To achieve satisfactory performance in vehicle-to-everything (V2X) communications, it is paramount to accurately estimate the channel. The traditional data-pilot aided (DPA) scheme and the variation of DPA, e.g., spectral temporal averaging (STA), have been adopted for IEEE 802.11p due to their low complexity, but their performances are not satisfactory. The more recently proposed time domain reliable test frequency domain interpolation (TRFI) scheme only marginally improves the performance. Deep neural network (DNN)-based estimators, e.g., STA-DNN and TRFI-DNN, have substantially improved the channel estimation, and the long short-term memory (LSTM)-based estimators, such as LSTM-DPA-TA and LSTM-MLP-DPA, achieve the state-of-the-art performance. LSTM-based estimators, however, have high computational complexity. In this paper, we propose a novel channel estimator that leverages temporal convolutional networks (TCNs) combined with the DPA procedure to estimate and track channel variations. Simulations on realistic V2X scenarios show that the proposed TCN-DPA channel estimation scheme outperforms existing methods in almost all V2X scenarios. The proposed estimator has about one order of magnitude improvement in terms of bit error rate compared to LSTM-based estimators. By exploiting the parallelism inherent in the TCN architecture, the computational complexity of the proposed TCN-DPA estimator is 40% and 47% lower than LSTM-DPA-TA and LSTM-MLP-DPA, respectively. Moreover, the training time of TCN-DPA is only 52% and 42% of the time of LSTM-DPA-TA and LSTM-MLP-DPA, respectively.

In this paper, an energy-efficient design for massive multiple-input multiple-output (MIMO) systems is studied with the consideration of hardware impairments. The objective is to maximize the system energy efficiency of both uplink and downlink transmissions by jointly optimizing the number of antennas at the base station, the number of served users, and the transmit power. Firstly, by considering the linear distortion due to hardware impairments, the resultant channel estimation error from both distortion and noise is analyzed, following which closed-form approximations for the average achievable uplink/downlink rates are derived. Then, a massive MIMO energy efficiency optimization problem considering hardware impairments is formulated. By applying the techniques of relaxation and change of variables, an alternative optimization with build-in bisection search (AO-BS) algorithm is proposed with the quasi-concavity of the transformed objective function theoretically proved. The performance of the proposed AO-BS algorithm is validated through numerical simulations, which shows fast convergence to near-optimal solutions. Compared with existing approaches, the proposed scheme improves the system energy efficiency greatly when the hardware impairments are considered. Furthermore, the system design guideline in terms of the number of antennas, the number of served users, and the transmit power is provided.

Unmanned aerial vehicles (UAVs) have extensive civilian and military applications, but establishing a UAV network providing high data rate communications with low delay is a challenge. Millimeter wave (mmWave), with its high bandwidth nature, can be adopted in the UAV network to achieve high speed data transfer. However, it is difficult to establish and maintain the mmWave communication links due to the mobility of UAVs. In this paper, a beam management scheme utilizing angular domain information (ADI) is proposed to rapidly establish and reliably maintain the communication links for the mmWave UAV network. Firstly, Gaussian process machine learning (GPML)-enabled position prediction is proposed to facilitate coarse-ADI acquisition through the proposed UAV clustering algorithm. Then, with the proposed confined-ADI acquisition which removes the redundancy in the coarse-ADI acquisition, fast beam tracking with respectively the single-beam pattern and the multi-beam pattern is achieved. Finally, a data-driven beam pattern selection scheme is proposed for improving the spectrum efficiency. Simulation results verify the outstanding performance of the proposed beam management for mmWave UAV networks.

Consider a large-scale cellular network in which base stations (BSs) serve massive Internet of Things (IoT) devices. Since IoT devices are powered by a capacity-limited battery, how to prolong their working lifetime is a paramount problem for the success of cellular IoT systems. This article proposes how to use BSs to manage the active and dormant operating modes of the IoT devices via downlink signaling in an energy-efficient fashion and how the IoT devices perform energy-efficient uplink power control to improve their uplink coverage. We first investigate the fundamental statistical properties of an activation signaling process induced by BSs that would like to activate the devices in their cells, which helps to derive the neat expressions of the true, false, and total activation probabilities that reveal joint downlink power control and BS coordination is an effective means to significantly improve the activation performance. We then propose an energy-efficient uplink power control for IoT devices which is shown to save power and ameliorate the uplink coverage probability at the same time. We also propose an energy-efficient downlink power control and BS coordination scheme, which is shown to remarkably improve the activation and uplink coverage performances at the same time.

We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning (C2ML) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one- dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the C2ML- enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA- TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline C2ML-enabled multicast grouping and the online CA-TSP inspired UAV- trajectory optimization, is shown to achieve excellent energy-saving performance.

Fountain codes are promising for unmanned aerial vehicle (UAV) communications with intermittent transmission links caused by high UAV mobility. However, it is challenging to estimate the transmission delay of UAV communication systems with fountain codes due to the uncertainty of the coding rate and the dynamic channel quality. In this paper, we propose a delay estimation method based on a joint buffer-decoder queuing model for UAV communication systems with LT codes, and show that the complexity of the proposed delay estimation method can be reduced from O(n3) to O(n2). Simulation results validate the effectiveness of the proposed delay estimation method.

For rate optimization in interference limited networks, improper Gaussian signaling has shown its ability to outperform conventional proper Gaussian signaling. In this paper, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel. To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error algorithm is adopted, and in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. The simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared with the existing schemes with proper as well as improper Gaussian signaling under various network configurations.

This paper studies an energy beamforming and time duration joint optimization problem for maximizing the sum-rate of a battery-free-multi-antenna-relay-assisted wireless powered communication network (WPCN). The considered problem is non-convex due to the strongly coupled optimization variables. By introducing new optimization variables and reformulating the problem into a convex problem, the optimal solution is obtained based on convex optimization methods. Furthermore, an alternate iteration algorithm is proposed by decoupling the problem into two subproblems, and a semi-closed form solution of the optimal energy beamforming matrix is derived with given time allocation. Simulation results indicate that the proposed battery-free multi- antenna relay architecture can deliver a significant sum-rate gain for the WPCN. In addition, the proposed suboptimal algorithm only has 5% sum-rate loss compared to the optimal solution.

