Machine learning augmented by domain knowledge plays a critical role, particularly as data volume grows and network architectures become increasingly complex. This work explores the extraction and integration of domain knowledge into SEM image denoising. We introduce a Knowledge-enhanced Noise2Noise (K-N2N) strategy to address the absence of clean SEM images. A novel SEM noise model is proposed, based on the revealed linear correlation between the variance and mean intensity of noisy image pixels. Leveraging this model, two noise maps are sampled as prior knowledge to train the K-N2N strategy. We theoretically demonstrate that K-N2N, with single-shot noisy images enhanced by prior knowledge, is equivalent to the Noise2Noise (N2N) [1] approach using multi-shot noisy pairs. K-N2N is established as a universal neural network training paradigm. Extensive experiments on SEM image denoising, performed without clean data, validate both the proposed noise model and the K-N2N strategy. This work advances the understanding of machine learning powered by domain knowledge and its application in SEM image denoising.

The cell-free integrated sensing and communication (ISAC) system, which collaborates multiple access points (AP), offers superior sensing and communication performance compared to single base station ISAC system. However, when line-of-sight (LoS) link does not exist between AP and the target, the sensing performance is significantly degraded thus the energy dedicated for sensing is wasted. To address this issue, we propose a two-stage design. In the identification stage, APs transmits omnidirectional beams to identify the LoS link between AP and the target. In the subsequent ISAC stage, if a LoS link exists, AP transmits ISAC signals. Otherwise only communication signals are transmitted. The detection probability of LoS identification is derived. Additionally, we propose a joint time and power allocation algorithm that minimizes transmit power while ensuring the minimum detection probability, communication throughput, sensing signal-to-interference-plus-noise ratio (SINR), and the maximum transmit power requirements. Numerical results demonstrate the proposed two-stage design outperforms state-of-the-art schemes without joint optimization and two-stage design.

The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or data-driven schemes, the proposed MDDRA framework employs a model-driven Lyapunov queue to accumulate long-term historical mismatch information and a data-driven Graph Radial bAsis Fourier (GRAF) network to predict the traffic variations under incomplete data, and hence eliminates the reliance on high-precision models and complete spatial-temporal traffic data. We establish the universal approximation property of the proposed GRAF network and provide convergence and complexity analysis for the MDDRA algorithm. Numerical experiments validate the performance gains achieved through the data-driven and model-driven components. By analyzing IREE and EE curves under diverse traffic conditions, we recommend that network operators shall spend more efforts to balance the traffic demand and the network capacity distribution to ensure the network performance, particularly in scenarios with large speed limits and higher driving visibility.

Hotspot detection has become a challenging problem in modern Design For Manufacture (DFM) streams with the decreasing of the minimum feature size of chips. While machine learning has been widely and successfully employed for hotspot detection, its performance may still degrade if inaccurate feature extraction methods are used. In this paper, we propose a hotspot detection method that utilizes feature fusion with the R-EfficientNet model to address the aforementioned issues. Experimental results on ICCAD 2012 Contest benchmarks and ICCAD 2019 Contest benchmarks shows that the F1 score can be up to 0.724 and the false alarm rate is reduced to 22.69%. Compared with the most advanced machine learning-based methods, our proposed method achieves better performance.

Bertsekas recently proposed Asynchronous Policy Iteration (API) as an alternative algorithm of Policy Iteration (PI) for solving the problem of two-player zero-sum Markov games. To quantifying the benefits of API, besides its flexibility for parallel and asynchronous implementation, the focus of this paper is to derive the computational complexity of API. We show that to reach within ϵ error to the optimal value function, the computational complexity of API is at most O (poly (n, m1, m2, ln(1/(1 − γ))), where n is the number of states, m1, m2are the number of actions for player 1 and player 2 respectively, and γ is the discount factor.

