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In this paper, we present a method of constructing PAM constellations with non-uniform spacing by determining the symbol amplitudes based on warping the uniform distancing by applying the inverse error function. The construction method can adapt to symbol energy (SNR) constraints by adjusting one parameter. This parameter determines the degree of separation of the outermost pair of constellation symbols from the inner symbols and the degree of spacing uniformity. Optimization of this parameter is discussed for maximizing the equiprobable mutual information of the channel having the constellation. The equiprobable mutual information of the constellation constructed by the suggested method is compared against the uniform spacing and the Gaussian approximated spacing, which asymptotically approaches the AWGN capacity.

The mutual information achieved by a pulse-amplitude modulated constellation depends strongly on the symbol labels. In general, these symbols are labeled based on a Gray code mapping, in which neighboring label bit-strings differ by only one bit value. For a 16-symbol constellation, there are numerous such Gray code mappings satisfying this property, each with distinct mutual information performance over SNR. In this paper, we present an investigation of the optimal labeling using cyclic Gray codes for symmetric 16-symbol one-dimensional constellations with uniform symbol probability and non-uniform spacing. These results can be extended to 256-symbol quadrature amplitude modulated constellations with in-phase/quadrature symmetry.

This paper studies the channel capacity of the discrete-input continuous-output channel in which the input signal constellation has a rectangular-grid shape and the noise is additive white Gaussian. The channel capacity of such a system depends on the two parameters defining the shape of the constellation and the signal-to-noise ratio. We study the capacity as a function of these variables.

In this paper, we study the issue of selecting the transmission power (energy per symbol) and power allocation between pilot and data symbols to maximize the energy efficiency (EE). We consider the system that has a slow-fading channel and employs a minimum mean square error (MMSE)-based channel estimation scheme for a pilot symbol-assisted modulation. We derive a closed-form expression for a lower bound on the channel capacity in the presence of channel estimation error, and use this lower bound to derive the energy efficiency. We then discuss optimizing transmission power and also power allocation between pilot and data symbols for maximizing the energy efficiency. At the end we present a very simple method for finding a suboptimal solution for transmission power and also the power allocation between pilot and data symbols that result in a performance very close to the optimal solution.

We study a cognitive radio network in which two sources communicate with each other through multiple relays operating in non-overlapping frequency bands in the radio spectrum. We consider the problem of optimizing the transmission powers of the sources and relays so that the minimum of the two sources' transmission capacities is maximized. Thanks to the non-overlapping nature of the relays' signals, the problem can be represented as a constrained optimization over two variables, but that formulation still results in a non-convex optimization. We show that an optimal solution occurs on the upper boundary of the feasible set. We further show that the objective function is unimodal on the boundary. We suggests a simple method of optimization that utilizes these properties.

In this paper, we study a cognitive radio network comprising a pair of sources and multiple relays operating in non-overlapping bands in the radio spectrum. We address the problem of deciding the transmission powers of the sources and relays so that the sum of the sources' transmission capacities is maximized. Due to the non-overlapping nature of the relays' signals, the problem can be represented as a constrained optimization over two variables, but such formulation results in a non-convex optimization. We transform this problem to an equivalent optimization problem over three variables for which the monotonic optimization (MO) can be employed. We discuss an algorithm that is guaranteed to terminate with an e-optimal solution.

In this work, we consider a cognitive radio network comprising a pair of sources and multiple relays exercising the amplify-and-forward cooperation in a shared frequency band. For such a network, we consider the problem of determining transmit powers of the sources and relays so that the lesser of the achievable error-free data rates from the two sources is maximized. The formulated problem is a non-convex optimization problem and does not have a structure to guarantee the quality of a solution. We present the transformation of this problem to an equivalent optimization problem for which the monotonic optimization methods can be applied. We establish that the polyblock outer approximation algorithm for this monotonic optimization terminates with an e-optimal solution after a finite number of iterations.