In this paper, the non-orthogonal multiple access (NOMA) technology is integrated into cognitive orthogonal frequency-division multiplexing (OFDM) systems, called cognitive OFDM-NOMA, to boost the system capacity. First, a capacity maximization problem is considered in half-duplex cognitive OFDM-NOMA systems with two accessible users on each subcarrier. Due to the intractability of the considered problem, we decompose it into three subproblems, i.e., the optimization of, respectively, sensing duration, user scheduling, and power allocation. By investigating and exploiting the characteristics of each subproblem, the optimal sensing duration adaptation, a matching-theory-based user scheduling, and the optimal power allocation are proposed correspondingly. An alternate iteration framework is further proposed to jointly optimize these three subproblems, with its convergence proved. Moreover, based on the non-cooperative game theory, a generalized power allocation algorithm is proposed and then used in the framework to accommodate half-duplex cognitive OFDM-NOMA systems with multiple users on each subcarrier. Finally, the proposed framework is extended to solve the capacity maximization problem in full-duplex cognitive OFDM-NOMA systems. Simulation results validate the superior performance of the proposed algorithms. For example, for the case of two accessible users, the proposed framework approaches the optimal solution with less than 1% capacity loss and 120 times lower complexity compared with exhaustive search.

For rate optimization in interference limited network, improper Gaussian signaling has shown its capability to outperform the conventional proper Gaussian signaling. In this work, we study a weighted sum rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel (MIMO-IBC). To solve this nonconvex and NP-hard problem, we propose an effective two-stage algorithm. In the first stage, a weighted mean square error (MSE) minimization algorithm is adopted to compute the transmit covariance matrices, and in the second stage, an alternating direction method of multipliers (ADMM)-based distributed multi-agent algorithm is proposed to obtain the globally optimal pseudo-covariance matrices in a parallel and scalable fashion. Finally, simulation results are presented to demonstrate the superior performance of our algorithm under different signal-to-noise ratio (SNR) and various network scenarios.

In time-slotted molecular communication systems, clock synchronization between transmitter and receiver is crucial. In this paper, training-based synchronization schemes in a molecular communication system with quantity-based modulation are proposed by utilizing the information of arrival times of molecules. For timing offset estimation, three estimators are proposed, including maximum likelihood estimation (MLE), unbiased linear estimation (ULE), and iterative ULE. For clock skew estimation, two algorithms, arithmetic mean estimation and weighted mean estimation, are proposed. Simulation results demonstrate that the proposed estimators are effective.

In this paper, we investigate a binary two-way diffusion channel from an information theoretical perspective. Molecule harvesting is considered by which molecules can be reused by the receiver for subsequent transmissions. An outer bound of the capacity region for the general case and inner bounds for specific cases are derived. With the proposed outer bound, we show that no signaling rate is achievable in the considered system if messenger molecules perform free diffusion in an unconfined space and the nodes are not replenished with extra molecules. The proposed lower bounds are tighter than the ones resulted from the traditional joint typicality decoding method.

In this paper, a distributed video coding (DVC) system with spatiotemporal side information is proposed. The proposed framework addresses the problem of poor compression performance of DVC for high-motion video sequences by integrating temporal and spatial prediction schemes in one framework. Super-resolution techniques are employed for spatially-predicted side information generation, and a support vector machine is trained to adaptively select the coding structure with both spatial and temporal prediction. In addition, an encoder-driven coding mode selection at different granularities, including frame, block and coefficient levels, is adopted to further improve the coding performance for various video conditions. Experimental results show that the average BD rate reduction of the proposed framework is 12.93% compared with the DISCOVER DVC, and the coding gain is significant, especially for high-motion sequences. Moreover, the average computing complexity is only 92.26% of the DISCOVER DVC.

In this paper, the problem of joint dynamic spectrum access and multi-relay selection is investigated in relayenabled cooperative communication systems to maximize the system sum-capacity. Since the considered problem is a mixed integer nonlinear program, which is generally intractable to find the optimal solution, two matching theory-based suboptimal algorithms are proposed to reduce the computational complexity for two different cases. For the case that each source node can only be assisted by one relay, a cyclic three-sided matching algorithm is firstly proposed to attain the stable matching results for the selection of the source node and the relay with the spectrum band used. Then, for the case that each source node can be assisted by more than one relay, a two-step matching algorithm is proposed to perform joint dynamic spectrum access and multi-relay selection. Simulation results show that the proposed algorithms, with much lower complexity compared to the optimal exhaustive search, can achieve the near-optimal performance with a gap to the optimum being less than 5%.

This paper studies the spectrum efficiency (SE) maximization problem for cooperative multi-antenna power beacon (PB)-enabled wireless powered communication networks (WPCN), where each transmitter harvests energy from surrounding PBs and then transmits data to the corresponding receiver within its allocated duration. The considered problem is formulated as jointly optimizing the energy beamforming vectors of PBs, the transmission duration, and the transmit power of users to maximize the total SE. In order to derive an efficient algorithm, the SE maximization problem is decomposed into two subproblems: the SE maximization problem for data transmission and the energy consumption minimization problem for energy transfer.We prove that the optimal SE is concave with the harvested energy of each user, and based on this concavity, an efficient algorithm is proposed to achieve the optimal solution. Finally, simulation results validate the optimality of the proposed algorithm, and verify the superiority of the proposed scheme-more than 150% performance gain is obtained, compared with the scheme of single PB with single antenna.

In this paper, an energy-incentive cooperative transmission (EICT) scheme is proposed for wireless ad hoc networks, where a node uses energy as reward to seek for cooperative transmission from neighboring nodes and then the cooperative node adopts a decode-and-forward (DF) protocol to relay data. An optimal time slot and power allocation algorithm is proposed to maximize the sum-rate under the constraints of peak power, energy consumption, and individual data rate when the channel state information (CSI) is perfectly known at transmitters. Furthermore, the scenario that only the statistical CSI is available at transmitters is investigated, and an alternative algorithm is presented to optimize time slot and power allocation. Simulation results confirm the superiority of the proposed scheme over existing ones, demonstrating a more effective mechanism to stimulate cooperation in wireless ad hoc networks.

Since mobile network operators not only need to satisfy user needs but also have to reduce operating cost (mainly electricity cost) to maintain long-term profitability under strict market competition, energy efficiency (EE), defined as capacity per energy cost, becomes a popular metric for designing mobile networks. For user-centric services, however, the metric of quality of experience (QoE) is more suitable than the metric of capacity or data rate. Therefore, in this paper, we consider a novel network design metric quality-energy efficiency (QEE), defined as the quality of experience achieved per energy consumed. By using QEE as the performance objective, we propose a framework to design user-centric green cloud-based radio access network (CRAN). In particular, the issues of fairness and fronthaul power consumption in C-RAN are taken into account. An efficient approach to solving the non-convex max-min optimization problem for the design of user-centric green C-RAN is proposed. Simulation results validate the near-optimal performance of the proposed algorithm and demonstrate the value of the proposed QEE metric for the design of energy-efficient user-centric CRAN.

