Search

Cooperative communication exploits spatial diversity via relay node antennas to increase data rates in wireless networks. Relay node selection, therefore, plays a critical role in system performance. This paper examines the problem of relay node selection in cooperative networks, in which one relay node can be used by multiple source–destination transmission pairs, and all transmission pairs share the same set of relay nodes. Centralized approaches may exhibit higher complexity when the number of source nodes is increased. This paper gives source nodes self-optimizing and self-learning abilities and enables them to autonomously select relay nodes. A fully distributed approach, called a “Decentralized Learning-based Relay Assignment” (DLRA) algorithm, is proposed to achieve this goal. DLRA uses a reinforcement learning technique called stochastic learning automata, with each source node attaining the self-learning ability to find an appropriate relay node according to environmental feedback. This paper shows the convergency, optimality, and performance of DLRA via mathematical analysis and evaluates the performance of DLRA in two different network systems: a cooperative ad hoc network and a Long-Term Evolution (LTE)-Advanced relay network. The experimental results present several properties of DLRA: the effectiveness of DLRA in cooperative communication systems, the significant improvements made by DLRA in fairness, and the capacity of each node in LTE-Advanced systems. [ABSTRACT FROM PUBLISHER]

We consider the problem of multiantenna source detection with dimension reduction in distributed wireless networks. Traditional strategies typically collect and perform raw data at the fusion center. In moderate-to-large-scale networks, however, the schemes create the bottleneck of computation and communication for high-dimensional data. The goal of this paper is to design a distributed algorithm in multiple-antenna wireless networks to project the raw data into the low-dimensional data to reduce the communication and computation burden while maintaining high detection performance. In this paper, a pseudosketching matrix is constructed to transform the raw data of each multiple-antenna node into the low-dimensional data. Furthermore, it is transformed into the vector by using the subspace method. By gathering the data vectors of all nodes at the fusion center, the eigenvalue-based detection methods, such as the generalized likelihood ratio test (GLRT), can be applied directly to determine if the source signal exists or not. Moreover, using the concentration inequalities of subgamma random variables, the theoretical analysis is derived to support the claim that the proposed algorithm has high detection performance. The simulations are presented to demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Cramé r–Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility. [ABSTRACT FROM AUTHOR]

Wireless network virtualization is a promising technique for future wireless networks. In this paper, different from traditional virtualization approaches by means of resource isolation at the subchannel or time-slot level, we propose a novel framework of heterogeneous cellular network virtualization combined with interference alignment (IA) technology, which utilizes IA to cancel the mutual interference, by aligning the interference from other transmitters into a lower dimensional subspace at each receiver. In this framework, we formulate the virtual resource allocation as a joint virtualization and IA problem, considering the gain not only from interference mitigation introduced by IA but from the sum-rate improvement brought by virtualization as well. In addition, to reduce the computational complexity, with the recent advances in discrete stochastic approximation (DSA), we propose a two-step algorithm to solve the formulated problem. The basic principle is to design IA schemes for each feasible association combination and then to traverse the association space to search for the optimal association combination with the maximum sum rate. Extensive simulations are conducted with different system parameters to show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

This paper studies robust resource-allocation algorithm design for a multiuser multiple-input–single-output (MISO) cognitive radio (CR) downlink communication network. We focus on a secondary system that provides wireless unicast secure layered video information to multiple single-antenna secondary receivers. The resource-allocation algorithm design is formulated as a nonconvex optimization problem for the minimization of the total transmit power at the secondary transmitter. The proposed framework takes into account a quality-of-service (QoS) requirement regarding video communication secrecy in the secondary system, the imperfection of the channel state information (CSI) of potential eavesdroppers (primary receivers) at the secondary transmitter, and a limit for the maximum tolerable received interference power at the primary receivers. Thereby, the proposed problem formulation exploits the self-protecting architecture of layered transmission and artificial noise generation to ensure communication secrecy. The considered nonconvex optimization problem is recast as a convex optimization problem via semi-definite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. In addition, two suboptimal resource-allocation schemes are proposed for the case when the solution of the dual problem is unavailable for constructing the optimal solution. Simulation results demonstrate significant transmit power savings and robustness against CSI imperfection for the proposed optimal and suboptimal resource-allocation algorithms employing layered transmission compared to baseline schemes employing traditional single-layer transmission. [ABSTRACT FROM PUBLISHER]

The ultimate efficiency of a wireless network is achieved when various resources are allocated in a joint optimization process. This paper proposes the optimal solution for a joint power allocation and relay assignment (JPARA) problem subject to the sum-power constraint at the source node. This formulation is applicable to different system models. Specifically, we assume a network consisting of one source node, one destination node, and $N$ relays, where the source node uses orthogonal frequency-division multiplexing (OFDM) to transmit its information to the destination. Each relay should be assigned to one of the subcarriers, and the source node should distribute its power among the subcarriers. We propose an algorithm to find the JPARA solution based on the max-min criterion. The optimality of the proposed algorithm is analytically proved, and its complexity is calculated to be $O(N^4)$. We show that our proposed algorithm offers significant improvement in the performance of the worst end-to-end link compared with the separate optimization case. To further improve the performance, a new approach is proposed, in which each subcarrier is assigned a predetermined initial power, and the remaining power is distributed through the proposed JPARA algorithm. [ABSTRACT FROM AUTHOR]

