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An Internet-of-Things (IoT) cellular network is considered in which IoT devices communicate with an IoT base station using IoT sub-bands placed between long-term evolution (LTE) bands. Due to spectral leakage, inter-band interference exists among IoT sub-bands and also between LTE and IoT bands. It is assumed that the IoT cellular network is responsible for reducing its interference to the LTE network to a certain threshold level. Under such interference regulation to LTE bands, we establish a joint sub-band assignment and power allocation optimization in order to maximize the sum rate of the IoT cellular network. A novel two-stage suboptimal algorithm that sequentially performs sub-band assignment and power control is proposed, reflecting the impact of spectral leakage in its optimization procedure. Simulation results demonstrate that the proposed algorithm considering the impact of spectral leakage outperforms the conventional optimization algorithms without considering spectral leakage. It is further shown that it provides almost the same sum rate achievable for a stand-alone network as if there were no LTE networks. [ABSTRACT FROM AUTHOR]

In this paper, we study the achievable degrees of freedom (DoFs) for $K$ -user multiple-input single-output broadcast channel (BC) in the absence of channel state information at the transmitter under the finite channel coherent time. For the considered $K$ -user BC, a transmitter is equipped with multiple conventional antennas, and receivers are equipped with a reconfigurable antenna that is capable of dynamically modifying the receiving beam radiation pattern. In this system, we propose a blind interference alignment scheme that is able to allocate an asymmetric number of information symbols to each user, which is essentially required to improve DoFs under limited symbol extension. A generalized systematic construction method of transmit beamforming vectors and receiving mode selection patterns to deal with such asymmetric allocation is established. As a consequence, for a broad class of network configurations and symbol extension constraints, the proposed scheme attains an improved sum DoF compared to the previous works, allowing only for the same number of information symbols for all users. [ABSTRACT FROM AUTHOR]

We consider a wireless device-to-device network, where $n$ nodes are uniformly distributed at random over the network area. We let each node caches M$ files from a library of size m\geq M$ . Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical “protocol model” of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions, including Zipf distributions with exponent less than one. In the parameter regime of interest, i.e., m=o(nM) for heavy-tailed popularity distributions. This scaling is constant with $n$ , thus yielding throughput scalability with the network size. Furthermore, the multihop capacity scaling can be significantly better than for the case of single-hop caching networks, for which the per-node capacity is $\Theta (M/m)$ . The multihop capacity scaling law can be further improved for a Zipf distribution with exponent larger than some threshold > 1, by using a decentralized random caching uniformly across a subset of most popular files in the library. Namely, ignoring a subset of less popular files (i.e., effectively reducing the size of the library) can significantly improve the throughput scaling while guaranteeing that all nodes will be served with high probability as $n$ increases. [ABSTRACT FROM PUBLISHER]

The $K$ -user multiple-input and multiple-output (MIMO) broadcast channel (BC) with no channel state information at the transmitter is considered, where each receiver is assumed to be equipped with either conventional antennas or reconfigurable antennas in which each reconfigurable antenna is capable of choosing a subset of receiving modes from several preset modes. Under general antenna configurations, the sum linear degrees of freedom (DoFs) of the $K$ -user MIMO BC with reconfigurable antennas are completely characterized, which corresponds to the maximum sum DoF achievable by linear coding strategies. [ABSTRACT FROM PUBLISHER]

An uplink–downlink two-cell cellular network is studied in which the first base station (BS) with M1 antennas receives independent messages from its N1 serving users, while the second BS with M2 antennas transmits independent messages to its N2 serving users. That is, the first and second cells operate as uplink and downlink, respectively. Each user is assumed to have a single antenna. Under this uplink–downlink setting, the sum degrees of freedom (DoFs) is completely characterized as the minimum of (N1N2+\min (M1,N1)(N1-N2)^{+}+\min (M2,N2)(N2-N1)^{+})/\max (N1,N2) , M1+N2,M2+N1 , \max (M1,M2) , and \max (N1,N2) , where a^+ denotes $\max (0,a)$ . The result demonstrates that, for a broad class of network configurations, operating one of the two cells as uplink and the other cell as downlink can strictly improve the sum DoF compared with the conventional uplink or downlink operation, in which both cells operate as either uplink or downlink. The DoF gain from such uplink–downlink operation is further shown to be achievable for heterogeneous cellular networks having hotspots and with delayed channel state information. [ABSTRACT FROM AUTHOR]