In molecular communication systems, information is conveyed via nanoscale particles or molecules. Traditionally, the distribution of the first arrival time to the receiver is considered for system design and evaluation if nanoscale particles or molecules are diffused from the transmitter to the receiver in diffusion-based molecular communication systems. In this paper, we consider an extra information in the diffusion-based molecular communication system, namely the first arrival position at the receiver. A mathematical framework is developed to obtain the closed-form density function of the first arrival position for particles/molecules diffusing under constant net drift. The derived density function not only provides a novel analytical framework for existing molecular communication systems but may inspire novel molecular communication system design.

In practical implementations of single-antenna full-duplex (FD) radios, the imperfect isolation between the transmit (TX) and receive (RX) ports of the circulator causes the leakage of a strong self-interference (SI) component. This issue reduces both the spectral and energy efficiency of the device. State-of-the-art SI cancellation (SIC) solutions mitigate this problem, however they also constrain the maximum TX power of the FD radio. To address these issues and restrictions, we propose an energy recycling FD architecture that improves both the SIC capability and the spectral/energy efficiency of FD devices by means of a suitable radio frequency (RF) energy harvesting. The benefits brought by such solution are analyzed in an uplink (UL) scenario where two FD access points (APs), operating according to the proposed FD architecture, exchange signaling messages via a wireless link while communicating with two half-duplex (HD) mobile users (MUs). Our findings highlight non-negligible performance enhancements as compared to state-of-the-art approaches. Specifically, the proposed architecture is shown to yield a rate enhancement up to 29% for both UL transmission and signaling, and the recycling of up to 50% of the wasted energy at the circulator.

Networks formed by energy-harvesting devices impose new technological challenges on data transmission due to uncertainty of the amount of energy that can be harvested. This is even more challenging with multihop networks, in which transmission outage occurs when one single device along the relay path cannot harvest enough energy. A virtual multiple-input multiple-output (MIMO) model for multihop, multipath networks has recently been developed to facilitate reliable open-loop end-to-end transmissions, making networks robust to random transmission outages without the necessity of bandwidth-consuming and complicated end-to-end feedback and control. In this paper, a framework based on a virtual MIMO model of multihop, multipath opportunistic networks formed by energy-harvesting devices is developed. We propose rotated-algebraic path-time codes (RA-PTC), by which data are encoded using Givens rotation and cyclic division algebras. Without rate loss, a form of time diversity is exploited by repeatedly transmitting the RA-PTC-coded data. Extensive theoretical analyses are carried out using amount of fading and diversity as metrics. Both the performance enhancement due to the proposed RA-PTC and the performance loss due to energy shortage are quantified. Furthermore, a simple yet effective cyclic power control is proposed to improve transmission reliability. Numerical results demonstrate that RA-PTC and cyclic power control enable efficient and reliable end-to-end transmission in multipath, multihop opportunistic networks formed by energy-harvesting devices.

This paper presents a systematic approach for solving wireless max-min utility fairness optimization problems in multiuser wireless networks with general monotonic constraints. These problems are often challenging to solve due to their nonconvexity. By establishing a connection between this class of optimization problems and the class of conditional eigenvalue problems that can be addressed by a generalized nonlinear Perron-Frobenius theory, we show how these problems can be solved optimally using an iterative algorithm that converges geometrically fast. The mathematical development in this paper unifies previous work and allows us to handle a broader class of competitive utility functions with general nonlinear monotonic constraints. Several representative applications illustrate the effectiveness of the proposed framework, including the max-min quality-of-service subject to robust interference temperature constraints in cognitive radio networks, the min-max weighted mean-square error subject to signal-to-interference-and-noise ratio constraints in multiuser downlink systems, the max-min throughput subject to nonlinear power constraints in energyefficient wireless networks, the max-min sigmoid utility in multimedia wireless networks, and the min-max outage probability subject to outage constraints in heterogeneous wireless networks. Numerical results are presented to demonstrate the fast-convergence behavior of the algorithms to the optimal fixed-point solution characterized by our generalized nonlinear Perron-Frobenius theoretic framework.

This paper investigates an energy efficiency optimization problem in cognitive orthogonal frequency division multiplexing systems. The goal is to maximize the energy efficiency by adapting the sensing duration, detection threshold, and transmit power to the constraints of the energy consumption of the secondary network and the interference to the primary network in a statistical manner. First, the case of identical detection threshold for all subcarriers is considered. In order to circumvent the intractability of the resulting problem, an alternate iteration framework is proposed to iteratively solve the three decoupled subproblems: sensing duration optimization, detection threshold optimization, and power allocation optimization. By exploiting the characteristics of each subproblem, the proposed framework is proved to be convergent. Then, the case with individual detection threshold for each subcarrier is explored. By proving that the optimal detection threshold is the root of a quadratic equation with one unknown variable, the proposed framework can be applied with minor modification. Simulation results show that the proposed alternating optimization framework can approach rapidly to the optimal solution, with less than 1% gap. Compared with the existing schemes, both the cases with identical and individual detection thresholds can achieve a considerable energy efficiency gain, with the latter further outperforming the former.

There are four major components in application systems with internet-of-things (IoT): sensors, communications, computation and service, where large amount of data are acquired for ultra-big data analysis to discover the context information and knowledge behind signals. To support such large-scale data size and computation tasks, it is not feasible to employ centralized solutions on cloud servers. Thanks for the advances of silicon technology, the cost of computation become lower, and it is possible to distribute computation on every node in IoT. In this paper, we take video sensing network as an example to show the idea of distributed computing in IoT. Existing related works are reviewed and the architecture of a system-on-a-chip solution for distributed smart cameras is proposed with coarse-grained reconfigurable image stream processing architecture. It can accelerate various computer vision algorithms for distributed smart cameras in IoT.

In conventional LTE networks, user equipments (UEs) access the network via a four-step random access procedure. When small amount of data is transmitted, such as in the scenarios of machine-type communications (MTC), signaling overhead resulted from the random access procedure becomes a critical issue. To enable an efficient small data transmission, a direct access scheme utilizing code division multiplexing (CDM) is proposed in this paper. Instead of transmitting on the resource blocks indicated by eNB, UEs randomly select resource blocks for direct data transmission without signaling eNB, with CDM used to resolve the collision problem. Under the proposed architecture, however, the data transmission is inherently non-orthogonal and the eNB has to detect the data without any information from UEs, making the receiver too complex to implement. To ease the burden of the receiver, we design a pseudo-random resource selection scheme such that the search space of the data detection is reduced. The analysis and simulation results show the advantage of using the proposed direct access scheme to improve the efficiency of small data transmission in cellular networks.