Cooperative vehicle safety (CVS) systems operate based on frequent broadcast of vehicle state (e.g., position, heading, and speed) and safety information to other vehicles over a wireless local area network. Such networks are formed using dedicated short-range communications (DSRC) technology. In crowded networks, the shared wireless channel may become congested and fail to deliver the broadcast information, potentially affecting the reliability of CVS applications. This issue, which is known as a scalability issue, has been identified as one of the challenges to be addressed with high-scale deployment of vehicle safety networks. Several scalability algorithms have been recently introduced and studied in the literature; currently, a select number of algorithms are under study by industry for possible adoption and standardization. One method used by some algorithms is congestion control through power or range adaptation. In this paper, we analyze two existing range control algorithms in terms of time stability and spatial fairness. We derive stability conditions for the main range control scheme that is currently under study. An improved algorithm that relaxes these conditions is then proposed, and its convergence properties are studied in detail. We show that the algorithm is stable and can be controlled to quickly converge to desired operation points. Finally, fairness properties of several power control schemes are studied, and an enhancement method is presented that ensures that spatial fairness is achieved by the presented power control schemes. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for time-varying parameter adaptive throttle and brake control for vehicle speed tracking. A control algorithm has been developed based on a linearized longitudinal vehicle model with characteristic lumped parameters. The lumped parameters are slowly time varying, except when a vehicle experiences gear shift. Combined parameter adaptation and throttle/brake control algorithm have been developed. The performance of the proposed control algorithm has been evaluated via simulations and vehicle tests. Since the proposed control algorithm has been designed using a generic form of the vehicle model, it can be implemented for different classes of vehicles with no information about the vehicle powertrain and the brake system. It has been shown from both simulations and vehicle tests that the vehicle speed tracking performance is robust to external disturbance. [ABSTRACT FROM PUBLISHER]

In the presence of perfect channel state information (CSI), the achievable degrees of freedom (DoF) in wireless interference networks can be linearly scaled up with the number of users. Achievability is based on the idea of interference alignment (IA). However, in the presence of imperfect CSI, the sum rate becomes degraded, and full DoF may no longer be achievable. In this paper, we propose novel least squares (LS)- and minimum mean square error (MMSE)-based IA schemes that adaptively design beamformers by relying on the availability of imperfect CSI and knowledge of the channel estimation error variance in advance. Interestingly and unlike the other robust algorithms, the proposed adaptive schemes do not impose extra computational complexity compared to their nonadaptive counterparts. It is shown that the LS-based IA is able to outperform interference leakage minimization algorithms under both perfect and imperfect CSI. Furthermore, we compare the performance of the proposed MMSE-based IA with maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. We show that while under perfect CSI, the MMSE-based IA achieves the same performance as that of Max-SINR, the former outperforms the latter under CSI mismatch. Meanwhile, it is shown that the proposed MMSE-based IA needs less CSI to be available and has less computational complexity compared to Max-SINR. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss three different design criteria for a distributed antenna system (DAS). They include maximizing throughput under the constraint of the overall transmit power, minimizing the overall transmit power while guaranteeing the minimum spectral efficiency (SE) requirements, and maximizing energy efficiency (EE) under the constraints of minimum SE requirements and overall transmit power. The optimization problems for the first two criteria are signomial programming (SP) problems when considering the interference among the mobile stations (MSs), which are nonlinear nonconvex and do not lead to the closed-form solutions directly. Exploiting the nature of geometric mean, we transform the SP problems into geometric programming (GP) problems, which can be solved by turning them into convex problems. For the third criterion, using the theorem of fractional programming, we first transform the fractional optimization problem into an equivalent optimization problem in a subtractive form, which is an SP problem. We then obtain its solution by transforming the SP to the GP problem as we have done for the previous two problems. Based on these design criteria, three power allocation algorithms are developed for the downlink multiuser DAS. Depending on the application environments, we can use the first and second criteria to achieve the highest throughput and to save energy, respectively, while balancing throughput and energy consumption using the third criterion. [ABSTRACT FROM AUTHOR]

In this paper, we consider an approximate weighted least squares (AWLS) estimate for the energy-based localization in wireless sensor networks, which is a quartic programming and is nonconvex in general. Based on a matrix rank-one decomposition (ROD) technique, we prove that this model can be transformed into a convex constrained programming model, and the proof is constructive. Furthermore, by a theoretical analysis of the semidefinite programming (SDP) model proposed by Wang, we present an easy verifiable condition that characterizes when the SDP model admits no gap with the primal AWLS model. By using this condition, we design a new algorithm that combines the SDP algorithm with the ROD technique. The new algorithm can solve the AWLS model exactly in theory and overcomes the shortcomings of the SDP algorithm. The numerical results show that, at low noise levels, the performance of the proposed algorithm is very close to Cramer–Rao bound accuracy, while the SDP method is not; at high noise levels, the performance of the proposed algorithm is similar to the SDP method while the SDP method is regarded best in the corresponding reference. Numerical simulations indicate that the proposed new algorithm performs better thanother existing methods. [ABSTRACT FROM PUBLISHER]