In this paper, we study the issue of optimizing the data transmission power (energy per symbol) for minimizing the expected energy required to successfully deliver a packet to the destination in a wireless point-to-point link through automatic repeat request (ARQ). We focus on the system that has a slow fading wireless channel and employs a pilot symbol-assisted modulation (PSAM) scheme along with a channel estimation scheme based on the minimum mean square error (MMSE) criterion. We derive the expected energy as a function of the data transmission power. Then, we derive a closed-form expression of the data transmission power that minimizes that function.

In this paper, we present a binary enhanced firework algorithm (BEFWA) for optimization problems with a search space of binary vectors, and we test this new algorithm for the MAX-SAT problem. The EFWA algorithm is a relatively recent development in swarm intelligence (SI) for continuous optimization, and the explosion amplitude operator in EFWA does not fit for searching a good solution in a discrete binary space. The original ABC algorithm is also not suitable for searching through a binary space, but its adaptation, the discrete ABC (DisABC), for a binary space was recently presented. In the present paper, we employ the similarity-measure-based differential expression from DisABC to design the binary EFWA algorithm to operate in binary space. The MAX-SAT is a well-known modelling framework for various computationally challenging problems, and thus it has many applications. However, the MAX-SAT problem has been proven to be NP-hard. Existing results indicate that evolutionary algorithms (EAs) can be useful for finding good-quality solutions without excessive computational resources. Our experimental results demonstrate that the binary EFWA can be a better choice over DisABC and Genetic Algorithm (GA) for various classes of MAX-SAT instances.

In this paper, we present a problem-specific, information-based enhanced fireworks algorithm (IEFWA) and a hybrid of the IEFWA and the Biogeography-based optimization (BBO) algorithm. These new algorithms are tested for virtual machine (VM) placement problem with the objective of minimizing the energy consumption in datacenters, which is an integer space optimization problem. The EFWA algorithm is a relatively recent development in swarm intelligence (SI), and is based on explosion amplitude operator that operates in the continuous space. The 'round' function is used to convert the explosion amplitude value to the nearest integer to operate in integer space. In our IEFWA algorithm design, some domain knowledge of VM placement problem was used. During the spark generation in IEFWA, the explosion amplitude is added to the information-based selected components of the fireworks instead of adding the explosion amplitude into instead of all components. The hybrid IEFWA/BBO algorithm probabilistically chooses the explosion amplitude operator of IEFWA algorithm or the migration operator of BBO algorithm with a user determined probability for the exploitation of good candidate solutions. The VM placement problem is NP-hard, and existing results demonstrate that evolutionary algorithms (EAs) can be useful choice for good-quality solution with reasonable computing resources. We experimentally compare the performance of BBO, EFWA, IEFWA, hybrid IEFWA/BBO and the first fit decreasing (FFD) algorithms. Simulation results demonstrate the two key findings of this study. First, IEFWA algorithm consumes less CPU time as compared to the EFWA algorithm. Second, IEFWA and hybrid IEFWA/BBO algorithms outperform EFWA and BBO algorithms in terms of average energy consumed in datacenters.

This paper proposes a stochastic gradient-like optimization for experimentally determining parameters to be used in the extended Kalman filter in an induction motor model. The gradient-like algorithm can be combined with an evolutionary algorithm in order to avoid being trapped in a low-performance local extremum. Recursive methods of computing the gradient is presented.

In this paper, we analyze two different decode-and-forward (DF) relay-assisted communication system models, which display different levels of practicality and represent large classes of DF systems in the literature. We spell out delineating aspects of the two models and rigorously show inequality among their symbol error probabilities (SEPs). Both system models assume imperfect channel side information at receivers and maximum ratio combining (MRC) at the destination node.

This paper presents methods of combining the extended Kalman filtering/smoothing, the maximum likelihood (ML) estimation, and expectation maximization (EM) for estimating the rotor speed and flux of the induction motor online in the case in which process and measurement noise statistics are not known.