Since uplink (UL) and downlink (DL) traffic loads are time-variant in femtocells, it is essential to adopt dynamic time-division duplexing (TDD) to effectively adjust the uplink and downlink transmission resources. However, the cross-link interference between dynamic TDD femtocells decreases the throughput gain of dynamic TDD. In this paper, we propose an evolutionary game-based distributed approach to choose the UL-DL configuration in order to minimize interference and maximize the system throughput in a large-scale femtocell network. A multiple populations evolutionary game is formulated to model femtocells with different traffic loads. We prove that the evolutionarily stable strategy (ESS) of the considered multiple populations evolutionary game is the optimal configuration which maximizes the system throughput. Simulation results confirm the effectiveness of the proposed evolutionary game-based approach for system throughput optimization in femtocells employing dynamic TDD.

We propose an efficient iterative multiple-input multiple-output (MIMO) detection algorithm based on the local search. Specifically, since the MIMO channel matrix twists the lattice structure of the received symbols, the proposed channel-aware local search (CA-LS) defines its search neighborhood according to the instantaneous channel realization. Such channel-dependent neighborhood can be efficiently identified by using the sphere decoder in a set that comprises the differences between pairs of QAM vectors, which is termed as delta vectors. The delta vectors with small quadratic norms with respect to the channel matrix are identified and then used as the search directions of the CA-LS. Features like sparsity and non-uniformity of delta vectors are exploited to reduce the SD complexity. Furthermore, by reformulating the detection criterion, the log-likelihood ratio (LLR) computations and searches of the CA-LS are greatly simplified. Numerical simulations demonstrate that compared with other practical iterative MIMO detectors, e.g., the list sphere decoder, the CA-LS achieves superior performance in both error rate and complexity aspects.

Wireless communication with nodes capable of harvesting energy emerges as a new technology challenge. In this paper, we investigate the problem of utilizing energy cooperation among energy-harvesting transmitters to maximize the data rate performance. We consider a general framework which can be applied to either cellular networks with base station energy cooperation through wired power grid or sensor networks with transmitting node energy cooperation through wireless power transfer. We model this energy cooperation problem as an infinite horizon Markov decision process (MDP), which can be optimally solved by the value iteration algorithm. Since the optimal value iteration algorithm has high complexity and requires non-causal information, we propose a distributed algorithm by using reinforcement learning and splitting the MDP into several small MDPs, each associated with a transmitter. Simulation results demonstrate the effectiveness of the proposed distributed energy cooperation algorithm.

In this paper, we investigate the joint design of transmit beamforming and power control to maximize the weighted sum rate in the multiple-input single-output (MISO) cognitive radio network constrained by arbitrary power budgets and interference temperatures. The nonnegativity of the physical quantities, e.g., channel parameters, powers, and rates, is exploited to enable key tools in nonnegative matrix theory, such as the (linear and nonlinear) Perron-Frobenius theory, quasi-invertibility, and Friedland-Karlin inequalities, to tackle this nonconvex problem. Under certain (quasi-invertibility) sufficient conditions, we propose a tight convex relaxation technique that relaxes multiple constraints to bound the global optimal value in a systematic way. Then, a single-input multiple-output (SIMO)-MISO duality is established through a virtual dual SIMO network and Lagrange duality. This SIMO-MISO duality proved to have the zero duality gap that connects the optimality conditions of the primal MISO network and the virtual dual SIMO network. Moreover, by exploiting the SIMO-MISO duality, an algorithm is developed to optimally solve the sum rate maximization problem. Numerical examples demonstrate the computational efficiency of our algorithm, when the number of transmit antennas is large.

In this work, a novel energy-recycling single-antenna full-duplex (FD) radio is designed, in which a new three-port element including a power divider and an energy harvester is added between the circulator and the receiver (RX) chain. The presence of this new element brings advantages over the state of the art in terms of both spectral efficiency and energy consumption. In particular, it provides the means of performing both an arbitrary attenuation of the incoming signal, which in turn increases the effectiveness of the state-of-the-art self-interference cancellation strategies subsequently adopted in the RX chain, and the recycling of a non-negligible portion of the energy leaked through the nonideal circulator. The performance of this architecture is analyzed in a practically relevant four-node scenario in which two nodes operate in FD and two nodes in half-duplex (HD). Analytical approximations are derived for both the achievable rates of the transmissions performed by the FD and HD radios and the energy recycled by the FD radios. The accuracy of these derivations is confirmed by numerical simulations. Quantitatively, achievable rate gains up to 40% over the state-of-the-art alternatives, in the considered scenario, are highlighted. Furthermore, up to 50% of the leaked energy at the circulator, i.e., 5% of the energy of the transmitted signal, can be recycled.

Cognitive radios (CRs) improve the spectrum efficiency in wireless communications. Nonetheless, owing to the intrinsic randomness of ad hoc cognitive radio networks (CRNs), e.g., the opportunistic links, the traditional realization of ad hoc networking that demands the end-to-end control information, is unscalable and impractical. A virtual multiple-input multiple-output (MIMO) framework has been recently developed for the realization of error-resilient end-to-end transmission without the necessity of feedback information. In this paper, we propose an end-to-end path-permutation coded (PPC) transmission in which one relay path is accessed at a time and the transmission is hopped among multiple relay paths. The hopping order is specified by a permutation array that encodes the data, meaning that the data is conveyed by the order of indices of the accessed paths. With the PPC scheme, the control overhead and data processing complexity of the end-to-end transmission become relatively low. The PPC technique can also be utilized as a multiuser technique. At the destination node, a joint sphere decoder efficiently implements the maximum a posteriori (MAP) probability decoding that simultaneously identifies the order of accessed paths and erasures. Comprehensive theoretical analyses and simulations are conducted to demonstrate the superior performance of the PPC technique in ad hoc CRNs.

The inter-cell interference (ICI) may considerably degrade the system performance of heterogeneous networks (HetNets) and is generally tackled by the transmitters, which often leads to lower spectrum efficiency. To avoid such disadvantage, this work presents a new receiver function that identifies the precoders assigned to active users in interfering cells, and thus enables more effective ICI mitigation. We theoretically derive the design criterion and analyze the performance of the receiver equipped with the proposed active precoder identification (API) scheme. We also propose two low-complexity techniques: the recursive structure based on the Sherman-Morrison formula/matrix determinant lemma, and the mode-switching technique adapting the search space on-the-fly. Numerical simulations demonstrate that such receiver can achieve error rate performance close to a receiver with perfect knowledge of the active precoders. The recursive structure achieves 68% complexity saving, while the mode-switching technique further increases the saving to 83%.

In this work, we consider a multiple-input single-output system in which an access point (AP) performs a simultaneous wireless information and power transfer (SWIPT) to serve a user terminal (UT) that is not equipped with external power supply. To assess the efficacy of the SWIPT, we target a practically relevant scenario characterized by imperfect channel state information (CSI) at the transmitter, the presence of penalties associated to the CSI acquisition procedures, and non-zero power consumption for the operations performed by the UT, such as CSI estimation, uplink signaling and data decoding. We analyze three different cases for the CSI knowledge at the AP: no CSI, and imperfect CSI in case of time-division duplexing and frequency-division duplexing communications. Closed-form representations of the ergodic downlink rate and both the energy shortage and data outage probability are derived for the three cases. Additionally, analytic expressions for the ergodically optimal duration of power transfer and channel estimation/feedback phases are provided. Our numerical findings verify the correctness of our derivations, and also show the importance and benefits of CSI knowledge at the AP in SWIPT systems, albeit imperfect and acquired at the expense of the time available for the information transfer.