Airbag systems have become an essential safety device for guaranteeing the physical well-being of drivers and their passengers. Unlike other safety devices, airbags are used as the last resort in a collision, and because they are directly linked to the life of the driver and passengers, the proper functioning of the system is an issue of paramount importance. Hence, to ensure the precision and reliability of airbag operation, it is necessary to design a robust crash algorithm. Currently, several companies are working to achieve an optimal deployment time for airbags in collisions by diversifying the type and location of crash-related sensors. Nevertheless, several problems must still be confronted. For instance, when a vehicle operates off road or when the sensor inside the airbag control unit (ACU) receives a powerful shock, the vehicle's airbags may inadvertently deploy, although no collision has occurred, because a crashlike signal is delivered to the ACU. Alternately, in a collision situation that requires airbag deployment, the crash algorithm may make an erroneous judgment with regard to the collision configuration and miss the time frame for airbag deployment or fail to deploy the airbags altogether. Such problems can be attributed to the following two major causes: 1) Only signals produced through crash tests are used in the design of crash algorithms, and 2) the algorithms themselves only utilize postcrash input from the relevant sensors. To resolve these issues, this paper proposes a precrash algorithm that generates information about the crash scenarios before a collision has occurred. The purpose of the precrash algorithm is to make judgments about the impending collision configuration prior to impact by estimating the behavior of frontal objects and to communicate this information to the crash algorithm to enable correct recognition of the crash scenarios. This paper also proposes a crash algorithm based on crash-related sensors for the verification of and interfacing with the proposed precrash algorithm. The limitations of existing crash algorithms, which deploy airbags using only postcrash signals, were resolved through the development of an integrative crash algorithm that combines the precrash and crash algorithms to reflect precrash information. The developed algorithm was verified by running a wide range of simulations using CarSim, based on data from real crash tests. The results showed that, compared with independently using the crash algorithm, adding precrash information estimation significantly improved the reliability of airbag deployment. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate spectrum sharing with power-constrained multiradio secondary users (SUs) in cognitive radio networks. The scenario under consideration consists of a primary spectrum owner who runs auctions for leasing her idle channels and multiple SU bidding for winning the usage of spectrum channels. Different from existing works in the literature with an assumption of single-minded SUs, in this paper, SUs can benefit from flexible quantity of channels. In addition, since each SU is ordinarily equipped with a fixed number of radios, she cannot utilize the amount of channels that exceed her radio capacity. Moreover, each SU has a certain power limitation so that the quality of service (QoS) of her transmission may also be constrained, even though the number of allocated channels is increased. To jointly address all these challenges, a novel ascending-price progressive auction algorithm is proposed, where the spectrum allocation decisions are made by gradually increasing the unit channel price. Theoretical analyses prove that the proposed algorithm meets the properties of QoS satisfaction, individual rationality, and incentive compatibility and achieves Pareto optimality. Simulation results further demonstrate that the proposed auction algorithm can improve both the auction revenue and the social welfare, and increase the number of winning SUs compared to the counterparts. [ABSTRACT FROM PUBLISHER]

Existing research on a simultaneous wireless information and power transfer (SWIPT) system is based on the assumption of Gaussian inputs. However, the optimal design on Gaussian inputs may lead to dramatic performance loss for practical systems with finite-alphabet inputs. This paper focuses on the precoder design for a SWIPT system with finite-alphabet inputs and instantaneous channel state information over a multiple-input multiple-output channel. We formulate the optimal precoder design as an optimization problem, in which the objective is to maximize the mutual information over the channel from the transmitter to the information receiver under the constraints of transmit power and harvested energy threshold. The formulated problem is NP-hard, so a global optimal solution cannot be found within the polynomial time. The main contributions of this paper are as follows: 1) By using its structure, the NP-hard problem is relaxed to a semidefinite programming problem. Then, a general solving framework for both co-located and separated receiver cases, based on the semidefinite relaxation (SDR) technique, is developed to achieve a near optimal precoder. 2) For the case of co-located receivers, we show that the optimal precoder design is a concave problem with respect to power allocation; then, a specific algorithm for co-located receivers is proposed. Compared with the general SDR-based method, the specific algorithm for co-located receivers exhibits almost the same performance but much lower complexity. 3) The performance of several practical co-located receiver designs is analyzed in SWIPT systems with finite-alphabet inputs. Finally, we provide simulation results to show the efficacy of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate receivers for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Vehicular channels are characterized by multiple paths and time variations, which introduce challenges in the design of receivers. We propose an algorithm for IEEE 802.11p-compliant receivers, based on orthogonal frequency-division multiplexing (OFDM). We employ iterative structures in the receiver as a way to estimate the channel despite variations within a frame. The channel estimator is based on factor graphs (FGs), which allow the design of soft iterative receivers while keeping acceptable computational complexity. Throughout this paper, we focus on designing a receiver that offers a good complexity–performance tradeoff. Moreover, we propose a scalable algorithm to be able to tune the tradeoff, depending on the channel conditions. Our algorithm allows reliable communications while offering a considerable decrease in computational complexity. In particular, numerical results show the tradeoff between complexity and performance measured in computational time and bit error rate (BER), as well as frame error rate (FER) achieved by various interpolation lengths used by the estimator, which both outperform by decades the standard least squares (LS) solution. Furthermore, our adaptive algorithm shows a considerable improvement in terms of computational time and complexity against state-of-the-art and classical receptors while showing acceptable BER and FER performance. [ABSTRACT FROM PUBLISHER]

