly Windows, being a non real-time OS, also cannot provide latency guarantees on reaction of the system on incom- ing events. Therefore, we encountered the emergent risks caused by external hardware and software components. Those could be accommodated in the optimization mechanisms either through thorough testing in case of reflex solutions or by intro- ducing hidden influence factors in the structure of learning agents. Additionally, it is also possible to hierarchically struc- ture the HCRM and, in particular, the meta-optimizer agent to invoke some of the optimization algorithms on SDR platforms, thus providing a tighter control over MAC/PHY mechanisms. Reflex and learning agents: Our experience when develop- ing the HCRM confirms that reflex agents are more efficient than learning agents in cases when a network operates in a small range predictable operational conditions. However, the learning components become increasingly important in cases when they could be used as training mechanisms to shape the reflex algorithms. In these terms the CRM architecture as opposed to traditional radio resource management archi- tectures shows its power, especially if it is extended with appropriate update and module loading mechanisms. However, the trade-offs between efficiency of simple resource manage- ment solutions, which have careful modular design and could be easily upgraded, and more complex cognitive approaches that are more flexible at the runtime and more optimal control over the network at the price of greater complexity have to be studied still. We believe that a careful consideration should be paid to system design to address the issues of maintainability and extensibility of both physical components and functionali- ties of the system keeping in mind the clear trade-offs between system performance, complexity and robustness.
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A Quantum MIMO Architecture for Antenna Wireless Digital Communications

A Quantum MIMO Architecture for Antenna Wireless Digital Communications

The new form of the optimum quantum detection scheme in Equation (41) with the constraints of Equation (42) can be considered an expansion of the available number of degrees of freedom that the engineer can draw on in his quest to develop and build more reliable wireless quantum communication systems. It is indeed the underlying motivating philosophy behind MIMO research to exploit the increased spatial complexity of the problem in order to design more complex but higher performance generation of wireless communication systems. The architecture proposed in this paper can be realized with electromagnetic infrastructure, but the full details of the implementation are quite extensive and will be addressed elsewhere. First, we note that proper models of quantum antennas must be derived and tested. Quantum channel modeling is already in general more well developed and several excellent Gaussian channel models suitable for electromagnetic-type quantum communications are currently available, see for instance [10, 20]. On the other hand, the receiver design process will require solving the constrained general optimization problem in Equation (41), most probably numerically for the general case. In outline, one must build the entire Hilbert space (theoretically infinite dimensional but should be truncated for practical computations) then perform all calculations of the quantum antennas, channel propagation, and receiver design offline in the simulator. It is hoped that the resulting system will exhibit greater design options since it is already more complex, being richer in new spatial degrees of freedom that can be tuned or optimized in order to best attain target link performance metrics. 4. CONCLUSION
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Distributed Stochastic Cross-Layer Optimization for Multi-Hop Wireless Networks With Cooperative Communications

Distributed Stochastic Cross-Layer Optimization for Multi-Hop Wireless Networks With Cooperative Communications

This section presents distributed solutions to problem (5). The architecture of the proposed distributed cross-layer frame- work is shown in Figure 2. In the initialization phase, nodes establish the HFG and corresponding data structures based on the original G(N, L) in a fully distributed manner (Subsection 3.1). For every time slot t, each node obtains the Channel State Information (CSI) and computes the capacities of its corre- sponding links according to (2)–(4). Then each node operates a distributed global algorithm (Subsection 3.2) to allocate source rates r(t) and hyper link forwarding rates f(t) based on a congestion price and cooperative scheduling (Subsection 3.3). Here the congestion price is the Lagrangian multiplier associated with constraint (6), and this is proportional to the data queue backlog for a commodity in a node (e.g. [31]). In addition, a complexity reduction scheme is developed in Subsection 3.4 to delete useless cooperative relays and virtual SIMO/MISO links. We assume that all control messages used in our distributed algorithms are error-free.
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Tree Based Distributed Multicast Algorithms for Directional Communications and Lifetime Optimization in Wireless Ad Hoc Networks

Tree Based Distributed Multicast Algorithms for Directional Communications and Lifetime Optimization in Wireless Ad Hoc Networks

There is an increasing interest in wireless ad hoc networks in many application domains where instant infrastructure is needed and no central backbone system and administration (like base stations and wired backbone in a cellular system) exist. Each communicating node in these networks acts as a router in addition to a host in order to communicate with each other over a limited number of shared radio channels. A communication session can be achieved either through a single-hop transmission if the communicating nodes are close enough to each other, or through multiple hops by re- laying through intermediate nodes. Since each node in such a network is usually powered by a battery with limited amount of energy, the wireless ad hoc network will become unus- able after the batteries are drained. Consequently, energy ef- ficiency is an important design consideration for wireless ad hoc networks.
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Wireless Sensor Node Localization Algorithm Based on Particle Swarm Optimization and Quantum Neural Network