Cognitive radio ad hoc network (CRAHN) is considered as a key technology to enhance the spectrum efficiency for diverse applications. However, due to the opportunistic links, the intrinsic randomness of the CRAHN makes the traditional precise control of the end-to-end transmission unscalable and generally infeasible. The recently-proposed virtual multiple-input multiple-output (MIMO) framework exploits the multipath routing to create the diversity at the network layer. With only local information and no feedback control channel, the path-time code (PTC) of the virtual MIMO system is able to efficiently provide the error resilient end-to-end transmission. In particular, while the transmission latency should be minimized, more attempts for accessing the opportunistic links can be made if a larger latency is allowed, which improves the error rate performance of the end-to-end transmission. By theoretically analyzing the error rate performance and erasure statistics, we propose a design guideline to determine the waiting period as the limit of the transmission latency. This designed waiting period not only preserves the diversity gained by the PTC but also provides a low transmission latency, resulting in a good balance of the reliability-latency trade-off of the end-to-end PTC transmission in CRAHNs.

Techniques of video summarization have attracted significant research interests in the past decade due to the rapid progress in video recording, computation, and communication technologies. However, most of the existing methods analyze the video in an off-line manner, which greatly reduces the flexibility of the system. On-line summarization, which can progressively process video during video recording, is then proposed for a wide range of applications. In this paper, an on-line summarization method using Gaussian mixture model is proposed. As shown in the experiments, the proposed method outperforms other on-line methods in both summarization quality and computational efficiency. It can generate summarization with a shorter latency and much lower computation resource requirements.

Mobile devices or sensor nodes are usually energy-constrained, and in some scenarios it is inconvenient or impractical to charge or replace batteries. To tackle this problem, the idea of transferring power to mobile devices wirelessly via radio frequency signals has recently gained much attention. In this paper, we consider a point-to-point communication system in which the transferred power is used for the receiver to feedback the quantized channel state information (CSI) to the transmitter, and then the transmitter utilizes the CSI to beamform the signals. Two power and information transfer architectures are considered: time-switching and power splitting. We derive bounds of the average achievable rates under the fading channel.

This paper presents a unifying and systematic framework to solve wireless max-min utility fairness optimization problems in multiuser wireless networks with generalized monotonic constraints. These problems are often challenging to solve due to their nonlinearity and non-convexity. Our framework leverages a general result in nonlinear Perron-Frobenius theory to characterize the global optimal solution of these problems analytically, and to design scalable and fast-convergent algorithms for the computation of the optimal solution. This work advances the state-of-the-art in handling wireless utility optimization problems with nonlinear monotonic constraints, which existing methodologies cannot handle, and also unifies previous works in this area. Several representative applications are considered to illustrate the effectiveness of the proposed framework, including max-min quality of service subject to robust interference temperature constraints in cognitive radio networks, min-max outage subject to outage constraints in heterogeneous networks, and min-max weighted MSE subject to SINR constraints in multiuser downlink system.

Distributed video coding (DVC) is a potential solution for distributed video sensors in wireless visual sensor and machine-to-machine (M2M) networks. However, the error resilience schemes have not been fully investigated in literatures. In this paper, we propose several error resilience schemes for key frame transmission of DVC, including resending, refining, and hybrid modes. The resending mode asks the transmitters to resend the lost packets as automatic repeat-request, the refining mode conceals the corrupted key frames by temporal error concealment techniques with considering the characteristics of DVC, and the hybrid mode can adaptively select the better mode packet-by-packet with the proposed rate-distortion optimized mode decision. Experimental results show that the proposed schemes can achieve around 6-dB gain in PSNR compared with a baseline approach with intra error concealment. Furthermore, the hybrid mode can achieve 0.5-dB gain in PSNR for some sequences compared with the better one among the resending and refining modes.

In this paper, we study the performance of the recently proposed class of path-time codes (PTCs), which exploit time and path diversity to increase the reliability of end-to-end transmission in ad hoc multihop networks. Using the central limit theorem, we prove that PTCs reshape the statistics of a relay path including multiple cascaded links. In particular, as the number K of relay paths becomes large, the cascaded channel statistic is transformed from K-product Rayleigh to Rayleigh. This is numerically demonstrated and analytically verified by using the concept of “amount of fading” (AF). Our results show that, by using PTCs, the end-to-end error-rate performance can be boosted in addition to the diversity and coding gains.

Video sensors are widely used in many applications such as security monitoring and home care. However, the growth of the number of sensors makes it impractical to stream all videos back to a central server for further processing, due to communication bandwidth and server storage constraints. Multi-view video summarization allows us to discard redundant data in the video streams taken by a group of sensors. All prior multi-view summarization methods, however, process video data in an off-line and centralized manner, which means that all videos are still required to be streamed back to the server before conducting the summarization. This paper proposes an on-line, distributed multi-view summarization system, which integrates the ideas of Maximal Marginal Relevance (MMR) and MS-Wave, a bandwidth-efficient distributed algorithm for finding k-nearest-neighbors and k-farthest-neighbors. Empirical studies show that our proposed system can discard redundant videos and keep important keyframes as effectively as centralized approaches, while transmitting only 1/6 to 1/3 as much data.

Dynamically choosing the time-division duplex (TDD) configuration in Long-Term Evolution (LTE) results in intercell interference. Among various interference types, the downlink-to-uplink interference is the strongest due to the much larger downlink (DL) transmission power from a base station than the uplink (UL) transmission power from a user equipment (UE). Although coordinating the neighboring base stations to operate in the same UL/DL configuration is a way to mitigate the interference, the flexibility of TDD is sacrificed. In this paper, we discuss an interference cancellation method exploiting the cloud radio access network (C-RAN) architecture. The downlink- to-uplink interference is cancelled by utilizing the knowledge of the downlink signal available at the cloud server. Simulation results show the effectiveness of this method, which maintains the flexibility of dynamically choosing the UL/DL configuration according to the traffic demand in the TDD system.