Spectrum sensing is a fundamental component of cognitive radio (CR). How to promptly sense the presence of primary users (PUs) is a key issue to a CR network. The time requirement is critical in that violating it will cause harmful interference to the PU, leading to a system-wide failure. The motivation of this paper is to provide an effective spectrum sensing method to detect PUs as soon as possible. In the language of streaming-based real-time data processing, short time means small data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on McDiarmid's concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV (DTV) signal, show that this proposed method can operate either using smaller data or working under a lower signal-to-noise ratio (SNR) environment. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of distributed channel selection for interference mitigation in a canonical communication network. The channel is assumed time-varying, and the active user set is considered dynamically variable due to the specific service requirement. This problem is formulated as an exact potential game, and the optimality property of the solution to this problem is first analyzed. Then, we design a low-complexity fully distributed no-regret learning algorithm for channel adaptation in a dynamic environment, where each active player can independently and automatically update its action with no information exchange. The proposed algorithm is proven to converge to a set of correlated equilibria with a probability of 1. Finally, we conduct simulations to demonstrate that the proposed algorithm achieves near-optimal performance for interference mitigation in dynamic environments. [ABSTRACT FROM AUTHOR]

Untethered vehicular roadside infrastructure will eventually be deployed using various energy sustainable designs. In the solar-powered case, cost-effective nodes must be provisioned with a combination of a solar panel and a battery, which is sufficient to prevent future node outage. During the network design phase, a bandwidth usage profile (BUP) is assumed, and historical solar insolation traces are used to determine the minimum-cost provisioning that is required for each network node. In practical systems, however, there are usually restrictions in the way that the nodes can be positioned, and this results in a time-varying and node-dependent attenuation of the available solar energy. Unfortunately, conventional resource provisioning methods cannot take this into account; therefore, the deployed system may be unnecessarily expensive. In this paper, the resource provisioning problem is considered from this point of view. We first review conventional resource provisioning mechanisms and give an example that shows the value of introducing positional solar insolation awareness. A provisioning algorithm is then introduced that takes known positional variations into consideration when performing the energy provisioning. A variety of results are then presented, which show that reductions in total network provisioning cost can be obtained using the proposed methodology, compared with conventional algorithms. The proposed algorithm also performs very well compared with a linear programming formulation, which gives lower bounds on the node resource provisioning cost assignments. [ABSTRACT FROM PUBLISHER]

The design of efficient and distributed scheduling algorithms is essential to garner the full potential of cognitive radio networks (CRNs). In this paper, we propose a distributed orthogonal frequency-division multiplexing (OFDM)-based scheduling algorithm, which is called the collision-queue-regulated algorithm, that aims to limit the collision rate to a level imposed by primary users of a CRN. Via a novel equivalent queue system analysis, we prove that the proposed algorithm can achieve at least a constant fraction of the capacity region in the many-channel regime, and our numerical studies indicate that the proposed distributed collision-queue-regulated algorithm achieves a throughput very close to that achievable by a centralized throughput-optimal back-pressure-based scheduling algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a synchronization and channel estimation method for amplify-and-forward two-way relay networks (AF-TWRNs) based on a low-complexity maximum-likelihood (LCML) algorithm and a joint synchronization and channel estimation (JSCE) algorithm. For synchronous AF-TWRNs, the LCML algorithm blindly estimates general nonreciprocal flat-fading channels. We formulate the channel estimation as a convex optimization problem and obtain a closed-form channel estimator. Based on the mean square error (MSE) analysis of the LCML algorithm, we propose a generalized LCML (GLCML) algorithm to perform channel estimation in the presence of the timing offset. Based on the approximation of the LCML algorithm, the JSCE algorithm is proposed to estimate jointly the timing offset and channel parameters. The theoretical analysis shows that the closed-form LCML channel estimator is consistent and unbiased. The analytical MSE expression shows that the estimation error approaches zero in scenarios with either a high signal-to-noise ratio (SNR) or a large frame length. Monte Carlo simulations are employed to verify the theoretical MSE analysis of the LCML algorithm. In the absence of perfect timing synchronization, the GLCML algorithm selects an estimation sample, which produces the optimal channel estimation, according to the MSE analysis. Simulation results also demonstrate that the JSCE algorithm is able to achieve accurate timing offset estimation. [ABSTRACT FROM PUBLISHER]

Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g., vehicle speed and location or traffic signal state and timing). Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology. This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver. This paper also extends the convergence analysis for two important cases, i.e., measurement noise in the input signal and delay in the update process. This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the power–delay tradeoff for the newly emerging wireless powered communication networks, in which wireless powered devices (WPDs) harvest energy from a power station via wireless energy transfer in the downlink and then communicate with an information receiving station in the uplink. Each WPD is equipped with an energy buffer and a data buffer to store the random harvested energy and the bursty data arrivals, respectively. To minimize the time-averaged power consumption, a stochastic optimization problem is formulated subject to both constraints of data queue stability and harvested energy availability. By employing Lyapunov optimization theory, we transform the stochastic optimization problem into a series of deterministic problems, where each is further proved to become the standard convex optimization problem and, hence, can be effectively and optimally solved by standard convex optimization techniques. Correspondingly, we propose an Online Power and TIMe Allocation (Optima) algorithm, which requires no a priori distribution knowledge of channel states and data arrivals. Most importantly, the proposed algorithm achieves the power–delay tradeoff as [\mathcalO(\text1/V),\mathcalO(V)], with $V$ being a system control parameter, and provides a significant method to control the power–delay performance on demand in system design. Simulation results verify the theoretical analysis and validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the shortest link scheduling problem in wireless networks under the signal-to-interference-plus-noise ratio (SINR) model. We propose an $O({}\log({}l_{\max}/l_{\min}{}){})-approximation algorithm called shortest link scheduling with power control (SLSPC) with oblivious power assignment and an O(\log1+\varphi(l\max/l\min)-approximation algorithm called shortest link scheduling with uniform or mean power assignment (SLSUM) with uniform or mean power control, where \varphi > 0 is a constant serving as a regulatory factor for slight transmit power adjustment, and where l\max and l\min denote the lengths of the longest and shortest links, respectively. We conduct a rigorous theoretical performance analysis to analyze the feasibility and approximation factors of the proposed algorithms. We also carry out an extensive comparison-based simulation study, whose results indicate that the performances of SLSPC and SLSUM are superior over the state of the art as the set of the so-called “black and gray” links, which are difficult to schedule and should be sequentially scheduled, is completely removed by adjusting the transmit power appropriately via $\varphi$. Our numerical analysis demonstrates that the approximation ratios of our algorithms are tighter than the best known ratios. [ABSTRACT FROM PUBLISHER]

Multiple-input–multiple-output (MIMO) full-duplex relaying (FDR) has been considered an efficient technique to provide coverage to users where their direct links from the base station (BS) are too weak for reliable signal reception. However, when multiple MIMO full-duplex relays are deployed in a network, the signal reception quality relies on the effective suppression of multiple types of interference. In this paper, the distributed beamforming is studied for the MIMO FDR network by formulating a power minimization problem with a nonstrict convex objective function (total transmit power of both the BS and all the relays in the network) under individual user rate constraints. We come up with two iterative distributed beamforming algorithms, Algorithm 1 for relays equipped with single receive antenna and Algorithm 2 with multiple receive antennas. The former can yield a global optimal solution of the power minimization problem, whereas the latter can yield only a local optimal solution due to conservative successive convex approximations (SCAs) performed at each iteration, and a rigorous analysis on the upper bounds of step sizes is proposed to guarantee their convergence. The proposed two algorithms only require local information exchange between relays and hence are scalable for different network sizes and topologies. An “early termination” strategy in the operation of the proposed two algorithms is also presented to acquire an acceptable transmit power solution with less computation time consumption and thus suitable for realistic applications. Finally, some simulation results are provided to demonstrate that the proposed two algorithms perform well and significantly better than the existing state-of-the-art scheme reported in the work of Lee and Shin. [ABSTRACT FROM PUBLISHER]