The low-cost Neural Architecture Search (NAS) for lightweight networks working on massive mobile devices is essential for fast-developing ICT technology. Current NAS work can not search on unseen devices without latency sampling, which is a big obstacle to the implementation of NAS on mobile devices. In this paper, we overcome this challenge by proposing the Device Generic Latency (DGL) model. By absorbing processor modeling technology, the proposed DGL formula maps the parameters in the interval theory to the seven static configuration parameters of the device. And to make the formula more practical, we refine it to low-cost form by decreasing the number of configuration parameters to four. Then based on this formula, the DGL model is proposed which introduces the network parameters predictor and accuracy predictor to work with the DGL formula to predict the network latency. We propose the DGL-based NAS framework to enable fast searches without latency sampling. Extensive experiments results validate that the DGL model can achieve more accurate latency predictions than existing NAS latency predictors on unseen mobile devices. When configured with current state-of-the-art predictors, DGL-based NAS can search for architectures with higher accuracy that meet the latency limit than other NAS implementations, while using less training time and prediction time. Our work shed light on how to adopt domain knowledge into NAS topic and play important role in low-cost NAS on mobile devices.

Neural Architecture Search (NAS) has emerged as a favoured method for unearthing effective neural architectures. Recent development of large models has intensified the demand for faster search speeds and more accurate search results. However, designing large models by NAS is challenging due to the dramatical increase of search space and the associated huge performance evaluation cost. Consider a typical modular search space widely used in NAS, in which a neural architecture consists of $m$ block nodes and a block node has $n$ alternative blocks. Facing the space containing $n^m$ candidate networks, existing NAS methods attempt to find the best one by searching and evaluating candidate networks directly. Different from the general strategy that takes architecture search as a whole problem, we propose a novel divide-and-conquer strategy by making use of the modular nature of the search space. Here, we introduce MathNAS, a general NAS framework based on mathematical programming. In MathNAS, the performances of all possible building blocks in the search space are calculated first, and then the performance of a network is directly predicted based on the performances of its building blocks. Although estimating block performances involves network training, just as what happens for network performance evaluation in existing NAS methods, predicting network performance is completely training-free and thus extremely fast. In contrast to the $n^m$ candidate networks to evaluate in existing NAS methods, which requires training and a formidable computational burden, there are only $m*n$ possible blocks to handle in MathNAS. Therefore, our approach effectively reduces the complexity of network performance evaluation. The superiority of MathNAS is validated on multiple large-scale CV and NLP benchmark datasets. Notably on ImageNet-1k, MathNAS achieves 82.5\% top-1 accuracy, 1.2\% and 0.96\% higher than Swin-T and LeViT-256, respectively. In addition, when deployed on mobile device, MathNAS achieves real-time search and dynamic network switching within 1s (0.4s on TX2 GPU), surpassing baseline dynamic networks in on-device performance.

Channel Charting (CC) is a newly developing framework, which aims to learn the low dimensional representation of radio geometry from the channel state information (CSI) collected in the region of interest. But one key question, how soon will channel charting be inapplicable in user moving scenarios, even in certain setting? remains open. In this paper we present some answers by designing corresponding experiments with single cell and multi cell scenarios in LOS channel and multi-path channel settings. Our experimental results suggest that, in the communication scheme with user moving speed = 10 m/s, which is much faster than human walking, the constructed channel charting can provide up to 30 seconds support for future channel estimation. Our work shows that, machine learning and wireless big data based wireless channel learning may support complex communication tasks, especially for massive MIMO system in future communication systems.

Federated learning (FL) is an emerging technique to tackle the problem of isolated data islands. Vanilla FL usually relies on a centralized topology with a synchronous communication setup, where a parameter server coordinates the distributed users by periodically receiving the model updates from them, performing the aggregation to build a global model and sending back the aggregated model to those users. These restrictions however make FL become vulnerable to the single point failure and not efficient enough given the heterogeneous device capabilities and communication conditions. This paper sheds light on enabling FL in a decentralized topology with an asynchronous communication setup. We first incorporate the asynchronous first-order dynamic average consensus into FL and propose an underlying Async-DFL algorithm. Then, to alleviate the staleness effect in Async-DFL, we introduce a hyper-parameter into Async-DFL and further devise an optimized hyper-mix Async-DFL solution. The experimental result demonstrates the feasibility of these approaches and shows that the hyper-mix solution performs better than the underlying Async-DFL.