Femtocell base stations (FBSs) have been widely considered in the next generation cellular systems to enhance spectrum efficiency, but macro-femto and femto-femto interferences are still challenging issues to solve. In this paper, we consider the backhaul interface of FBSs to be home broadband connection (such as ADSL and optical fiber) with capacity lower than the air interface, and propose a novel resource optimization for FBSs to mitigate both the macro-femto and femto-femto interferences. First, the user rate requirements are normalized according to the backhaul capacity. Then, the total spectrum utilized by FBSs is minimized to alleviate the macro-femto interference. To reduce the femto-femto interference, we integrate the Gibbs sampler into the resource optimization to make each FBS select the frequency bands which have lower probability to introduce interference. Via sophisticated simulations based on the 3GPP femtocell evaluation scenario, the resource optimization is demonstrated to be effective and ready for LTE-A.

When a disaster happens, the communication network is very likely to be damaged. Since communication is critical for coordinating the operations of disaster recovery, restoring the communication network is always the first priority. In many situations, however, the breakdown of the communication network results from the cutoff of the power network, and it is difficult to recover the power network in a short time. In this paper, we propose a wireless information and power transfer scheme such that the communication network can be recovered without firstly restoring the power network. Challenges of the proposed scheme are discussed.

Ad hoc cognitive radio networks (CRNs) have the potential for meeting the challenge of increasing radio spectrum efficiency. However, traditional control of ad hoc networking demands end-to-end information channel feedback, whose feasibility is hard in ad hoc CRNs due to the opportunistic nature of spectrum access. In this paper, we propose a virtual multiple-input multiple-output (MIMO) approach to facilitate error-resilient end-to-end transmission with no need for feedback information. An erasure-channel model is used to describe the randomness of outage caused by opportunistic links. At source node, a discrete Fourier transform (DFT)-based path-time code (PTC) is used to exploit path diversity. The a priori erasure probability is analyzed, and a pipeline scheduling scheme with unequal waiting periods is designed to reduce such probability. At destination node, knowledge of the a priori erasure probability is exploited to overcome the decoding challenge raised by the presence of random erasures. A joint sphere decoder (JSD) with a minimum mean-squared error sorted QR decomposition (MMSE-SQRD) effectively implements maximum a posteriori (MAP) probability decoding. This decoder simultaneously performs erasure identification and data decoding. Numerical results show that the proposed virtual MIMO framework can pave the way to efficient and reliable end-to-end transmission in ad hoc CRNs.

Dramatic progress in nanotechnology has enabled nano-machines to perform basic functions of communication. To interconnect nano-machines, molecular communication is one of the most promising communication mechanisms, in which molecules are used to deliver information. However, compared to classical communication schemes, the reliability of molecular communication is often criticized due to the slow diffusion process and the inter-symbol interference (ISI). In this paper, we propose a low complexity receiver design for diffusion-based molecular communication to solve the ISI problem. We first derive the optimal receiver and then use the Poisson distribution to approximate the Poisson binomial distribution in order to obtain a receiver with lower complexity. The simulation results confirm the near-optimal performance of the proposed receiver design.

The advance in nanotechnology has enabled the fabrication of nanomachines for health applications. Recently, molecular communication has become a promising communication paradigm that allows nanomachines to exchange information by using messenger molecules in fluid environments. To enable molecular communications, the knowledge of distance between nanomachines is critical since the distance affects both the performance and the efficiency of molecular communication. However, works on molecular communication either assume the distance is known or the distance estimation is based on the assumption of clock synchronization between nanomachines. In this paper, we propose novel methods for distance estimation using only one-way transmission and requiring no clock synchronization between nanomachines. The noise of diffusion channel due to random walk of molecules is investigated and methods to effectively improve the estimation accuracy are proposed.

Mobile devices are usually energy-constrained. In order to avoid exhausting energy and lose the network connection, the idea of transferring power to mobile devices wirelessly via radio signals has recently gather much attention. In this paper, we consider the scenario in a point-to-point communication system that the transferred power is used for the receiver to feedback the quantized channel state information (CSI) to the transmitter. The transmitter then utilizes the CSI to beamform the signals. Although feedbacking more bits yields better CSI, it requires more power and a larger portion of time spent on the wireless power transfer, affecting the overall achievable rate. We derive bounds of the average achievable rate of the forward channel and investigate its relation to the average transmission power for wireless power transfer and the quantization bit number.

Distributed video coding (DVC) is an emerging video coding paradigm due to its flexibility to introduce much lower encoding complexity than conventional predictive codecs, which is beneficial for some applications such as wireless video surveillance, wireless sensor networks, and disposable video cameras. Although DVC systems without feedback channel address a wider range of applications, it is not commonly discussed in literatures due to its lower coding performance. In this paper, on the basis of PRISM DVC architecture, a low-complexity feedback-channel-free DVC system is proposed with a new classifier to improve the coding performance. Experimental results show that the proposed system can provide about 0.8 dB gain in PSNR to the state-of-the-art and 2 dB gain to IST-PRISM. It is also competitive regarding other feedback-channel-free DVC systems and can even achieve similar performance to DVC systems with feedback channel for low-motion sequences.

The performance of heterogeneous networks (Het-Nets) is strongly affected by inter-cell interference. Instead of relying on coordination among base stations, this study presents a new receiver block that identifies the precoders assigned to active users in an adjacent cell. Equipped with this proposed active precoder identification (API), the inter-cell interference at the receiver becomes feasible. The low complexity API is based on the maximum likelihood (ML) criterion and Sherman-Morrison formula. Moreover, by leveraging the fact that the list of active precoders often changes one precoder at a time, an efficient mode-switching API algorithm is proposed for further complexity reduction. Demonstrated by numerical results, the proposed API enables the receiver to achieve error rate performance close to that of the receiver with perfect knowledge of the active precoders used for transmission in the adjacent cell.

Extension from the single-measurement vector (SMV) problem to the multiple-measurement vectors (MMV) problem is critical for compressed sensing (CS) in many applications. By increasing the number of measurement vectors, a k-jointly-sparse signal can be recovered with less stringent requirements on the signal sparsity. Simultaneous orthogonal matching pursuit (SOMP), an MMV extension of the orthogonal matching pursuit (OMP) algorithm, is a widely used algorithm for the MMV problem. We noticed that for the SMV problems, the subspace pursuit (SP) algorithm outperforms OMP, so it was expected that the extension of SP to its MMV version, called simultaneous subspace pursuit (SSP) here, will easily outperform SOMP. However, we found that this direct approach does not allow the signal recovery rate to scale with the increase in the number of measurement vectors. To circumvent this, in this paper we propose the generalized subspace pursuit (GSP) algorithm, in which the number of columns to be selected in each of subspace pursuit iteration is properly chosen. Extensive simulation results confirm that the proposed GSP algorithm outperforms SOMP and SSP under various sampling matrix settings with noiseless and noisy measurements. In addition, we show the restricted isometry property (RIP)-guarantee that leads to the convergence of the proposed GSP algorithm and the uniqueness of the recovered signal.