Low power wireless sensors, personal wireless hub (PWH) and receivers can reduce the workload of the paramedic staff in a hospital. In this paper, we use multiple PWHs to transfer sensor data to the main central controller, which helps the wireless sensor devices. A well designed multiple PWH assignment and power control scheme can reduce the electromagnetic and in-band interference induced to the other medical devices in the hospital. We propose a framework and low complexity algorithm for interference aware joint power control and multiple PWH assignment (IAJPCPA) in a hospital building with cognitive radio capability. The proposed IAJPCPA is a non-convex mixed integer non-linear optimization problem (NC-MINLP) which is generally NP-hard. We present an efficient PWH assignment and power control scheme for IAJPCPA. We also propose an upper bound on the IAJPCPA that converts the non-convex problem into a convex optimization problem. We examine the effect of different system parameters.

MAX-SAT is a classic NP-hard optimization problem. Many real problems can be easily represented in, or reduced to MAX-SAT, and thus it has many applications. Finding optimum solutions of NP-hard optimization problems using limited computational resources seems infeasible in general. In particular, all known exact algorithms for MAX-SAT require worst-case exponential time, so evolutionary algorithms can be useful for finding good quality solutions in moderate time. We present the results of an experimental comparison of the performance of a number of recently proposed evolutionary algorithms for MAX-SAT. The algorithms include the Artificial Bee Colony (ABC) algorithm, Quantum Inspired Evolutionary Algorithm (QEA), Immune Quantum Evolutionary Algorithm (IQEA), Estimation of Distribution Algorithm (EDA), and randomized Monte Carlo (MC). Our experiments demonstrate that the ABC algorithm has better performance than the others. For problems with Boolean domain, such as MAX-SAT, the ABC algorithm requires specification of a suitable similarity measure. We experimentally evaluate the performance of the ABC algorithm with five different similarity measures to indicate the better choice for MAX-SAT problems.

Recently, high levels of energy consumption in datacenters has become a concern not only due to operational costs, but also due to adverse effects on the environment (i.e., carbon emission, climate change, etc.) Virtualization technology can provide better management of physical servers/machines (PM) and may help reduce power consumption. The purpose of this study is to minimize the total energy consumption through good virtual machine (VM) placement. The VM placement problem has a large search space. Finding an optimal solution of such problems using an exhaustive search is impractical. Heuristic algorithms can provide high-quality solutions with limited computing resources in acceptable time. Evolutionary Algorithms (EAs) can be considered as heuristic tools that can provide high-quality solutions to this type of problems. We propose a Biogeography Based Optimization (BBO) Algorithm for energy-efficient VM placement. We compare the BBO results with the Genetic Algorithm (GA). Overall, simulation results show that BBO outperforms GA.

In this paper, we present a novel method to derive a tight upper bound on the symbol error probability (SEP) of a rotation coded scheme in the case of Rayleigh fading channels. This upper bound has a closed-form expression and provides results closer to the exact expression than the union-Chernoff bound. We present numerical simulations that show the tightness of the proposed analytical upper bound. Based on the tight upper bound, we also find the optimal rotation angle, which minimizes the derived tight upper bound expression of the SEP.

In this paper, we study the issue of optimally detecting symbols for a Selective-Decode-and-Forward (SDF) relay-assisted communication system in the presence of channel estimation error for flat Rayleigh fading channels. In the system studied, a pilot symbol-assisted modulation (PSAM) scheme is used along with a channel estimation scheme based on minimum mean square error (MMSE) criterion. In particular, we present a novel spectrum detection in presence of channel estimation error for the SDF protocol. Numerical simulation results are presented to show that the presented spectrum detection scheme performs well in terms of bit error probability (BEP).

In this paper, we study the issue of optimizing transmission power (energy per symbol) for minimizing the expected energy required to deliver a frame to the destination node in a point-to-point link employing an automatic repeat request (ARQ) scheme over a fading channel. We derive the expected energy as a function of the transmission power and derive a simple optimization algorithm based on the properties of this function.