Estimating 6D pose of the object from a single image is es-sential for robotic manipulation. Many recent learning-based methods directly regress the pose from 2D-3D points corre-spondence. The problem is that, these methods only make use of visible information from the single-view image, resulting ambiguity for the network to solve pose from the limited cor-responding pairs. To overcome this problem, this paper intro-duces INVNet, integrating invisible information into the visi-ble 2D-3D correspondence to model geometry features of the 3D object. Instead of directly reconstruct the coordinate of in-visible points, we propose Trace-level Geometry Path, which estimates the trace-level depth of the object model for each image pixel. Specifically, our INVNet generates dense visible correspondence as well as Trace-level Geometry Path map, then learn to solve 6D pose from them. Meanwhile, each cam-era ray along with Trace-level Geometry Path is transformed to the object space by the predicted pose to compute invisi-ble correspondence loss from visible one, back to enhance its learning. Extensive experiments show that our approach out-performs state-of-the-art methods on the benchmark LM and LM-O datasets.

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier. Besides, the RBCom system possesses the characteristics of mobility, high signal-to-noise ratio (SNR), and multiplexing. Nevertheless, the channel of the RBCom system is distinct from other light communication technologies due to the echo interference issue. In this article, we reveal the mechanism of the echo interference and propose the method to eliminate the interference. Moreover, we present an exemplary design based on frequency shifting and optical filtering, along with its mathematic model and performance analysis. The numerical evaluation shows that the channel capacity is greater than 15 b/s/Hz.

WiFi fingerprinting-based indoor localization system is extensively researched with the advent of the high-density wireless networks deployment, but is limited by heavy site survey in the offline phase, for which fingerprint augment is an effective solution. In this paper, we innovatively propose a fingerprint augment method based on super-resolution (FASR) and formulate the processing framework. In order to perform super-resolution on sparse fingerprint database, the conversions between WiFi fingerprint data and fingerprint images are proposed. EDSR, a method based on deep learning in super-resolution, is adopted in FASR to obtain high-resolution fingerprint images, which are then reconstructed to augmented fingerprint database. Experiments on simulated and real scenarios verified the feasibility of FASR. Our work demonstrates a new application of machine learning in wireless communication.

Federated learning(FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less uplink transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.

Voice traffic prediction is significant for network deployment optimization thus to improve the network efficiency. The real entropy based theorectical bound and corresponding prediction models have demonstrated their success in mobility prediction. In this paper, the real entropy based predictability analysis and prediction models are introduced into voice traffic prediction. For this adoption, the traffic quantification methods is proposed and discussed. Based on the real world voice traffic data, the prediction accuracy of N-order Markov models, diffusion based model and MF model are presented, among which, 25-order Markov models performs best and approach close to the maximum predictability. This work demonstrates that, the real entropy can also predict voice traffic well which broaden the understanding on the real entropy based prediction theory.

We propose content compression coding for massive users, where correlated contents of grouped users can be utilized to achieve high compression efficiency. We divide the users into several groups. For each group, we select the group head, encode the information of group head independently, and encode other users referring to the group head. We formulate the grouping optimization problem, transform it to a zero-one discrete optimization problem as well as a subset selection problem, and provide upper and lower bounds on the compression ratio. We investigate the sufficient condition and necessary condition on the optimality of one user group. We also investigate two specific type of sources, with star-type and chain-type user statistics, and provide the optimal user grouping. The compression ratio is also studied from both theoretical and experimental perspectives.

Multiple access technology is facing a great challenge posed by the upcoming massive Machine-Type Communication (mMTC). Traditional grant-based random access (RA) schemes are not competent to tackle the massive connectivity and small infrequent data packets of mMTC, because of the intolerable signalling overhead and delay. Grant-free random access joint with non-orthogonal multiple access (NOMA) may be suitable for the mMTC services as it reduces the signaling overhead. However, user collisions are still possible and damage the system's performances. In this paper, we propose a grouping-based grant-free random access based on statistical user rate information in which the base station (BS) divides MTDs into several groups according to the users' rate distribution and allocates proper radio resources for each group. BS broadcasts this grouping determinations to all MTDs with a small amount of signaling and MTD then randomly selects a group to access the channel on their own instant data rates. We analyze the blocking rate and provide an optimization method for deciding the grouping policy. Simulations prove the proposed scheme outperforms the traditional non-grouping RA even if there are some estimation errors.

