Maximization of average number of correctly received symbols over multiple channels in the presence of idle periods

In this study, optimal channel switching (time sharing) strategies are investigated under average power and cost constraints for maximizing the average number of correctly received symbols between a transmitter and a receiver that are connected via multiple flat-fading channels with additive Gaussian noise. The optimal strategy is shown to correspond to channel switching either among at most three different channels with full channel utilization (i.e., no idle periods), or between at most two different channels with partial channel utilization. Also, it is stated that the optimal solution must operate at the maximum average power and the maximum average cost, which facilitates low-complexity approaches for obtaining the optimal strategy. For two-channel strategies, an upper bound is derived, in terms of the parameters of the employed channels, on the ratio between the optimal power levels. In addition, theoretical results are derived for characterizing the optimal solution for channel switching between two channels, and for comparing performance of single channel strategies. Sufficient conditions that depend solely on the systems parameters are obtained for specifying when partial channel utilization cannot be optimal. Furthermore, the proposed optimal channel switching problem is investigated for logarithmic cost functions, and various theoretical results are obtained related to the optimal strategy. Numerical examples are presented to illustrate the validity of the theoretical results.     

Stochastic signaling in the presence of channel state information uncertainty

In this paper, stochastic signaling is studied for power-constrained scalar valued binary communications systems in the presence of uncertainties in channel state information (CSI). First, stochastic signaling based on the available imperfect channel coefficient at the transmitter is analyzed, and it is shown that optimal signals can be represented by a randomization between at most two distinct signal levels for each symbol. Then, performance of stochastic signaling and conventional deterministic signaling is compared for this scenario, and sufficient conditions are derived for improvability and nonimprovability of deterministic signaling via stochastic signaling in the presence of CSI uncertainty. Furthermore, under CSI uncertainty, two different stochastic signaling strategies, namely, robust stochastic signaling and stochastic signaling with averaging, are proposed. For the robust stochastic signaling problem, sufficient conditions are derived for reducing the problem to a simpler form. It is shown that the optimal signal for each symbol can be expressed as a randomization between at most two distinct signal values for stochastic signaling with averaging, as well as for robust stochastic signaling under certain conditions. Finally, two numerical examples are presented to explore the theoretical results.     

基于独立非同分布广义K信道的OSTBC-MIMO系统及性能

针对独立非同分布（i.n.i.d.）广义K衰落信道中的正交空时分组编码多输入多输出（OSTBC-MIMO）系统性能问题,建立了采用M-QAM调制的OSTBC-MIMO系统在i.n.i.d广义K衰落信道中的系统模型。利用接收端等效信噪比（SNR）可以近似为两个由多个独立伽马随机变量之和构成的随机变量的乘积这一关系,推导出近似等效信噪比的概率密度函数表达式,继而推导得到平均符号差错概率、中断概率和容量的闭式。仿真结果表明：除波形参数m外,k取任意不同正整数值也会对系统性能产生重要的影响,OSTBC-MIMO系统在i.n.i.d.广义K衰落信道中的性能与在独立同分布（i.i.d.）广义K衰落信道中的性能存在显著差别。     

Remote stabilization over fading channels

In this paper, we study the problem of remote mean square stabilization of a MIMO system when independent fading channels are dedicated to each actuator and sensor. We show that the stochastic variables responsible for the fading can be seen as a source of model uncertainty. This view leads to robust control analysis and synthesis problems with a deterministic nominal system and a stochastic, structured, model uncertainty. As a special case, we consider the stabilization over Erasure or drop-out channels. We show that the largest probability of erasure tolerable by the closed loop is obtained from solving a robust control synthesis problem. In more general terms, we establish that the set of plants which can be stabilized by linear controllers over fading channels is fundamentally limited by the channel generated uncertainty. We show that, the notion of mean square capacity, defined for a single channel in the loop, captures this limitation precisely and characterizes equivalence classes of channels within the class of memoryless fading channels.     