In this paper, we study the issue of optimally allocating the transmission energy between a pilot symbol and data symbols for a point-to-point communication system over the slow fading channel. In the system studied, our performance criterion for the optimal energy allocation is the frame error probability (FEP). A pilot symbol-assisted modulation (PSAM) scheme is used along with a channel estimation scheme based on the minimum mean square error (MMSE) criterion. We derive an approximate expression for the FEP in the presence of channel estimation error and use this expression for minimizing the FEP in allocating the transmission energy. Numerical simulation results are presented to show that the derived approximate FEP expression is close to the actual FEP.

In this paper, we present a mathematical model of power line communication access systems and a technique for an efficient network deployment. We focus on the problem of designing an infrastructure network model which fits the power line communication needs and installing an apt number of base stations and repeaters at adequate locations suited to serve all the subscribers to the required level at a low cost to the utility company. The computational complexity of determining an optimal deployment by using exhaustive search grows exponentially with the number of base stations, repeaters and the users. We propose a heuristic method combined with an Estimation-of-Distribution Algorithm (EDA) for this assignment problem. EDA is a probabilistic evolutionary algorithm which updates its population at each iteration on the basis of the probability densities obtained from the population of superior candidates evaluated and chosen at the previous iteration.

We demonstrate performance of Ge-Si optical modulator and Ge PD receiver on a SOI platform for the parallel 4×25 Gbs application.

We report a 9.5% wall-plug-efficient un-cooled external-cavity DBR laser built with a III–V-semiconductor reflective-SOA edge-coupled to an SOI waveguide containing a Bragg grating mirror. The hybrid laser is suitable for power efficient, CMOS-compatible optical transmitters.

In this paper we present a low-complexity Artificial Bee Colony (ABC) based interference aware relay assignment scheme with power control for a cognitive radio network comprises of one source, multiple relays and multiple destination nodes. The Exhaustive Search Algorithm (ESA) returns the optimal solution to the problem; yet it has a high computational complexity that grows exponentially with the number of users and relays. Our contribution includes formulating the jointly relay assignment with source and relays' power allocation as a mixed integer non-linear programming problem. This problem is further reduced to an integer programming problem. In order to demonstrate the performance of the discrete ABC, we compare it with other contemporary Evolutionary Algorithms (EAs) like ACO, EDA and BBO, as well as the optimal ESA. Our Binary ABC relay assignment results outperform other EAs, while its performance is close to the optimal ESA.

In this paper, we consider a two-way cognitive relay network comprising two sources and multiple relays. The relays use a simple Amplify-and-Forward relaying mechanism. For such networks, we formulate the max-min and sum capacity optimization problems. The formulated optimization problems are non-convex and nonlinear in nature. We obtain the optimal solution of the optimization problems by using a known technique called Global Optimization Algorithm (GOP). We note that the computational complexity of the GOP algorithm grows exponentially with the number of relays. Therefore, we propose low-complexity heuristics that provide suboptimal solutions to the given optimization problems. The simulation results show that the performance of the heuristics is close to that of the respective optimal solutions.

In this paper, we present an interference and channel conditions aware multiple relay assignment and subcarrier allocation scheme for OFDMA based cognitive radio systems employing cooperative transmission using Decode and Forward (DF) technique. We focus on the problem of assigning the relays, the relay powers and allocating the subcarriers to the destination nodes using the sum capacity of the cognitive radio system as the objective function to be maximized under the constraint of maximum acceptable levels of interference for the primary users (PU). The computational complexity of this assignment using exhaustive search algorithm grows exponentially with the number of relays, subcarriers and receivers. Thus we propose Estimation of Distribution Algorithm (EDA) for this assignment problem. EDA is a probabilistic evolutionary algorithm which updates its population at each iteration on the basis of the probability densities obtained from the population of superior candidates evaluated and chosen at the previous iteration. EDA employed with our allocation scheme has low computational complexity and its performance is comparable to the Upper Bound of the solution as shown by various simulation results.