An interference management problem among multiple overlapped random access networks (RANs) is investigated, each of which operates with a slotted ALOHA protocol. Assuming that access points and users have multiple antennas, a novel opportunistic interference alignment (OIA) is proposed to mitigate interference among overlapped RANs. The proposed technique intelligently combines the transmit beamforming technique at the physical layer and the opportunistic packet transmission at the medium access control layer. Transmit beamforming is based on interference alignment, and opportunistic packet transmission is based on the generating interference of users to other RANs, which can be regarded as a joint optimization of the physical layer and the medium access control layer. It is shown that the proposed OIA protocol significantly outperforms the conventional schemes such as multipacket reception and interference nulling. [ABSTRACT FROM PUBLISHER]

In wireless sensor networks, various applications involve learning one or multiple functions of the measurements observed by sensors, rather than the measurements themselves. This paper focuses on the class of type-threshold functions, e.g., the maximum and the indicator functions. A simple network model capturing both the broadcast and superposition properties of wireless channels is considered: the collocated Gaussian network. A general multiround coding scheme exploiting superposition and interaction (through broadcast) is developed. Through careful scheduling of concurrent transmissions to reduce redundancy, it is shown that given any independent measurement distribution, all type-threshold functions can be computed reliably with a nonvanishing rate in the collocated Gaussian network, even if the number of sensors tends to infinity. [ABSTRACT FROM AUTHOR]

Function computation over Gaussian networks with orthogonal components is studied for arbitrarily correlated discrete memoryless sources. Two classes of functions are considered: 1) the arithmetic sum function and 2) the type function. The arithmetic sum function in this paper is defined as a set of multiple weighted arithmetic sums, which includes averaging of the sources and estimating each of the sources as special cases. The type or frequency histogram function counts the number of occurrences of each argument, which yields various fundamental statistics, such as mean, variance, maximum, minimum, median, and so on. The proposed computation coding first abstracts Gaussian networks into the corresponding modulo sum multiple-access channels via nested lattice codes and linear network coding and then computes the desired function using linear Slepian–Wolf source coding. For orthogonal Gaussian networks (with no broadcast and multiple-access components), the computation capacity is characterized for a class of networks. For Gaussian networks with multiple-access components (but no broadcast), an approximate computation capacity is characterized for a class of networks. [ABSTRACT FROM AUTHOR]

The capacity scaling laws of two overlaid networks are investigated, which are located in the same area sharing the same wireless resources with different priorities. The primary network can be regarded as an existing communication system operated in a licensed band and, therefore, is assumed to operate in an order-optimal fashion to achieve its standalone capacity scaling law. The secondary cognitive network must keep its interference to the primary network below a certain threshold while at the same time maximizing its own throughput scaling law based on cognition information. The existing scaling results for cognitive networks inherently assume multihop communication, which is a restricted coding model. By contrast, in this paper, a general coding model is considered without any specific physical layer coding assumptions. The capacity scaling exponents for both networks are analyzed when the numbers of primary nodes n , primary base stations l , which support the communication between primary nodes, and secondary nodes m increase with the relations , \beta >1 , and l=n^\gamma , 0\leq \gamma \leq 1 . For the extended network model, the capacity scaling exponents are completely characterized as \max \2-\alpha /2,1/2,\gamma \ and \max \2-\alpha /2,1/2\ for the primary and secondary networks respectively, where \alpha >2 denotes the path-loss exponent. That is, the capacity scaling laws for the primary and secondary networks are represented, respectively, by n^{\max \{2-\alpha /2,1/2,\gamma \}\pm \epsilon } and m^{\max \{2-\alpha /2,1/2\}\pm \epsilon }$ for \epsilon >0$ arbitrarily small. For the dense network model, when the primary network achieves its standalone capacity scaling exponent of 1, the secondary network is shown to achieve a scaling exponent of 1-{1}/({2\beta }) , which improves the previous scaling exponent of 1/2 achieved by multihop. For both models, it turns out that the conventional multihop approach is in general quite suboptimal. [ABSTRACT FROM AUTHOR]