Finite-time control for discrete-time Markovian jump systems with deterministic switching and time-delay

In this paper, the finite-time control problem is investigated for a class of discrete-time Markovian jump systems (MJLSs) with deterministic switching and time-delay. The considered systems are subject to a piecewise-constant transition probability (TP) matrix, which leads to both the deterministic switches and stochastic jumps. First, the stochastic finite-time boundedness (SFTB) and l ₂ gain analysis for the systems are studied by employing the average dwell time (ADT) approach. Note that a finite-time weighted l ₂ gain is obtained to measure the disturbance attenuation level. Then, the mode-dependent and variation-dependent controller is designed such that the resulting closed-loop systems are stochastically finite-time bounded and have a guaranteed disturbance attenuation level. Finally, a numerical example is given to verify the potential of the developed results.     

Some decision problems for bottom-up triangle acceptors

It is proved that the emptiness problem for deterministic bottom-up triangle acceptors (BTA's) over a single input symbol is recursively unsolvable. From this result, it is also shown that certain decision problems involving BTA's and BPA's (deterministic bottom-up pyramid acceptors) are unsolvable.     

Congestion-aware channel assignment for multi-channel wireless mesh networks

In this paper, we propose a distributed congestion-aware channel assignment (DCACA) algorithm for multi-channel wireless mesh networks (MC–WMNs). The frequency channels are assigned according to the congestion measures which indicate the congestion status at each link. Depending on the selected congestion measure (e.g., queueing delay, packet loss probability, and differential backlog), various design objectives can be achieved. Our proposed distributed algorithm is simple to implement as it only requires each node to perform a local search. Unlike most of the previous channel assignment schemes, our proposed algorithm assigns not only the non-overlapped (i.e., orthogonal) frequency channels, but also the partially-overlapped channels. In this regard, we introduce the channel overlapping and mutual interference matrices which model the frequency overlapping among different channels. Simulation results show that in the presence of elastic traffic (e.g., TCP Vegas or TCP Reno) sources, our proposed DCACA algorithm increases the aggregate throughput and also decreases the average packet round-trip compared with the previously proposed Load-Aware channel assignment algorithm. Furthermore, in a congested IEEE 802.11b network setting, compared with the use of three non-overlapped channels, the aggregate network throughput can further be increased by 25% and the average round-trip time can be reduced by more than one half when all the 11 partially-overlapped channels are used.     

Optimal channel switching in multiuser systems under average capacity constraints

In this paper, the optimal channel switching problem is studied for average capacity maximization in the presence of multiple receivers in the communication system. First, the optimal channel switching problem is proposed for average capacity maximization of the communication between the transmitter and the secondary receiver while fulfilling the minimum average capacity requirement of the primary receiver and considering the average and peak power constraints. Then, an alternative equivalent optimization problem is provided and it is shown that the solution of this optimization problem satisfies the constraints with equality. Based on the alternative optimization problem, it is obtained that the optimal channel switching strategy employs at most three communication links in the presence of multiple available channels in the system. In addition, the optimal strategies are specified in terms of the number of channels employed by the transmitter to communicate with the primary and secondary receivers. Finally, numerical examples are provided in order to verify the theoretical investigations.     

Optimal sensor rules and unified fusion rules for multisensor multi-hypothesis network decision systems with channel errors

In this paper, for general jointly distributed sensor observations, we present optimal sensor rules with channel errors for a given fusion rule. Then, the unified fusion rules problem for multisensor multi-hypothesis network decision systems with channel errors is studied as an extension of our previous results for ideal channels, i.e., people only need to optimize sensor rules under the proposed unified fusion rules to achieve global optimal decision performance. More significantly, the unified fusion rules do not depend on distributions of sensor observations, decision criterion, and the characteristics of fading channels. Finally, several numerical examples support the above analytic results and show some interesting phenomena which can not be seen in ideal channel case.     