Mobile self-organizing networks, such as vehicular ad-hoc network (VANET), in most cases rely on infrastructure deployment to provide access to internet services and other resource. Therefore, it is crucial to optimize the deployment of roadside units (RSUs) in vehicle network. In this paper, we propose a RSU optimized deployment scheme based on large vehicle data, which considers the deployment cost and latency performance synthetically. We deploy the RSU problem as a multiobjective optimization problem for mathematical modeling. On this basis, two-step solution is proposed: firstly, the RSU candidate positions can be obtained by considering the road topology and large vehicle data; secondly, the branch and bound algorithm is used to obtain the better RSU deployment based on the mathematical model. The simulation results show that the proposed RSU deployment scheme uses a small amount of RSU can achieve high coverage and greatly reduce the deployment cost. Moreover, the low latency performance of the vehicle access network and the quality of the latency sensitive application service can also be ensured.

Human mobility is an interesting topic attracting researchers from various fields. In this paper, we investigate mobility of college students in campus during their full life cycle and find the demographic differences on the predictability of human mobility. We explore an extensive consuming record data set collected from a college campus. Specifically, our data set includes over 15 million consuming logs gathered from 4,741 students (with demographic information) from 2013 to 2016. We present and analyze the evolving patterns of mobility entropy by ages, academic majors, and genders. We find that entropy decreases with age increasing and entropy grouped by genders follows different distributions, which is opposite to previous studies. Our findings will supply novel insights in human mobility research and provide possible support for wireless networking design in campus.

Future fifth generation (5G) wireless networks will be challenged by the huge amount of mobile data traffic, especially from enormous Machine Type Communication(MTC) devices, so in this paper we proposed Aggregation Coding Multiple Access(ACMA) for MTC downlink transmissions, which exploits the inherent correlation among aggregated users in spatial domain to improve the spectrum efficiency. By assuming the data traffic together with the delay requirements of MTC devices can be perfectly predicted based on wireless big data analysis, the transmission order of multicast downlink data traffic from base stations to multiple terminals could be adjusted by proposed aggregation coding technique using much less timeslots. We then evaluated the performance of conditional random search(CRS), standard-row algorithm(SRA) and Genetic Algorithm(GA) to optimize the data traffic transmission order compared to Multimedia Broadcast Multicast Service (MBMS) and simulation results validated the performance of ACMA and the optimization of GA. Our work shed light on dealing with massive MTC data traffic for future wireless communications.

The explosive growth of cellular data traffic demands poses a heavy burden to the cellular service provider (CSP), making it urgent to offload part of the cellular traffic through already-deployed third-party owned small cells. Auction based offloading is a promising method of traffic offloading, but the existing related works haven't considered the utilization efficiency of the leased spectrum resource. In this paper, we present and analyze a reverse auction to deploy an innovative market for tradeoff between the lower leasing cost and larger amount of offloading traffic, aiming to maximize the benefit of CSPs. Furthermore, we propose a greedy offloading mechanism to reach the tradeoff and spectrum efficiency with low complexity. The simulation results demonstrate that the proposed greedy mechanism can make a good compromise between the offloading cost and the offloading traffic.

Satellite communication plays an important role in the ubiquitous global communication. However, for a long time, the communication provided by satellite suffers from serious signal attenuation and transmission delay. Conventional techniques such as HARQ couldn't provide timely service unless powerful channel codes are used. Once channel code fails, retransmission will be inevitable, and unbearable delay may be incurred. Fortunately, things started to change when Luby proposed the landmark LT code. As a practical implementation of fountain code, LT code avoids frequent feedback during the transmission. However, due to the high packet error rate (PER), the original LT code couldn't work stably in satellite communication. In this paper, we propose a novel hybrid decoding scheme for LT code. It consists of two decoding procedures: normal decoding using BP algorithm and mining decoding. The idea behind mining decoding is to release as many corrupted original packets as possible and combine the "identical" ones as in HARQ. Actually we develop three types of packet mining techniques to make a tradeoff between performance and computation complexity. The final simulation shows that, the proposed hybrid decoding scheme makes LT code work more stably, and achieves significant advantages over HARQ in terms of delay.