Wireless Sensor Node Localization Algorithm Based on Particle Swarm Optimization and Quantum Neural Network

Therefore, we use particle swarm optimization (PSO) algorithm as the learning mechanism of quantum neural network (QNN) [9], and propose a PSO-QNN localiza- tion algorithm for WSNs. By combining the two algorithms, a node localization model with short learning time, fast convergence speed and high precision is constructed, and after training, quantum neural network obtains the best initialization weights and thresholds. As a result, when the model is applied to DV-HOP algorithm, a better aver- age hop distance is obtained, thus improving the positioning accuracy of nodes. Be- sides, simulation results show that the algorithm has greatly improved the positioning accuracy and efficiency.
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Quantum annealing and digital quantum ground state preparation algorithms

Quantum annealing and digital quantum ground state preparation algorithms

Although QA and QAOA appear as unrelated models of computation, they are both computationally universal [Aharonov et al., 2008; Mizel et al., 2007; Lloyd, 2018], suggesting that some connections might exist. Here we take a step forward in establishing this connection, by showing that one can construct an optimal QAOA solution which is adiabatic. Our contribution builds upon two recent interesting works. The first is the proposal for a fully digitized-QA (dQA) [Barends et al., 2016]. As we discussed in Chapter 1 and Chapter 3, digitized-QA shares technical similarities with the QAOA quantum circuit and points towards a universal-gate approach to QA, with the bonus of the possibility of error-correction [Bravyi and Kitaev, 1998; Fowler et al., 2012]. The second is the result of Yang et al. [2017], who showed that the digital nature of the QAOA Ansatz emerges naturally, when searching for an optimal protocol, from the “bang-bang” form predicted by the application of Pontryagin’s principle [D’Alessandro, 2007; Brif et al., 2010].
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Wireless communications are flexible: a device

Wireless communications are flexible: a device

Fingerprinting. The framework proposed in this paper does not capture the capabilities of wireless re- ceivers to perform device fingerprinting, i.e., to identify a transmitter based on the characteristics of the signal it transmits [28]. This potential receiver capability has implications, notably for the impossibility result. If the model were to include some form of fingerprinting, then the indistinguishability of local views would not hold, and the impossibility result would be lifted. It is not clear how such capabilities could be introduced into a formal framework, especially because these techniques are relatively recent, and we have only limited under- standing of the hardness of fingerprint spoofing [8]. Integrating these into a formal framework is a possible direction of future work.
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Optimization: Algorithms and Applications

Optimization: Algorithms and Applications

SPIDAL has or will have support for model fitting at several levels of abstractions: – Low-level: grid search, Viterbi, Forward-Backward, Markov Chain Monte Carlo MCMC algorithms, determ[r]

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UWB antennas for wireless communications

UWB antennas for wireless communications

Various wideband antennas have been interesting subjects and have found important applications in military and civilian systems. The PMA has been a good candidate for such applications. There has been a question of how wide the bandwidth of a PMA could be. Can we make a PMA a SWB antenna? SWB technology could possibly be a potential approach to offer high- resolution sensing applications such as the ground-penetrating radar and through wall sensing. SWB radio technology has unique advantages as compared to narrowband technology, and also comprised all UWB technology features but with more channel capacity, higher precision and supper resolution in communications and ranging. Therefore, design of a compact planar monopole antenna with SWB performance is the first research topic in the thesis.
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Spectrum Monitoring Algorithms for Wireless and Satellite Communications

Spectrum Monitoring Algorithms for Wireless and Satellite Communications

Nowadays, there is an increasing demand for more efficient utilization of the radio fre- quency spectrum as new terrestrial and space services are deployed resulting in the congestion of the already crowded frequency bands. In this context, spectrum monitor- ing is a necessity. Spectrum monitoring techniques can be applied in a cognitive radio network, exploiting the spectrum holes and allowing the secondary users to have access in an unlicensed frequency band for them, when it is not occupied by the primary user. Furthermore, spectrum monitoring techniques can be used for interference detection in wireless and satellite communications. These two topics are addressed in this thesis. In the beginning, a detailed survey of the existing spectrum monitoring techniques ac- cording to the way that cognitive radio users 1) can detect the presence or absence of the primary user; and 2) can access the licensed spectrum is provided. Subsequently, an overview of the problem of satellite interference and existing methods for its detection are discussed, while the contributions of this thesis are presented as well.
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Mobile Communications Wireless Telecommunication

Mobile Communications Wireless Telecommunication

o Already in the first generation the smallest part of the transmission path is done wireless is done wireless cellular user air interface core network Hand n t er Handynutzer fixed ne[r]

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Quantum Secret Broadcast for Wireless Quantum Networks

Quantum Secret Broadcast for Wireless Quantum Networks

Inspired by the feature of QSS that more than one party can receive secret message each time, we introduce the idea of QSS into a wireless quantum network, and propose a quantum secret broadcast scheme to solve the troubling efficiency problem. In a cluster network cored on three parties of QSS, three cluster heads, Alice, Bob, and Charlie will collaborate honestly to broadcast messages to cluster members by using pre-shared GHZ states. The communi- cation mode can be whole-network broadcast or intra-cluster broadcast. Furthermore, to prevent illegal eavesdropping, three cluster heads will perio- dically update a encoding key Y . Consequently, illegal nodes cannot read out the message correctly for lack of suitable decoding key.
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Evolving Quantum Computer Algorithms!