Adaptive linear filtering design with minimum symbol error probability criterion

Adaptive digital filtering has traditionally been developed based on the minimum mean square error (MMSE) criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate (MSER) criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.     

Markup pricing strategies between a dominant retailer and competitive manufacturers

Markup pricing contracts have been widely employed in many industries. Under such contracts, a retailer charges a retail margin over the wholesale price levied by the supplier to guarantee her financial prudence. In a setting where two competitive manufacturers sell substitutable products through a common dominant retailer, we investigate and compare performance of two different markup arrangements, namely, percentage and dollar, under the deterministic and stochastic demand situations, respectively. We find that, no matter what the demand characteristic is, when the retailer switches from dollar to percentage markup, the retailer makes a higher profit while the manufacturers suffer, because the switching forces manufacturers charge lower wholesale prices and thus leads to lower retail prices. Moreover, under the deterministic demand situation, the switching brings about a larger order quantity and a higher channel profit. Under the stochastic demand situation, however, the effect of the switching on order quantity and channel profit depends on manufacturer differentiation and retailer efficiency: order quantity (channel profit) becomes smaller (lower), as manufacturer differentiation becomes weaker or retailer efficiency becomes higher. And, the demand uncertainty intensifies the effect.     

On the end-to-end flow allocation and channel assignment in multi-channel multi-radio wireless mesh networks with partially overlapped channels

The performance of wireless mesh networks (WMNs) can be improved significantly with the increase in number of channels and radios. Despite the availability of multiple channels in several of the current wireless standards, only a small fraction of them are non-overlapping and many channels are partially overlapped. In this paper, we formulate the joint channel assignment and flow allocation problem for multi-channel multi-radio WMNs as a Mixed Integer Linear Program (MILP). Unlike most of the previous studies, we consider the case when both non-overlapped and partially overlapped channels are being used. We consider an objective of maximizing aggregate end-to-end throughput and minimizing queueing delay in the network, instead of the sum of link capacities, since the traffic characteristics of a multi-hop WMN are quite different from a single hop wireless network. Our static channel assignment algorithm incorporates network traffic information, i.e., it is load aware. Our formulation takes into consideration several important network parameters such as the transmission power of each node, path loss information, the signal to interference plus noise ratio at a node, and the frequency response of the filters used in the transmitter and receiver. We show by simulations that our MILP formulation makes efficient use of the spectrum, by providing superior channel assignments and flow allocations with the addition of partially overlapped channels, without the use of any additional spectrum. We also justify the need to consider alternative objective functions such as, minimizing average queueing in the network. We also propose a polynomially bounded heuristic algorithm to scale the proposed algorithm to bigger network topologies.     

A simheuristic algorithm to set up starting times in the stochastic parallel flowshop problem

This paper addresses the parallel flowshop scheduling problem with stochastic processing times, where a product composed of several components has to be finished at a particular moment. These components are processed in independent parallel factories, and each factory can be modeled as a permutation flowshop. The processing time of each operation at each factory is a random variable following a given probability distribution. The aim is to find the robust starting time of the operations at each factory in a way that all the components of the product are completed on a given deadline with a user-defined probability. A simheuristic algorithm is proposed in order to minimize each of the following key performance indicators: (i) the makespan in the deterministic version; and (ii) the expected makespan or a makespan percentile in the stochastic version. A set of computational experiments are carried out to illustrate the performance of the proposed methodology by comparing the outputs under different levels of stochasticity.     

Multi channel frequency hopping spread spectrum signaling using code M-ary frequency shift keying

In this paper we have proposed code M-ary frequency shift keying technique based frequency hopping (CMFSK/FH) spread spectrum signaling for multi channel. In this technique, taking one bit from each channel, we have constructed data word (called as symbol) for CMFSK system and corresponding to each state of data word, the transmitter generates a frequency, which is selected and amplified for transmission. The received signal in the receiver is tuned to each transmitted frequency and decoded for separation of channels. Here, we have developed simulation model for bit error rate (BER) performance analysis of CMFSK/FH system and compared the same with analytical results of BER. It is seen that simulation model is matched closely with analytical result. The BER performance for CMFSK/FH is better than that of existing FH system. It is also seen that the BER decreases with increase of frequency hop size.     