In this paper, we conduct a detailed analysis on voice traffic intensity measurement of large-scale mobile communication base stations. Our data set tracks over 700 base stations inside one city in Southern China over a continuous period of 18 days. We cluster base stations with k-means method based on their average traffic and the result shows four distinctive clusters, among which one cluster consisting of base stations near university campuses reveals unique calling behavior of college students. The spatial characteristics of base station traffic from global and local perspectives demonstrates that the highest global spatial correlation is around 6 AM and local non-stationarity is identified. Our insights of spatial-temporal traffic dynamics in mobile network can be leveraged into network monitoring and resource management.

One of the most critical issues in two-tier femtocell networks is the inter-cell interference management, in which Femtocell Base Station (FBS) locating in the edge area of the Macrocell Base Station (MBS) coverage suffers from interference produced by adjacent Macrocell User (MU), making this area a “Dead Zone” to the FBS. In this paper, we study the uplink cross-tier interference for “Dead Zone” problem in closed access femtocell networks from the aspect of resource allocation based on orthogonal frequency division multiple access (OFDMA). We propose a resource-reallocation based strategy to mitigate the cross-tier interference, which reassigns the subchannels and power among MUs and the impaired FUs after the latter ones handover from the serving FBS to the MBS. Theoretical analysis and simulation results confirm that higher throughput can be achieved by our proposed strategy.

The forwarding activities of Pocket Switched Network (PSN) under social scene has attracted increasing attention, which are deeply affected by the social features of PSN nodes, especially the social network structure and characteristics of selfishness among nodes that jointly construct their behavior model under an opportunistic forwarding environment. Through both analysis and simulation, we investigate how social network structure and node selfishness influence data forwarding activities of PSN nodes based on our proposed contact-and-cooperative model. Simulation results obtained by PROPHET algorithm show that PSN forwarding performance, including delay time and packet delivery ratio, are improved by the introduction of social network structure. Further, the differentiation of node selfishness according to nodes' relationships on social structure level also displays intrinsic benefit in network performance in the presence of the node selfish behavior that is inevitable in real world. The results also show that hub nodes in the network, which undertake heavy relay mission, may play a more important role in forwarding activities under our model with social selfishness compared with unconditionally cooperative cases.

Time-varying property of practical wireless channels affects the performance of cooperative protocol in multi-relay networks. In this paper, we evaluate and compare four decode-and-forward (DF) retransmission strategies over time-correlated Rayleigh channels, which are the combinations of two relay selection (RS) schemes and two repeater switching patterns: RS can be based on either statistical or outdated channel state information (CSI); in the static switching pattern all forwardings are executed by the same relay that is selected at the beginning, while in the dynamic pattern relays that have not recovered the source message keep receiving from the forwarding relay to extend the decoding subset, from which the activated relay for next retransmission is reselected. Numerical simulations validate that statistical information is more beneficial than outdated CSI over weakly correlated channels. On the other hand, the dynamic switching pattern generally outperforms the static one especially with relatively large maximum transmission number, at the cost of higher complexity of decoding and signalling.

In cellular/WLAN interworking, WLAN is usually deployed to offload the heavy traffic in hot spots. To improve the offloading efficiency, we proposed a new WLAN-first scheme with service rate differentiation and analyzed its performance. The analytical results are validated by computer simulation. It is observed that, under the premise of QoS guarantee, the proposed scheme can significantly improve the throughput compared with the existing WLAN-first scheme. And the proposed scheme is easy to implement as the AP could decide the admission of a TIE based on its RSS (received signal strength) and the admission region, which could be computed off-line according to the analysis in this paper. The proposed scheme could be used for the future cellular/WLAN integration and may provide some insights for future extension.

Vertical handover decision (VHD) and radio resource allocation for the handover TIE (RRA) are two important issues in the integration of heterogeneous wireless networks, which are mainly considered separately for network utility problems, although both of them interact from the perspective of temporal dimension. The ergodic utility based joint-optimization algorithms should outperform that independent ones in the sense of optimal resource configuration, although that are generally more complex due to dealing with a more huge state space. In this paper, we proposed a distributed algorithm to immensely decrease the complexity of this joint-optimization algorithm with slightly performance decay, which validated by simulation, and the simulation results demonstrate that separately considering VHD or RRA for ergodic utility maximization has feeble improvement, while jointly consideration shows more improvement.