We consider a promising application for electric taxis (eTaxis) in transportation cyberphysical systems. The new rideshare scheme introduced herein takes into consideration both the limited battery of eTaxis and the user requirements. In the proposed eTaxi-sharing system, a passenger may share a taxi with others to enjoy a reduced fare and can potentially transfer from one eTaxi to another before reaching her destination, as long as her total trip time is within the maximum she specifies to be tolerable. Transfers are restricted to only take place at the designated (safe and convenient) battery charging stations scattered around the city. When an eTaxi comes to a charging station to pick up/drop off a transfer passenger, the eTaxi's battery can be charged. In this paper, we address a new optimization problem called Transfer-Allowed Shared eTaxis (TASeT), whose goal is to schedule an eTaxis service and find optimal rideshare and transfer plans to maximize the system throughput in terms of the number of passengers served by the taxi service within a given time period. A mixed-integer programming (MIP) formulation is presented to solve TASeT, along with an efficient greedy heuristic. Aside from large-scale simulation, we also present a case study that utilizes real taxi traces collected from the city of Shanghai, China. Compared with the nontransferable taxi-sharing (NTT) case, our solution could improve the number of served passengers and shared travel distance by 22% and 37%, respectively, during rush hours. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of user association in heterogeneous networks (HetNets). With the existence of small cells, to better associate user equipment (UE) with base stations (BSs), we propose a novel collaborative filtering (CF)-based wireless network recommendation system, which involves social interactions among UEs. Different from traditional user association schemes, the UEs in the proposed system interact with each other by rating the BSs to improve recommendation qualities. Similarity between UEs' preferences to the quality of service (QoS) and historical information on the QoS of networks are taken into consideration. The core of the network recommendation system is a rating matrix maintained by the operator. A UE can automatically rate the connected BS according to real-time measured parameters (interference, packet loss rate, time delay, etc.) that it experiences during service. Taking the Voice over Internet Protocol (VoIP) service as an example, the measured parameters can be mapped to certain QoS levels with the E-model, which is used as the ratings. During the process of handover, the operator recommends the BSs to the UEs based on the rating matrix. While the ratings help the UEs gain better recommendation qualities, they tend to avoid the costs, e.g., computational and bandwidth resources. Therefore, the UEs have to trade off between the profits and costs brought about by the rating procedure. A satisfaction game is formulated to deal with this problem. We use a utility function to measure a UE's satisfaction. When every UE's utility comes to a preset level, the game is considered to reach the satisfaction equilibrium (SE). An algorithm is designed to learn the SE, and the convergence is analyzed. The advantages of the proposed system lie in two aspects. First, it comprehensively considers multiple factors that influence the QoS, instead of signal strength only. Second, the historical information helps UEs select BSs with better long-term QoS but not immediate QoS. Simulation results show the effectiveness of the learning algorithm on both learning the SE and avoiding severe congestions in each BS. [ABSTRACT FROM PUBLISHER]

This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit–receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of nonoverlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R, which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R \le \text{2} at the LLs, the stability of the entire network can be assured, even when each user only has partial network information. For practical implementation, we propose each LL to use R=\text{2}$ as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed, and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput. [ABSTRACT FROM PUBLISHER]

Chained turbo equalization (CHATUE) has been recently recognized as a low-complexity frequency-domain turbo equalization technique that eliminates the necessity of transmitting the cyclic prefix (CP), and hence allows for spectrally efficient signaling in wireless communications. However, two issues arise from the original version of CHATUE (referred to as CHATUE1) as a consequence of eliminating the CP, which are the noise enhancement and the latency due to the time-concatenated structure. This paper proposes a new version of CHATUE (referred to as CHATUE2) to solve the noise enhancement problem. CHATUE2 retrieves the circulant structure of the channel matrix, originally inherent within the CP transmission, by utilizing composite replica signals that combine the received signals and the soft reference signals replicated from the log-likelihood ratio (LLR) fed back from the decoder. For this purpose, this paper determines the optimal combining ratio based on the minimum-mean-square-error (MMSE) criterion. CHATUE2 is hence able to achieve an improvement in bit-error-rate (BER) performance over CHATUE1. In addition, this paper provides a solution to solving the latency problem by making a practical assumption on the training sequence (TR) transmission, which is required to perform channel estimation generally, in practical systems. Furthermore, this paper proposes a new channel estimation technique, i.e., chained turbo estimation (CHATES), which improves the spectrum efficiency and asymptotically achieves the Cramér–Rao bound (CRB). CHATES assumes that the TR length is exactly equal to the channel impulse response (CIR) length, although the conventional technique requires twice as long or even longer TR lengths. Numerical results show that CHATUE2 with CHATES achieves a gain of 1 dB over conventional turbo equalization with a CP at a BER of \10^-5 in realistic propagation scenarios represented by channel sounding measurement data and in model-based frequency-selective fading channels. [ABSTRACT FROM PUBLISHER]

This paper discusses options for testing correspondence algorithms in stereo or motion analysis that are designed or considered for vision-based driver assistance. It introduces a globally available database, with a main focus on testing on video sequences of real-world data. We suggest the classification of recorded video data into situations defined by a cooccurrence of some events in recorded traffic scenes. About 100–400 stereo frames (or 4–16 s of recording) are considered a basic sequence, which will be identified with one particular situation. Future testing is expected to be on data that report on hours of driving, and multiple hours of long video data may be segmented into basic sequences and classified into situations. This paper prepares for this expected development. This paper uses three different evaluation approaches (prediction error, synthesized sequences, and labeled sequences) for demonstrating ideas, difficulties, and possible ways in this future field of extensive performance tests in vision-based driver assistance, particularly for cases where the ground truth is not available. This paper shows that the complexity of real-world data does not support the identification of general rankings of correspondence techniques on sets of basic sequences that show different situations. It is suggested that correspondence techniques should adaptively be chosen in real time using some type of statistical situation classifiers. [ABSTRACT FROM PUBLISHER]