The fading AWGN two-user two-hop network is considered where the channel coefficients are independent and identically distributed (i.i.d.) according to a continuous distribution and vary over time. For a broad class of channel distributions, the ergodic sum capacity is characterized to within a constant number of bits/second/hertz, independent of the signal-to-noise ratio. The achievability follows from the analysis of an interference neutralization scheme where the relays are partitioned into M pairs, and interference is neutralized separately by each pair of relays. When M=1, the proposed ergodic interference neutralization characterizes the ergodic sum capacity to within 4 bits/sec/Hz for i.i.d. uniform phase fading and approximately 4.7 bits/sec/Hz for i.i.d. Rayleigh fading. It is further shown that this gap can be tightened to 4\log\pi-4 bits/sec/Hz (approximately 2.6) for i.i.d. uniform phase fading and 4\-4\log (3\pi/8) bits/sec/Hz (approximately 3.1) for i.i.d. Rayleigh fading in the limit of large M. ref refid="fn1"/ id="fn1" asterisk="no"paraThroughout this paper, log(.) denotes the logarithm of base two.para [ABSTRACT FROM AUTHOR]

In this paper, we study a layered linear binary field network with time-varying channels, which is a simplified model reflecting broadcast, interference, and fading natures of wireless communications. We observe that fading can play an important role in mitigating interuser interference effectively for both single-hop and multihop networks. We propose new coding schemes with randomized ergodic channel pairing, which exploit such channel variations, and derive their achievable ergodic rates. By comparing them with the cut-set upper bound, the capacity region of single-hop networks and the sum capacity of multihop networks are characterized for some classes of channel distributions and network topologies. [ABSTRACT FROM AUTHOR]

We consider a wireless network in which a single-source node having m antennas transmits independent messages to n destination nodes, where each destination node has a single antenna. We assume that the source is located at the center of a unit area and the destinations are located uniformly at random in the same area. By applying transmit beamforming at the source, an achievable sum rate can scale as \min \m,n\, which is defined by the aggregate rate of all messages. For transmit beamforming, however, channel state information (CSI) is essentially required at the transmitter (CSIT), which is hard to acquire in practice because of the time-varying nature of wireless channels and feedback overhead. We show that, even without CSIT, almost the same sum rate scaling law assuming CSIT is achievable by inducing cooperation between destinations. Specifically, we study the sum rate scaling law when both the number m of the source antennas and the number n of destinations increase such that n=m^\beta for \beta >0. If \beta >1 the optimal sum rate scales as m\log m and if 0< \beta \leq 1 the optimal sum rate scales between m^\beta (1-\epsilon) and m^\beta\log m, where \epsilon >0 is an arbitrarily small constant, which shows significant improvement compared to the case of no cooperation between destinations. Our result is of particular interest because we did not assume any additional bandwidth for cooperation. [ABSTRACT FROM PUBLISHER]