In this paper, we study energy-efficient channel aggregation problem in cognitive radio networks, where a secondary user senses multiple channels simultaneously or sequentially in the sensing period and subsequently aggregate those channels sensed free. We aim at designing the optimal sensing time and power allocation to achieve the maximum energy efficiency, meanwhile considering the maximum transmit power and minimum rate constraints as well as the protection to primary users. Taking into account the dynamics of the primary users' activities and the impact of imperfect spectrum sensing, we formulate the above design problem as a sum-of-ratios problem and solve it by applying the theory of nonlinear fractional programs. Numerical results validate the optimality of our energy-efficient design. Moreover, the impacts of the spectrum sensing approaches, the maximum number of sensed channels and the maximum power and minimum rate constraints on energy efficiency are also investigated respectively.

Though cooperative relaying is believed to be a promising technology to improve the energy efficiency of cellular networks, the relays' static power consumption might worsen the energy efficiency therefore can not be neglected. In this paper, we focus on whether and how the energy efficiency of cellular networks can be improved via relays. Based on the spatial Poisson point process, an analytical model is proposed to evaluate the energy efficiency of relay-assisted cellular networks. With the aid of the technical tools of stochastic geometry, we derive the distributions of signal-to-interference-plus-noise ratios (SINRs) and mean achievable rates of both non-cooperative users and cooperative users. The energy efficiency measured by “bps/Hz/W” is expressed subsequently. These established expressions are amenable to numerical evaluation and corroborated by simulation results.

To achieve highly efficient and flexible spectrum sharing in cognitive radio networks, we proposed a queuing method in combined channel aggregation and fragmentation (CAF) strategy for dynamic spectrum access (DSA). We derived the balance equation and evaluated various system performance metrics including blocking probability, dropping probability, spectrum utilization, throughput and mean queuing time by a continuous time Markov chain (CTMC) model. Numerical results show that this CAF with queuing strategy greatly lowers the dropping probability without obviously impacting the blocking probability, and slightly increases the spectrum utilization and throughput of the secondary network compared with the non-queuing case. Moreover, by tuning the bandwidth requirement of each secondary user, different system performance can be achieved.

Cooperative communication with network coding can yield higher system diversity gain and improve the efficiency of cooperation, for which designing the proper and efficient coding coefficients is one key technique for implementation. The important contributions of our work reveal the existence of coding coefficient matrix of order M constructed in GF(M = 2n) and propose practical and effective method to search such matrix. An improved genetic algorithm solution is proposed by introducing the local gradient search mechanism for large searching space and the search results show that the proposed method can obtain satisfying solutions and better computing performance for M = 8 in GF(8). The discussion on improved local gradient search mechanism illustrates its underlying potentials in global search framework. This work gains an insight into the coefficient matrix construction for cooperative communication with network coding in GF(2n).

Mobile terminals in heterogeneous wireless networks continuously undergo network selection within the initial access process and handover process. In order for a mobile terminal to be connected to a network in the best possible way in terms of QoS performance and energy consumption, this paper presents a novel method that takes into account user preferences, network conditions, QoS and energy consumption requirements in order to select the optimal network which achieves the best balance between performance and energy consumption. The proposed network selection method incorporates the use of fuzzy logic because of the available sources of information from different radio access technology (RAT) are qualitatively interpreted and heterogeneous in nature, and adopts different energy consumption metrics for real-time and non-real-time applications. Finally, simulations confirm our scheme's suitability and effectiveness.

Adaptive Relay-Assisted/ Direct Transmission (ARDT) proposed in [13] is a simple and efficient protocol which adaptively utilizes the direct link between two sources with only channel state information at receiver (CSIR), and it validly enhances the spectrum efficiency of time division broadcast (TDBC) system. In this paper, we combine ARDT with joint power allocation and relay selection for multi-relay scenario. Each relay optimize its own forwarding power to maximize the minimum end to end signal to noise ratio (SNR) towards two sources, and the optimal relay is then selected to cooperate. System outage probability, average throughput and average bit error rate (BER) are analyzed with theoretical upper and lower bounds. Numerical simulations validate the rationality of bounds, and show that whether maximal ratio combining (MRC) is adopted for receiving makes little difference. The proposed relay selection scheme is verified to outperform that based on original TDBC in reference work.