Spectrum sensing data falsification (SSDF) attacks represent a major challenge for cooperative spectrum sensing (CSS) in cognitive radio (CR) networks. In an SSDF attack, a malicious user or many malicious users send false sensing results to the fusion center (FC) to mislead the global decision about spectrum occupancy. Thus, an SSDF attack degrades the achievable detection accuracy, throughput, and energy efficiency of CR networks (CRNs). In this paper, a novel attacker-identification algorithm is proposed that is able to skillfully detect attackers and reject their reported results. Moreover, we provide a novel attacker-punishment algorithm that aims at punishing attackers by lowering their individual energy efficiency, motivating them either to quit sending false results or leave the network. Both algorithms are based on a novel assessment strategy of the sensing performance of each user. The proposed strategy is called delivery-based assessment, which relies on the delivery of the transmitted data to evaluate the made global decision and the individual reports. Mathematical analysis and simulation results show promising performance of both algorithms compared with previous works, particularly when then the number of attackers is very large. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an intelligent traffic light controlling (ITLC) algorithm. ITLC is intended to schedule the phases of each isolated traffic light efficiently. This algorithm considers the real-time traffic characteristics of the competing traffic flows at the signalized road intersection. Moreover, we have adopted the ITLC algorithm to design a traffic scheduling algorithm for an arterial street scenario; we have thus proposed an arterial traffic light (ATL) controlling algorithm. In the ATL controlling algorithm, the intelligent traffic lights installed at each road intersection coordinate with each other to generate an efficient traffic schedule for the entire road network. We report on the performance of ITLC and ATL algorithms for several scenarios using NS-2. From the experimental results, we infer that the ITLC algorithm reduces, at each isolated traffic light, the queuing delay and increases the traffic fluency by 30% compared with the online algorithm (OAF) traffic light scheduling algorithm. The latter algorithm achieved the best performance when compared with the OAF traffic light scheduling algorithm. On the other hand, the ATL controlling algorithm increases the traffic fluency of traveling vehicles at arterial street coordinations by 70% more than the random and separate traffic light scheduling system. Furthermore, compared with the previously introduced traffic scheduling ART-SYS, the ATL controlling algorithm decreases the average delay at each traffic light by 10%. [ABSTRACT FROM AUTHOR]

In this paper, an iterative design method for beamformers is proposed for multiple-input multiple-output (MIMO) two-way relay networks with multiple pairs of source nodes. It is known that the joint beamformer design problem based on the minimum mean-square-error (MMSE) is known to be nonconvex, and it cannot be analytically solved. To manage this difficulty, a novel iterative algorithm is proposed, which uses duality relationships. First, the MSE duality for MIMO two-way relay networks with multiple pairs of nodes is presented. Moreover, the optimization problem is divided into three subproblems, which can be separately solved; then, the duality is applied to the proposed iterative algorithm, which provides closed-form solutions for all subproblems. The numerical results demonstrate that the proposed method improves network performance and can be applied to various communication systems that use the two-way relay network. [ABSTRACT FROM AUTHOR]

This paper proposes a particle filter framework to perform an online estimation of the unsaturated buffers of the stations in the IEEE 802.11 distributed coordination function (DCF) network. Using this framework, an access point can adapt flow control to its serving stations and configure related parameters dynamically, thus improving the system throughput and reducing the packet latency. Current research analyzing the unsaturated condition in the IEEE 802.11 DCF network is based on the steady-state model, whereas this proposed method is devoted to the dynamic estimation for the probability distribution of the unsaturated buffer in the stations, in either homogeneous or heterogeneous networks. This study also employs theoretical support from Bayesian inference to the particle-filtering algorithm. The estimation accuracy and effectiveness were evaluated via root mean square error (RMSE) and time complexity. Furthermore, we considered different network loads and convergence speeds in our analysis. Our analysis demonstrated that the proposed dynamic estimation scheme has greater awareness of the traffic changes in the varying wireless network when compared with the traditional static traffic model. [ABSTRACT FROM AUTHOR]

To efficiently trade off system sum-rate and link fairness, this paper is dedicated to maximizing the sum of \alpha-fair utility in spectrum-sharing networks, where multiple interfering links share one channel. In the literature, three special cases, including \alpha=\mbox{0} (sum-rate maximization), \alpha=\mbox{1} (proportional fairness), and \alpha=\infty (max-min fairness), have been investigated; the complexity for cases \mbox{1} < \alpha < \infty and \mbox{0} < \alpha < \mbox{1} is still unknown. In this paper, we prove that the problem is convex when \mbox1 < \alpha < \infty and is NP-hard when \mbox0 < \alpha < \mbox1. To deal with the latter case, we transform the objective function and represent it by the difference of two concave functions (D.C.). Then, a power allocation algorithm is proposed with fast convergence to a local optimal point. Simulation results show that the proposed algorithm can obtain global optimality in two-link cases when \mbox0 < \alpha < \mbox1. In addition, we can get a flexible tradeoff between sum-rate and fairness in terms of Jain's index by adjusting $\alpha$. [ABSTRACT FROM AUTHOR]