On a model and capacity of MIMO channels

A model of a MIMO fading channel is considered which does not assume that the discrete-time axis is divided into long intervals with a constant channel. It assumes only that there are ℓ possible states (or subchannels) of the channels, and at each moment both the transmitter and receiver know the channel state. The average input power in the subchannels can be different and not equal to the average input power over the full time axis. We obtain a lower bound for the capacity of the considered vector channel. Also, we consider a vector channel with one transmitting and two or more receiving antennas. We obtain optimum distributions of the average power over the subchannels and lower bounds for the channel capacity.     

Monotonic walks on a necklace and a coloured dynamic vector

Stochastic and deterministic versions of a discrete dynamical system on a necklace are investigated. This network consists of a sequence of contours NSWE with nodes, i.e. the nodes are common points at W and E. There are two cells and a particle on each contour. Each time instance, the particle occupies a cell and, at every time unit, comes to the next cell in the same direction. The particles of the neighbouring contours move in accordance with rules of stochastic or deterministic type. The behaviour of the model with the rule of the first type is stochastic only at the beginning and after a time interval becomes a pure deterministic system. The system with the second rule comes to a steady mode, which depends on the initial state. The average velocity of particles and characteristics of the system are studied.     

OAZE: A network-friendly distributed zapping system for peer-to-peer IPTV

IPTV systems attracting millions of users are now commonly deployed on peer-to-peer (P2P) infrastructures and provide an appealing alternative to multicast-based systems. Typically, a P2P overlay network is associated with each channel, composed of users who receive, watch and redistribute this channel. Yet, channel surfing (aka as zapping) involves switching overlays and may introduce delays, potentially hurting the user experience when compared to multicast-based IPTV. In this paper, we present a distributed system called OAZE (Overlay Augmentation for Zapping Experience) which speeds up the switching process and reduces the overall cross-domain traffic generated by the IPTV system. In OAZE, each peer maintains connections to other peers, not only in a given channel, but also in a subset of all channels to which the associated user is likely to zap. More specifically, we focus on the channel assignment problem, i.e. determining, in a given P2P overlay, the optimal distribution of the responsibility to maintain contact peers to other channels. We propose an approximate algorithm providing guaranteed performances, and a simpler and more practical one. Our experimental results show that OAZE leads to substantial improvements on the connections between peers, resulting in less switching delay and lower network cost; it then represents an appealing add-on for existing P2P IPTV systems.     

基于长短期记忆神经网络的矢量跟踪通道状态监测算法

卫星导航系统接收机分为标量跟踪架构和矢量跟踪架构。矢量跟踪接收机的特点是采用一种中心导航滤波器实现所有通道信息的集中处理，这样可以充分利用通道之间的共享信息，提升接收机的性能。但由此带来的问题是通道之间的相互影响，当某个通道的信号被遮挡或者信号较弱时，会影响导航滤波器的正常工作，因此需要进行通道运行状态的监测。本文提出一种基于长短期记忆神经网络的通道状态监测方法，将通道的信息序列值作为神经网络的输入向量。仿真结果表明，本文提出的方法能够有效地检测故障，保证矢量跟踪接收机的定位精度。     

Lyapunov exponents of hybrid stochastic heat equations

In this paper, we investigate a class of hybrid stochastic heat equations. By explicit formulae of solutions, we not only reveal the sample Lyapunov exponents but also discuss the pth moment Lyapnov exponents. Moreover, several examples are established to demonstrate that unstable (deterministic or stochastic) dynamical systems can be stabilized by Markovian switching.