Powermesh MAC protocol is a relatively simple protocol designed for applications that mostly have a regular schedule of data transport activities such as routine status reports of a monitored power system. In particular, this protocol is designed for sensor network nodes that tend to be arranged in an approximately linear configuration, such as along the power lines. The Powermesh MAC protocol is designed with a special intention to allow the nodes to have low duty-cycles. A special feature of this protocol is that each node transmits its own beacon to announce its presence and to send signaling information related to the available time slots. The individual beacons and individual neighbourhood maps are the basic features of the Powermesh MAC protocol. The neighbourhood map is a small data structure which indicates timeslot usage by the node's neighbours. The Powermesh MAC addresses the hidden terminal problem and the exposed terminal problems by individual beacons and neighborhood maps.

This talk will discuss opportunities for photonics to contribute to signal processing applications in general, and RF signal processing in particular. Key issues associated with embedded photonic links, important analog RF functions, and key component and systems requirements will be addressed. Progress in silicon photonics and associated systems technologies, along with basic proof of concept demonstrations will be presented. Specific topics include reconfigurable silicon photonic filters, operation of such filters in coherent RF photonic links, precision photonic control, link linearization methods, and the utility of optical amplifiers.

We demonstrate a Terabit/s receiver by monolithic integration of a polarization independent DWDM echelle grating and high-speed Ge photodiodes on the SOI platform. The compact device has an overall fiber-accessed responsivity of 0.4 A/W.

The latest increase in mobile data usage and emergence of new applications such as Multimedia Online Gaming (MMOG), mobile TV and streaming contents have motivated advances in wireless broadband systems. Recently, the Long-Term Evolution (LTE) technology, which is based on the Universal Mobile Telecommunications System (UMTS) specifications, joins WiMAX as a competitor to achieve increasing demands of the broadband wireless access. Careful deployment of such a network is required to fulfill the high data rate demands with minimal cost of infrastructure and comprehensive coverage of the subscribers. In this paper, a multi-objective network planning problem is defined as utilizing the minimum number of infrastructure sites (i.e. Base Stations or eNode B in UMTS systems) while maximum number of users in service. We proposed a Quantum Inspired Evolutionary Algorithm (QIEA) in order to achieve optimized solution for this problem. The QIEA can be viewed as a probabilistic evolutionary algorithm and thus it is plausible to expect a reasonably good performance in solving combinatorial optimization problems. In this algorithm, each individual is represented by a string of Q-bits, where a Q-bit is the probabilistic representation inspired by the qubit concept in the quantum computing. Computational experiments show that our algorithm is fairly efficient to different scenarios of the network planning problem and performs better than the Genetic Algorithm (GA).

We demonstrate the integration of low dark current Ge photodetectors with a high performance demultiplexer on a large cross-section SOI waveguide platform. This Si-based WDM receiver can be used for multichannel terabit data transmission.

As an enabling building block to support high bandwidth optical interconnections among supercomputing processing elements, we present the first monolithically integrated chip-to-chip wavelength-division multiplexing (WDM) optical proximity coupler with 4-channel × 200GHz-spacing multiplexer/demultiplexer (MUX/DEMUX) functionality utilizing an echelle grating, micro reflective mirrors, and waveguide mode transformers on single silicon-on-insulator (SOI) substrate. This new photonic integrated circuits can be utilized to provide a data-rate independent optical interface that surpasses the throughput bottleneck of current electrical data paths.