In two-tier macro-femto cellular networks, cross-tier interference is a long-standing problem and may greatly degrade the performance of networks. Since most femtocell base stations (FBSs) can be accessed only by a certain closed subscriber group (CSG), mobile users may suffer from heavy interference and poor signal reception. Hybrid access control is one of methods to improve the communication quality by allowing mobile users to access nearby FBSs when these FBSs have surplus resource. In this paper, we propose a new hybrid access policy that mobile users may access nearby FBSs when they are outside the coverage regions of macrocell base stations (MBSs) and suffer from low signal power. We then derive the coverage probability by assuming that the locations of the macrocell base stations and the femtocell base stations follow independent Poisson point processes. The proposed hybrid policy can be viewed as a generalization of both the open and the closed access policies, hence the analytical results can also be applied to either the open access policy or the closed access policy.

Molecular communications emerges as a promising scheme for communications between nanoscale devices. In diffusion-based molecular communications, molecules as information symbols diffusing in the fluid environments suffer from molecule crossovers, i.e., the arriving order of molecules is different from their transmission order, leading to intersymbol interference (ISI). In this paper, we introduce a new family of channel codes, called ISI-free codes, which improve the communication reliability while keeping the decoding complexity fairly low in the diffusion environment modeled by the Brownian motion. We propose general encoding/decoding schemes for the ISI-free codes, working upon the modulation schemes of transmitting a fixed number of identical molecules at a time. In addition, the bit error rate (BER) approximation function of the ISI-free codes is derived mathematically as an analytical tool to decide key factors in the BER performance. Compared with the uncoded systems, the proposed ISI-free codes offer good performance with reasonably low complexity for diffusion-based molecular communication systems.

In physical-layer security, secret bits are extracted from wireless channels. With the assumption of channel reciprocity, the legitimate users share the same channel which is independent of the channels between the legitimate users and the eavesdropper, leading to secure transmissions. However, practical implementation of the physical layer security faces many challenges. First, for the correlated channel such as the multiple-input and multiple-output (MIMO) channel, the security is decreased due to the correlation between the generated secret bits. Second, the nearby eavesdropper posts a security threat due to observing the same channel as the legitimate user's. Third, the eavesdroppers might try to reconstruct the wireless environments. In this paper, we propose two practical physical layer security schemes for the MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems: the precoding matrix index (PMI)-based secret key generation with rotation matrix (MOPRO) and the channel quantization-based (MOCHA) scheme. The former utilizes PMI and rotated reference signals to prevent the eavesdroppers from learning the secret key information and the latter applies channel quantization in order to extract more secret key bits. It is shown that not only the secure communication but also the MIMO gain can be guaranteed by using the proposed schemes.

Low-power wireless video sensor nodes play important roles for applications in machine-to-machine (M2M) network. Several design issues to optimize the power consumption of a video sensor node are addressed in this paper. For the video coding engine selection, the comparison between conventional video coding system and distributed video coding (DVC) system shows that although the rate-distortion performance of existing DVC codec still has room to improve, it can provide lower power consumption with a noisy transmission channel. Furthermore, it also demonstrated that video analysis unit can help to filter out video contents without event-of-interest to reduce transmission power. Finally, several future research directions are addressed, and the trade-off between the video analysis unit, video coding unit, and data transmission should be further studied to design wireless video sensors with optimized power consumption.

The emerging diffusion-based molecular communications is a promising scheme for nano-machine communications. Nevertheless, the Brownian motion model, which describes the behavior of molecules, makes the physical channel different from the channel in conventional wireless communications. In particular, the crossovers in time caused by the late arrivals of the molecules severely affect the communication reliability. In traditional communications, channel coding has long been used to enhance the reliability. Through our explorations, it is shown that the commonly used Hamming distance is no longer a good metric for the channel decoding in diffusion-based molecular communications. The conventional concepts of the channel code design cannot be straightforwardly applied. In this paper, the molecular coding (MoCo) distance function has been proposed, and shown to approach the optimum performance beyond the capability of using Hamming distance. This suggests that new paradigms can be developed upon the MoCo distance.

Diffusion-based molecular communications has becoming a promising approach for end-to-end communication between nano-machines. However, due to the randomness nature of molecule diffusions, the arriving time of each molecule is hard to predict and the late coming molecules will become a source of interference when doing detection. This effect will cause a severe degeneration of the system performance. Through our explorations, the conventional methods for mitigating the interference cannot be directly applied in these diffusion-based molecular communication environments. One of the main reasons is that the channel response is now time-variant. In this paper, we first apply Bayesian criterion to design an end-to-end communication system using quantity-based modulation (i.e. the information is imbedded in the number of molecules transmitted). Inspired by the idea of decision feedback in traditional communications, we propose an inter-symbol interference (ISI) cancellation approach. We also demonstrate the cancellation process based on our proposed quantity-based modulation system. Numerical results show that the proposed ISI cancellation method can help us achieve a reliable end-to-end transmission between nano-scale devices.

Molecular communications emerges as a promising scheme for communication between nanoscale devices. In diffusion-based molecular communications, molecules as information symbols are released by transmitters and diffuse in the fluid or air environments to transmit messages. Under the diffusion channel modeled by Brownian motion, information sequences suffer from molecule crossovers, i.e., molecules released at an earlier time may arrive later, causing intersymbol interference (ISI). In this paper, we investigate practical channel codes for combating ISI. An ISI-free coding scheme is proposed to increase the communication reliability while keeping the encoding/decoding complexity reasonably low. We exemplify an ISI-free code and theoretically approximate its bit error rate (BER) performance. In addition, repetition codes are revisited out of the complexity concern and proved to be desirable. The BER approximations of the repetition code family are given as well. Compared with convolutional codes, the proposed ISI-free code and repetition codes offer comparable performance with much lower complexity for diffusion-based molecular communication systems.

Distributed video coding (DVC), a new video coding paradigm based on Slepian-Wolf and Wyner-Ziv theories, is a promising solution for implementing low-power and low-cost distributed wireless video sensors since most of the computation load is moved from the encoder to the decoder. It has been showed that there is still room to improve the coding efficiency of the current DVC codec. In this paper, we propose a hybrid coding structure with frame-level coding mode selection (CMS) to allow the DVC encoder to flexibly choose channel coding or entropy coding to code each band. The experimental results show the significant improvement on the rate-distortion (R-D) performance with only slight increase in the encoding complexity compared to the DISCOVER codec. The proposed DVC system performs comparably to H.264 No Motion with much lower encoding complexity.