In this paper, performance of three-time slot (3TS) physical layer network coding (PNC) is investigated in interference-limited two-way relaying system. Effective lower and upper bounds are derived for important performance metrics like outage probability and average bit error rate (BER) with different modulation modes. Asymptotic behavior is also proposed to intuitively exhibit the trend of performance in high signal-to-interference and noise ratio (SINR) regime. We differentiate practical applications with different relay power for the bounds. A particular scenario with positional grid structure of sources, relay and co-channel interferers is established for numerical analysis, and the effects of some critical parameters, such as relay's location, forwarding capability and power allocation factor, are compared and discussed. The theoretic solutions are finally verified by simulation results.

In this paper, we study the communication between terrestrial source and destination terminals via a geosynchronous (GEO) multi-beam satellite in satellite mobile communication (SMC) system. The end-to-end transmission is modeled by a relaying process, in which inter-beam interference plays a key role due to frequency reuse among adjacent beam cells and side-lobe leaking of practical beam antenna. Through theoretic analysis we obtain the closed-form expressions of end-to-end outage probability and system achievable throughput as the major performance metrics. Both theoretic and simulation results, which coincide well with each other under diverse beam frequency planning, suggest that reuse factor of 1/3 is an appropriate compromise between system throughput and reliability in the GEO SMC system.

Though opportunistic relaying coded cooperation is known as an efficient cooperation scheme for cooperative multi-relay networks, the existing works have the disadvantage of low resource utilization or high system complexity. In this paper, we focus on how to achieve a balance between resource utilization and system complexity by proposing an novel opportunistic relaying coded cooperation with selective source-to-destination parity transmission over Nakagami-m fading channels without additional cost. We derive closed-form expressions of the outage probability and explicit upper bounds on bit error probability using the statistical characteristic of the signal-to-noise ratios (SNRs) and confirm that the proposed scheme outperforms both opportunistic amplify-and-forward (AF) and opportunistic decode-and-forward (DF). Simulation results on the unbalanced generalized Nakagami-m fading conditions show that both system complexity and resource utilization are ameliorated with a better performance compared to the previous works.

SUMMARY Selfish behavior of nodes in opportunistic networks impact greatly on the network performance. Therefore, an effective mechanism should be proposed to resist such misbehavior and to stimulate the nodes willing to relay packets for other nodes toward their destinations. On the basis of the evolutionary reputation model proposed, this paper investigates the network performance, including message delivery ratio, average delivery delay, and average intermittent hops, under the influence of reputation degree and selfish degree. In addition, the reputation evolution process is described, which helps to understand the interaction among network nodes. Simulation results show that the opportunistic network adopting the model proposed can resist partially the node selfishness and maintain acceptable network performance. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, lifetime of user equipments in hotspots is prolonged by combining relay technology and power control policy. As to both cable relay and wireless relay scenarios, effective power control policies are adopted to maximize energy efficiency under the premise of guaranteeing QoS. In addition, the closed-form results for both power control and lifetime gain are obtained, which match well with numerical results. This work extends our understanding about the effect of relay technology on UE's lifetime prolongation.

In this paper, we investigate the utility-based resource allocation problem in OFDMA relay networks with service differentiation, where Rate Constraint (RC) and Best Effort (BE) services are supported. Our objective of resource allocation is to maximize the sum utility of BE service users, while guaranteeing that the utility of each RC service user is equal to one. A joint optimization problem for relay selection, subcarrier assignment and power allocation is formulated. Since the problem cannot be solved directly, we make continuous relaxation and solve it by Lagrangian dual method. The optimal allocation strategy is obtained using Karush-Kuhn-Tucker (KKT) conditions. As it is difficult to converge to the optimal solution, thus we further present a heuristic resource allocation algorithm with low complexity. Simulation results show that our proposed algorithm achieves higher utility of BE users and lower outage probability of RC users, and also yields a good tradeoff between system throughput and user fairness.

The mechanism for the formation of small-world networks is important but still unsolved. We proposed a network evolution model based on local interaction among rational individuals with fixed network dimensions. This model extends Barabasi's preferential attachment mechanism to consider two more realistic factors when choosing opponent to interact. Prisoner's dilemma game are utilized to model such local interaction between individuals. The edges of the network are regulated by one simple rule proposed which strengthen the edges with good interaction while weaken those with bad ones. Numerical results show that small-world network structure could be evolved.