In this work we consider an OFDMA-based two-way cognitive relay network that comprises multiple source-destination pairs and multiple relays. The relays assist communication between the source-destination pairs, and different relays transmit on orthogonal subcarriers. The relays employ amplify-and-forward relaying. For this network, we formulate a sum capacity maximization problem to determine the subcarrier assignment and power allocation to the relays. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP). An intuitive way to obtain the optimal solution of MINLP is to exhaustively try all the combination of the discrete variables and solve the resulting non linear optimization problem. However, this approach is computationally intractable. Therefore, we use particle swarm optimization (PSO) to solve the MINLP. The proposed algorithm has low computational complexity, and we verify its effectiveness through simulation results.

In this paper, we present swarm based Resource Minimization Scheme (RMS) for Network-Coding-based Multicast System (NCMS). The main objective of the RMS is to reduce network coding links while maintaining the desired rate of a multicast system. The resource minimization problem in NCMS is NP-Hard. In this paper, we apply the Binary Particle Swarm Optimization (BPSO) algorithm to the resource minimization problem in NCMS. The numerical results show the effectiveness of the proposed BPSO based RMS in comparison to the previously proposed algorithms.

In this paper, we consider the uplink communication in a network of cognitive radio nodes. The transmitting nodes and the receiver are equipped with multiple antennas and MIMO processing abilities. For this network, we study the problem of interference-aware joint secondary user (SU) selection/scheduling and power control (JSUS-QPC). The main objective of the JSUS-QPC is to maximize the sum-rate capacity of the cognitive MIMO uplink communication system under the constraint that the interference to the primary users (PU) is below a specified level. We formulate this optimization problem as nonlinear integer programming problem. The computational complexity of finding an optimal solution to the JSUS-QPC problem by exhaustive search grows exponentially with the number of users and power levels. Therefore, we apply a Quantum Inspired Evolutionary algorithm (QIEA) to determine the suboptimal solution to the JSUS-QPC problem. The proposed scheme has low computational complexity and its results are comparable to the optimal exhaustive search algorithm.

In this paper, we consider the problem of minimizing the resources used for network coding (MRUNC) while achieving the desired throughput in a multicast system. The problem of minimizing the number of network coding links is NP-hard. In this paper we propose a low-complexity Estimation of Distribution Algorithm (EDA) for MRUNC. Our EDA is applicable to the network with and without cycles. The numerical results show the effectiveness of the proposed method over previously proposed algorithms.

In this paper, we present a low-complexity power allocation scheme for non-regenerative (amplify and forward) relaying in cognitive radio systems. The main objective of the power allocation is to maximize the signal to noise ratio (SNR) at the destination under the constraint of acceptable interference to the primary users (PU). In this paper, we propose an iterative power allocation using SNR upper Bound (IPAUB) for non-regenerative relaying. The proposed algorithm has low computational complexity, and we verify its effectiveness through simulation results.

In this paper, we present a binary particle swarm optimization (BPSO)-based low-complexity interference aware relay assignment scheme for multiple-user cognitive radio networks with discrete power control. We consider a network of cognitive radio nodes comprising single source, multiple relays and multiple destinations. For this system, we formulate an optimization problem to allocate power to source and relays and assign the relays to the destinations. The optimization problem is formulated as a mixed integer nonlinear program. Then, we show that the formulation can be reduced into a simpler integer programming problem. Then, we propose a BPSO-based relay assignment scheme to attain a good suboptimal solution to this integer programming problem. The proposed scheme has low computational complexity, and simulation results show that its performance is close to the optimal exhaustive search algorithm.

This paper describes the development of a transmitter and receiver leveraging a combination of monolithic and hybrid integration for Datacenter applications to deliver 500Gbps over 2km on a single mode fiber. The transmitter encompasses 12 electro-absorptive modulated lasers (EMLs) with wavelength channels from 1270nm to 1380nm on a 10nm channel grid. The lasers are grouped in three (3) arrays with each array monolithically integrating four (4) EMLs operating at 43Gbps each. The three EML arrays are flip-chip bonded to a planar light-guide circuit (PLC), which serves as the mounting platform and provides the optical multiplexer function. The receiver couples the signal through an optical PLC-based de-multiplexer to twelve (12) high-speed PIN detectors with a 3dB bandwidth of more than 50GHz. The transmission tests were performed over 2km of SMF with each channel tested at 40Gbps. For the demonstration, a commercial semiconductor optical amplifier was used in front of the receiver to boost the signal. Each channel was tested to be error free to