In recent years, there has been rapid progress on understanding Gaussian networks with multiple unicast connections, and new coding techniques have emerged. The essence of multi-source networks is how to efficiently manage interference that arises from the transmission of other sessions. Classically, interference is removed by orthogonalization (in time or frequency). This means that the rate per session drops inversely proportional to the number of sessions, suggesting that interference is a strong limiting factor in such networks. However, recently discovered interference management techniques have led to a paradigm shift that interference might not be quite as detrimental after all. The aim of this paper is to provide a review of these new coding techniques as they apply to the case of time-varying Gaussian networks with multiple unicast connections. Specifically, we review interference alignment and ergodic interference alignment for multi-source single-hop networks and interference neutralization and ergodic interference neutralization for multi-source multi-hop networks. We mainly focus on the ”degrees of freedom” perspective and also discuss an approximate capacity characterization. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact and benefits of infrastructure support in improving the throughput scaling in networks of n randomly located wireless nodes. The infrastructure uses multiantenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbitrary rates relative to n. Under the model, capacity scaling laws are analyzed for both dense and extended networks. Two BS-based routing schemes are first introduced in this study: an infrastructure-supported single-hop (ISH) routing protocol with multiple-access uplink and broadcast downlink and an infrastructure-supported multihop (IMH) routing protocol. Then, their achievable throughput scalings are analyzed. These schemes are compared against two conventional schemes without BSs: the multihop (MH) transmission and hierarchical cooperation (HC) schemes. It is shown that a linear throughput scaling is achieved in dense networks, as in the case without help of BSs. In contrast, the proposed BS-based routing schemes can, under realistic network conditions, improve the throughput scaling significantly in extended networks. The gain comes from the following advantages of these BS-based protocols. First, more nodes can transmit simultaneously in the proposed scheme than in the MH scheme if the number of BSs and the number of antennas are large enough. Second, by improving the long-distance signal-to-noise ratio (SNR), the received signal power can be larger than that of the HC, enabling a better throughput scaling under extended networks. Furthermore, by deriving the corresponding information-theoretic cut-set upper bounds, it is shown under extended networks that a combination of four schemes IMH, ISH, MH, and HC is order-optimal in all operating regimes. [ABSTRACT FROM PUBLISHER]

Two distinct, but overlapping, networks that operate at the same time, space, and frequency is considered. The first network consists of n randomly distributed primary users, which form an ad hoc network. The second network again consists of m randomly distributed ad hoc secondary users or cognitive users. The primary users have priority access to the spectrum and do not need to change their communication protocol in the presence of the secondary users. The secondary users, however, need to adjust their protocol based on knowledge about the locations of the primary users to bring little loss to the primary network's throughput. By introducing preservation regions around primary receivers, a modified multihop routing protocol is proposed for the cognitive users. Assuming m=n^\beta with \beta>1, it is shown that the secondary network achieves almost the same throughput scaling law as a stand-alone network while the primary network throughput is subject to only a vanishingly small fractional loss. Specifically, the primary network achieves the sum throughput of order n^1/2 and, for any \delta>0, the secondary network achieves the sum throughput of order m^1/2-\delta with an arbitrarily small fraction of outage. Thus, almost all secondary source-destination pairs can communicate at a rate of order m^-1/2-\delta. [ABSTRACT FROM PUBLISHER]

We study a layered K-user M-hop Gaussian relay network consisting of Km nodes in the m^th layer, where M\geq 2 and K=K1=KM+1. We observe that the time-varying nature of wireless channels or fading can be exploited to mitigate the interuser interference. The proposed amplify-and-forward relaying scheme exploits such channel variations and works for a wide class of channel distributions including Rayleigh fading. We show a general achievable degrees of freedom (DoF) region for this class of Gaussian relay networks. Specifically, the set of all (d1,\ldots , dK) such that di\leq 1 for all i and \sum i=1^{K} di\leq K\Sigma is achievable, where di is the DoF of the i^th source-destination pair and K\Sigma is the maximum integer such that K\Sigma \leq \mathop min\limits m\{Km\} and M/K\Sigma is an integer. We show that surprisingly the achievable DoF region coincides with the cut-set outer bound if M/ \mathop min\limits m\{Km\} is an integer; thus, interference-free communication is possible in terms of DoF. We further characterize an achievable DoF region assuming multi-antenna nodes and general message set, which again coincides with the cut-set outer bound for a certain class of networks. [ABSTRACT FROM PUBLISHER]

Wireless device-to-device (D2D) caching networks are studied, in which n nodes are distributed uniformly at random over the network area. Each node caches M files from the library of size m ≥ M and independently requires a file from the library. Each request will be served by cooperative D2D transmission from other nodes having the requested file in their cache memories. In many practical sensor or Internet of things (IoT) networks, there may exist simple sensor or IoT devices that are not able to perform real-time rate and power control based on the reported channel quality information (CQI). Hence, it is assumed that each node transmits a file with a fixed rate and power so that an outage is inevitable. To improve the outage-based throughput, a cache-enabled interference cancellation (IC) technique is proposed for cooperative D2D file delivery which first performs IC, utilizing cached files at each node as side information, and then performs successive IC of strongly interfering files. Numerical simulations demonstrate that the proposed scheme significantly improves the overall throughput and, furthermore, such gain is universally achievable for various caching placement strategies such as random caching and probabilistic caching. [ABSTRACT FROM AUTHOR]