Evolving Quantum Computer Algorithms!

Primary Classification:! #F. Theory of Computation! # F.m MISCELLANEOUS! Additional#Classification: " # I. Computing Methodologies" # I.2 ARTIFICIAL INTELLIGENCE" # # # I.2.2 Automatic Programming" # # # # # Subjects: Program synthesis " General Terms: Algorithms, Performance!

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Quantum algorithms for computational nuclear physics

Quantum algorithms for computational nuclear physics

and Optics, Faculty of Mathematics and Phys., Charles University, Ke Karlovu 3, 121 16 Prague 2, Czech Rep.[r]

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COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NETWORKSManpreet Kaur, Jagroop Kaur

COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NETWORKSManpreet Kaur, Jagroop Kaur

ABSTRACT : Fast growth of wireless services in recent years is an indication that considerable value is placed on wireless networks. Wireless devices have most utility when they can be used anywhere at any time. One of the greatest challenges is limited energy supplies. Therefore, energy optimization is one of the most challenging problems in wireless networks. In recent years, Wireless Sensor Networks have gained growing attention from both the research community and actual users. As sensor nodes are generally battery-energized devices, so the network lifetime can be widespread to sensible times. Therefore, the crucial issue is to prolong the network lifetime. In this paper, two Energy Optimization Schemes Clustering and Direct Diffusion for Wireless Sensor Networks (WSN) has been compared on the basis of different parameters like scalability, energy efficiency and reliability etc. on the basis of this comparison we can use better Optimization technique according to the situation.
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Algorithms Bridging Quantum Computation and Chemistry

Algorithms Bridging Quantum Computation and Chemistry

tal level. From this cornerstone, we developed a new quantum algorithm capable of utilizing any quantum device to its maximum capability in studying quantum eigen- value problems [196]. We then helped to extend this algorithm to new architectures as well as import ideas from traditional quantum chemistry to further enhance its ef- ficacy [166, 261]. In the study of quantum algorithms, we gained new insight into the traditional methods of simulating quantum chemistry. From these insights, we were inspired to write down a new quantum variational principle connecting time dynam- ics to ground state eigenvalue problems [167]. This formulation facilitated not only the development of time-parallel algorithms, but a new way to stochastically sam- ple space-time paths with a discrete form of quantum Monte Carlo [165]. In study- ing the performance of this algorithm, we were inspired to consider a different ap- proach to the traditional simulation of electronic structure problems. This method uses compressed sensing techniques to find simple representations of electronic wave- functions [164]. The continuous cross-pollination between quantum computation and classical computation has offered numerous insights into both fields, and this is not a phenomenon likely to stop soon. We have learned much in navigating this bridge between two fields, and believe there is much yet to be learned.
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Photonic Communications and Quantum Information Storage Capacities

Photonic Communications and Quantum Information Storage Capacities

This paper presents photonic communications and data storage capacitates for classical and quantum communications over a quantum channel. These capacities represent a generalization of Shannon’s classical channel capacity and coding theorem in two ways. First, it extends classical results for bit communication transport to all frequencies in the electro- magnetic spectrum. Second, it extends the results to quantum bit (qubit) transport as well as a hybrid of classical and quantum communications. Nature’s limits on the rate at which classical and/or quantum information can be sent er- ror-free over a quantum channel using classical and/or quantum error-correcting codes are presented as a function of the thermal background light level and Einstein zero-point energy. Graphical results are given as well as numerical results regarding communication rate limits using Planck’s natural frequency and time-interval units!
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Spacetime effects on satellite-based quantum communications

Spacetime effects on satellite-based quantum communications

B. A simple continuous-variable QKD protocol As an instructive counter example we now consider a type of quantum communication protocol whose perfor- mance is not affected by curvature. While some proto- cols require the users to perform only local operations and exchange quantum systems, other protocols require the users to exchange additional systems, such as local oscillators. The latter systems might naturally incorpo- rate the means for the compensation of the effects due to space-time curvature. Here, we will use the techniques developed above to analyze one such example of a contin- uous variable QKD protocol similar to that investigated in [18]. Alice employs two coherent states originating from the same source (i.e. a laser) with strong power. The source is then split up into one strong beam which is used as a reference (the Local Oscillator) and one weak beam used as a signal. Bob collects the two beams, mixes them at a balanced beam splitter and measures the pho- tocurrents of the two output modes. In this section we will show that by using extra resources (i.e., the Local Oscillator) Bob will be able to compensate for the effects of the curvature of space-time.
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