In this paper, we present two low-complexity interference aware multiple relay assignment schemes for cognitive radio systems. The main objective in assigning multiple relays is to maximize the sum capacity of the cognitive radio system under the constraint of acceptable interference to the primary users (PU). The computational complexity of finding an optimal assignment by exhaustive search grows exponentially with the number of relays and users. The proposed schemes have low computational complexity, and their effectiveness is verified through simulation results.

We present the design and fabrication of thermally-efficient tuning structures integrated into a narrowband reconfigurable radio-frequency-photonics filter using silicon-on-insulator waveguide optical delay lines. We are able to achieve IIR, FIR or mixed responses with < 100mW total tuning power.

We report the first monolithically integrated chip-to-chip WDM optical proximity coupler with 4-channel × 200GHz-spacing multiplexer/demultiplexer (MUX/DEMUX) functionality utilizing an echelle grating, micro reflective mirrors, and waveguide mode transformers on single silicon-on-insulator (SOI) substrate.

In this paper, we present a low-complexity Interference Aware Multiple Relay Selection (IAMRS) scheme for cognitive radio system. The main objective in selecting multiple relays is to maximize the SNR at the destination under the constraint of acceptable interference to the primary users (PU). The computational complexity of finding an optimal IAMRS by exhaustive search grows exponentially with the number of relays. In this paper, we propose an interference-aware greedy algorithm for the relay selection problem. The proposed algorithm has low computational complexity, and its effectiveness is verified through simulation results.

In this paper, we apply Estimation-of-Distribution Algorithms (EDAs) to the problem of selecting a set of k sensors from m sensors for the purpose of parameter estimation. Unlike other evolutionary algorithms, in EDAs a new population of individuals in each iteration is generated without crossover and mutation operators; instead, a new population is generated based on a probability distribution, which is estimated form the best selected individuals of previous iteration. Our results indicate that EDA is a good candidate for solving the sensor selection problems.

In this paper, we present a real-time low-complexity joint transmit and receive antenna selection (JTRAS) algorithm. The computational complexity of finding an optimal JTRAS by exhaustive search grows exponentially with the number of transmit and receive antennas. The proposed Estimation of Distribution Algorithm (EDA) is resorts to probabilistic distribution learning evolutionary computation. EDA updates its chosen population at each iteration on the basis of the probability distribution learned from the population of superior candidate solutions chosen at the previous iterations. The proposed EDA has a low computational complexity and can find a nearly optimal solution in real time. Beyond applying the general EDA to JTRAS, we also present a specific improvement to EDA, which reduces computation time by generating cyclic shifted initial population. The proposed EDA for JTRAS has a low computational complexity, and its effectiveness is verified through simulation results.

In this paper, we present an algorithm for joint symbol detection (JSD) at the receiver in a Multi-Device (MD) STBC-MIMO System. We apply Cross Entropy Optimization (CEO) to JSD problem in a MD-STBC-MIMO system. We find that this algorithm has good performance especially at a low SNR region in a rank-deficient MD-STBC-MIMO system. ML detector, Sphere Decoder (SD) and Semi-definite relaxation (SDR) are computationally complex. The proposed CEO detector finds a nearly optimal solution in real time and also requires very low amount of computation as compared to the ML, SD and SDR.

In this paper, we present a real-time low-complexity joint transmit and receive antenna selection algorithm for MIMO systems. We apply Ant Colony Optimization (ACO) to the joint antenna selection problem. The computational complexity of exhaustive search in this problem grows exponentially with the number of transmit and receive antennas. The proposed ACO for joint antenna selection has a low computational complexity, and its effectiveness is verified through simulation results.