In this paper, we study an UAV base station (UBS) assisted cellular network that consists of a single ground macro base station (MBS), multiple UBSs, and multiple ground terminals. We assume that the MBS transmits to the UBSs and the terminals in the licensed band while the UBSs use a separate unlicensed band (e.g., Wi-Fi) to transmit to the terminals. For the utilization of the UBSs, we propose a cooperative decode–forward (DF) protocol in which multiple UBSs assist the terminals simultaneously while maintaining a predetermined interference level on the coexisting unlicensed band users. For our network setup, we formulate a joint optimization problem for minimizing the aggregate gap between the target rates and the throughputs of terminals by optimizing over the 3D positions of the UBSs and the resources (power, time, bandwidth) of the network. To solve the optimization problem, we propose an efficient nested structured algorithm based on particle swarm optimization and convex optimization methods. Extensive numerical evaluations of the proposed algorithm is performed considering various aspects to demonstrate the performance of our algorithm and the gain for utilizing UBSs. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods. [ABSTRACT FROM AUTHOR]

High-dimensional optimization problems are more and more common in the era of big data and the Internet of things (IoT), which seriously challenge the optimization performance of existing optimizers. To solve these kinds of problems effectively, this paper devises a dimension group-based comprehensive elite learning swarm optimizer (DGCELSO) by integrating valuable evolutionary information in different elite particles in the swarm to guide the updating of inferior ones. Specifically, the swarm is first separated into two exclusive sets, namely the elite set (ES) containing the top best individuals, and the non-elite set (NES), consisting of the remaining individuals. Then, the dimensions of each particle in NES are randomly divided into several groups with equal sizes. Subsequently, each dimension group of each non-elite particle is guided by two different elites randomly selected from ES. In this way, each non-elite particle in NES is comprehensively guided by multiple elite particles in ES. Therefore, not only could high diversity be maintained, but fast convergence is also likely guaranteed. To alleviate the sensitivity of DGCELSO to the associated parameters, we further devise dynamic adjustment strategies to change the parameter settings during the evolution. With the above mechanisms, DGCELSO is expected to explore and exploit the solution space properly to find the optimum solutions for optimization problems. Extensive experiments conducted on two commonly used large-scale benchmark problem sets demonstrate that DGCELSO achieves highly competitive or even much better performance than several state-of-the-art large-scale optimizers. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated optimization problems with many local areas. In PSO, the interaction among particles and utilization of the communication information play crucial roles in improving the learning effectiveness and learning diversity of particles. To promote the communication effectiveness among particles, this paper proposes a stochastic triad topology to allow each particle to communicate with two random ones in the swarm via their personal best positions. Then, unlike existing studies that employ the personal best positions of the updated particle and the neighboring best position of the topology to direct its update, this paper adopts the best one and the mean position of the three personal best positions in the associated triad topology as the two guiding exemplars to direct the update of each particle. To further promote the interaction diversity among particles, an archive is maintained to store the obsolete personal best positions of particles and is then used to interact with particles in the triad topology. To enhance the chance of escaping from local regions, a random restart strategy is probabilistically triggered to introduce initialized solutions to the archive. To alleviate sensitivity to parameters, dynamic adjustment strategies are designed to dynamically adjust the associated parameter settings during the evolution. Integrating the above mechanism, a stochastic triad topology-based PSO (STTPSO) is developed to effectively search complex solution space. With the above techniques, the learning diversity and learning effectiveness of particles are largely promoted and thus the developed STTPSO is expected to explore and exploit the solution space appropriately to find high-quality solutions. Extensive experiments conducted on the commonly used CEC 2017 benchmark problem set with different dimension sizes substantiate that the proposed STTPSO achieves highly competitive or even much better performance than state-of-the-art and representative PSO variants. [ABSTRACT FROM AUTHOR]