As more and more wireless communication scenarios, such as Internet of Things and 4G wireless networks, rely on the deployment of wireline networks, the analysis of the heterogeneous wired-wireless networks becomes critical. In this paper, the probability mass function (PMF) of the number of hops in heterogeneous wired and wireless networks is analyzed. The nodes are assumed to be spatially distributed following Poisson point process (PPP). The wireless links are formed by considering the spatial distances, and the wired links are randomly deployed. To derive the PMF of the number of hops, we first approximate the number of hops between any two nodes in the wireless-link-only network as a function of the Euclidean distance. Then one wired link is added and the PMF of the number of hops is derived. Finally, the analysis is generalized to the heterogeneous network with multiple wired links. Simulation results confirm the accuracy of our proposed approach.

In recent years, a number of rate-distortion (R-D) optimization schemes have been proposed. These works discussed the joint source-channel rate allocation problem for achieving best end-to-end video transmission quality under limited transmission bit rate. However, encoding complexity, a critical concern for video transmission from mobile devices, was hardly considered. Especially, the time-varying nature of computational resources for mobile devices was largely ignored. In this paper, an on-line adaptive video encoder parameters control scheme is proposed. Simulation results show that the proposed scheme, by dynamically adjusting the encoder parameters in H.264 using the precomputed rate-complexity-distortion (R-C-D) tables, achieves low end-to-end distortion under time-varying channel condition and dynamic computational complexity constraints.

Consider a cognitive radio network with two types of users: primary users (PUs) and cognitive users (CUs), whose locations follow two independent Poisson point processes. The cognitive users follow the policy that a cognitive transmitter is active only when it is outside the primary user exclusion regions. We found that under this setup the active cognitive users form a point process called the Poisson hole process. Due to the interaction between the primary users and the cognitive users through exclusion regions, an exact calculation of the interference and the outage probability seems unfeasible. Instead, two different approaches are taken to tackle this problem. First, bounds for the interference (in the form of Laplace transforms) and the outage probability are derived, and second, it is shown how to use a Poisson cluster process to model the interference in this kind of network. Furthermore, the bipolar network model with different exclusion region settings is analyzed.

Most channel access schemes for cognitive radio only consider using the idle periods of the primary users. Such schemes are not using the spectrum efficiently, since transmission opportunities also arise when a primary transmitter is active but its corresponding primary receiver is far away from the cognitive user. By detecting the signal power of the acknowledgments, a cognitive user is able to estimate the distance and channel condition between the primary receiver and itself. Then if the primary receiver is far away, the cognitive user can transmit simultaneously with the primary transmitter without affecting the primary link. Based on this idea, the spatio-temporal channel access scheme, utilizing both idle periods and spatial reuse, can be applied. This paper provides fundamental analysis of the channel access delays and shows the advantage of using the spatio-temporal access scheme in the carrier sense multiple access (CSMA)-based network with bi-directional links.

We propose a novel method, called segmentation by temporal detection integration (STDI), to improve the segmentation results of background subtraction. The STDI applies split and merge algorithms forwardly and backwardly to obtain appropriate region segmentations based on the integration of the temporal detections across frames. The proposed scheme can be applied to human detection and tracking to avoid the situations that a group of people being detected as one single segment or a human body being separated into fragments, thus making the subsequent human tracking more reliable. To verify the performance of the proposed STDI, a dynamic layer model is used to depict motion, appearance, and shape of detections for human tracking. Experimental results show that the proposed STDI significantly improves the background subtraction segmentation results, and therefore, the accuracy of human tracking.

Networks in the future will be composed of a large number of devices with heterogeneous resources, various kinds of communication schemes, and different mobility capabilities. In order to achieve maximal efficiency under various constraints, it is critical to determine how devices should connect to each other, through wired or wireless links. The impact and the design of mixed wireline and wireless networks thus require thorough investigation. In this paper, link load, efficiency, and energy consumption of this kind of heterogeneous networks are studied through the simulation of network topologies using different spatial node distribution models, including uniform distribution, Poisson point process, and Matern cluster process. How the spatial node distribution affects the performance of the mixed wireless and wireline networks is discussed.

Low-power and low-cost distributed wireless video sensors play important roles for applications in machine-to-machine (M2M) and wireless sensor networks. Distributed video coding (DVC), an emerging coding technology based on Wyner-Ziv theory, seems to be a possible solution for implementing low-power video sensors since most of the computational complexity is moved from the encoder to the decoder. In this paper, existing works on DVC are discussed with rate-distortion and power consumption analyses compared with H.264/AVC-based approaches. We show that, since more transmission power is required for compensating the lower rate-distortion performance, the power consumption of sensor nodes using DVC is just similar to that of using H.264/AVC with zero motion vectors. Therefore, there is still a room for improvement to make DVC applicable for distributed wireless video sensors. Based on our analysis results, several possible research directions, such as studies on the trade- off between hardware cost and system power consumption, are also addressed in this paper under a unified DVC framework.

Superposition coding is a well-known capacity-achieving coding scheme for stochastically degraded broadcast channels. Although well-studied in theory, it is important to understand issues that arise when implementing this scheme in a practical setting. In this paper, we present a software-radio based design of a superposition coding system on the GNU Radio platform with the Universal Software Radio Peripheral acting as the transceiver frontend. We also study the packet error performance and discuss some issues that arise in its implementation.

We consider a cognitive radio network with two types of users: primary users (PUs) and cognitive users (CUs), whose locations follow two independent Poisson point processes. The cognitive users follow the policy that a cognitive transmitter is active only when it is outside the primary user exclusion regions. Due to the interaction between the primary users and the cognitive users through exclusion regions, an exact calculation of the interference and the outage probability seems unfeasible. Instead, we derive bounds for the interference (in the form of Laplace transform) and the outage probability. Two network models, the bipolar and the nearest-neighbor model, are considered.

This paper proposes two energy-efficient techniques suitable for cooperative spectrum sensing for cognitive radios. Cooperative spectrum sensing collects information from different users to make final decisions. The simplest information to be collected is the decision of each user, and this method is analogous to voting. First of all, we propose a voting scheme based on users' own confidence. Users vote only when they are confident about their spectrum sensing results. Simulation results show that about 10% to 40% of transmission energy can be saved compared to traditional voting schemes. Secondly, we propose a cluster-collect-forward scheme to save energy spent on vote-collection and information exchange. This scheme relies on clustering for vote-collection, and combing and forwarding for information propagation. Our scheme shows significant energy saving from 65% to over 95% compared to the broadcasting scheme. Besides, the processing time is only about half of the broadcasting scheme. Finally, these two proposed techniques can be easily combined to further improve the overall performance.

This paper proposes and compares two new physical layer architectures for cognitive radio. The idea of cognitive radio has been proposed for a while, but little research has been done on detailed architectural design and complexity analysis of physical layer functions and systems. Two cognitive radio functional architectures are proposed: one is based on the traditional communication components and the other is based on blind signal separation. Complexities of critical cognitive radio functions are evaluated and the two proposed architectures are compared.