Hybridizing multiple mutation strategies has shown much effectiveness in helping differential evolution (DE) algorithms achieve good optimization performance. Though abundant adaptive ensemble strategies have been developed to adaptively employ multiple mutation strategies to evolve the population, most of them ignore to make full use of the properties and characteristics of the multiple mutation strategies. To fill this gap, this paper devises a bi-directional ensemble scheme for DE to adaptively assemble totally 8 mutation strategies with different properties and characteristics. As a result, a novel DE, which we call bi-directional ensemble DE (BDEDE), is developed. Specifically, this paper sorts the 8 mutation strategies roughly from two opposite perspectives, namely the convergence and the diversity. Then, we assign each mutation strategy with two different non-linear probabilities, which are calculated on the basis of its two rankings obtained from the two perspectives. Subsequently, to make full use of these mutation schemes, we first partition the whole population into two separate parts, namely elite individuals and non-elite individuals. Then, for each elite individual, we randomly select a mutation strategy from the 8 candidates based on the probabilities calculated by the convergence rankings, while for each non-elite individual, we stochastically choose a mutation scheme from the same 8 candidates but based on the probabilities computed by the diversity rankings. In this manner, the elite individuals prefer to exploit the located optimal areas, while the non-elite individuals tend to explore the solution space. Therefore, it is likely that BDEDE expectedly maintains a good balance between search diversity and search convergence. To further help BDEDE achieve such a purpose, this paper devises an adaptive partition strategy to dynamically separate the whole population into the two categories. With the above two techniques, BDEDE anticipatedly obtains good optimization performance. To verify its effectiveness and efficiency, we conduct experiments on the CEC2014 and the CEC2017 benchmark sets by comparing BDEDE with totally 14 well-known and state-of-the-art DE variants. Experimental results have shown that BDEDE performs competitively with or even significantly better than the 14 compared DE variants. The source code of BDEDE can be downloaded from https://gitee.com/mmmyq/BDEDE. [ABSTRACT FROM AUTHOR]

Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants. [ABSTRACT FROM AUTHOR]

Optimization problems are ubiquitous in every field, and they are becoming more and more complex, which greatly challenges the effectiveness of existing optimization methods. To solve the increasingly complicated optimization problems with high effectiveness, this paper proposes an adaptive covariance scaling estimation of distribution algorithm (ACSEDA) based on the Gaussian distribution model. Unlike traditional EDAs, which estimate the covariance and the mean vector, based on the same selected promising individuals, ACSEDA calculates the covariance according to an enlarged number of promising individuals (compared with those for the mean vector). To alleviate the sensitivity of the parameters in promising individual selections, this paper further devises an adaptive promising individual selection strategy for the estimation of the mean vector and an adaptive covariance scaling strategy for the covariance estimation. These two adaptive strategies dynamically adjust the associated numbers of promising individuals as the evolution continues. In addition, we further devise a cross-generation individual selection strategy for the parent population, used to estimate the probability distribution by combing the sampled offspring in the last generation and the one in the current generation. With the above mechanisms, ACSEDA is expected to compromise intensification and diversification of the search process to explore and exploit the solution space and thus could achieve promising performance. To verify the effectiveness of ACSEDA, extensive experiments are conducted on 30 widely used benchmark optimization problems with different dimension sizes. Experimental results demonstrate that the proposed ACSEDA presents significant superiority to several state-of-the-art EDA variants, and it preserves good scalability in solving optimization problems. [ABSTRACT FROM AUTHOR]

We show that the 2\,\times \,2\,\times \,2 interference network, i.e., the multihop interference network formed by concatenation of two two-user interference channels achieves the min-cut outer bound value of 2 DoF, for almost all values of channel coefficients, for both time-varying or fixed-channel coefficients. The key to this result is a new idea, called aligned interference neutralization, that provides a way to align interference terms over each hop in a manner that allows them to be canceled over the air at the last hop. [ABSTRACT FROM PUBLISHER]