Compared to battery-powered wireless nodes having a constant but limited power supply, wireless nodes having energy harvesting (EH) capability may greatly prolong the network's sustainability. However, the energy usage policies (EUPs) have to be carefully designed according to the characteristics of the random power supply gleaned from the environment. In this paper, we carry out the outage analysis of a point-to-point (P2P) network relying on an EH transmitter, which has a finite energy buffer (EB) for transmission over a fading channel when having random energy arrival rates. A discrete Markov chain (DMC) model is proposed for characterizing the energy state of the EB, which is then used for quantifying the outage probability (OP) over the fading channels. Then, we propose both a novel 2-D and a low-complexity 1-D search algorithm for finding the specific EUPs, which are capable of minimizing the OP for the P2P network considered. It is shown that the EUP found by both algorithms outperforms the state-of-the-art EUPs disseminated in the open literature. Furthermore, we consider a multiple-access network having $M$ EH-aided sources, where we propose a distributed EUP optimization (DEUPO) algorithm and then minimize the OP relying on the local optimization of each EH-aided source. [ABSTRACT FROM AUTHOR]

Using a spatial multiplexing transmission scheme can improve the data rate in multiple-input–multiple-output (MIMO) relaying systems, while making signal detection more difficult at receivers. Aiming at lowering the computational complexity of the receiver, this paper proposes a maximum likelihood combining (MLC) algorithm for spatial multiplexing MIMO amplify-and-forward (AF) relaying systems in a Rayleigh flat-fading environment, which is implemented before maximum likelihood (ML) detection. The combining signal and equivalent channel are opportunely designed based on the ML rule in the MLC algorithm. We also formulate the diversity gain of the systems that employ the MLC algorithm mathematically, induced by the Chernoff bound of pairwise error probability (PEP). An upper bound on the symbol error probability (SEP) for the MLC algorithm with multiple modulations is also given, based on the derived bound of PEP. Moreover, the complexities of ML receivers adopting the MLC algorithm and the conventional vector combining (VC) algorithm are analyzed. Numerical simulations indicate that systems with the MLC algorithm achieve the same performance while consuming lower computational complexity compared to that with the VC algorithm. [ABSTRACT FROM PUBLISHER]

To decrease potentially harmful effects to the environment and reduce costs of cellular network operators, the exploration of energy saving is significant. In this paper, we shed light on the following energy consumption minimization (ECM) problem: “how to select the smallest set of active base stations (BSs) that can preserve the quality of service (the minimum data rate) required by the users.” Since the ECM problem belongs to the field of integer programming and is NP-hard, we propose two low-complexity approximation algorithms that yield performance guarantees: the iterative minimal set cover algorithm and the iterative maximal coverage (IMC) algorithm. Moreover, by leveraging the submodularity theory, we establish the performance bound of the IMC algorithm. Finally, the computational complexity of our proposed algorithms is analyzed, and a simulation platform is developed to evaluate the number of active BSs of proposed approaches. The numerical results demonstrate that our proposed algorithms outperform the conventional counterpart. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cellular/infostation integrated network that supports on-demand data service delivery over a wireless link to users in a high-speed train. For the requests with different lifetimes and prices that they are willing to pay, we develop the optimal packet schedule that aims at earning the maximum revenue. To this end, we formulate the problem as an integer linear program (ILP). The problem cannot be solved efficiently by existing ILP solvers with guaranteed polynomial-time complexity in the worst case. By exploring the special structure of the formulated ILP, we propose a novel Checker algorithm and prove that this algorithm is guaranteed to find the offline optimal schedule in polynomial time. Based on the relevant insights, we further develop a class of online Checker algorithms that require only causal knowledge of service demands and wireless channel capacities. It is established that these online algorithms have a worst-case competitive ratio of 1/2, i.e., total revenue earned by them is at least half as much as the revenue earned by an offline optimal schedule that knows the complete a priori knowledge of future requests and channel conditions. Simulation results are provided to demonstrate the merit of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

Both orthogonal frequency-division multiple access (OFDMA) and femtocell are promising technologies providing subscribers with better services. However, due to the ad hoc nature of femtocells, there is a great challenge to mitigate interference, which may seriously compromise the benefits promised by this novel network architecture. This paper investigates the distributed subchannel allocation (DSA) for cotier interference mitigation in OFDMA-based femtocells, where the femtocells and macrocell transmit on orthogonal subchannels. Particularly, to intuitively study system performance, we formulate this problem as a noncooperative rate maximization game where the utility of each player or femtocell access point is its capacity instead of the incoming interference. Unfortunately, the uncertainty of the existence of the Nash equilibrium for this game makes it difficult to design efficient distributed schemes. To address this issue, we introduce a state space to reflect players' desire for new strategies and then devise a utility-based learning model that requires no information exchange between different players. Utilizing this model, a utility-based DSA algorithm is developed. Moreover, it is analytically shown that the Pareto-optimal solution can be achieved with our proposed algorithm, and as a result, the overall capacity can be efficiently improved, and the system interference can be efficiently mitigated. Finally, simulation results verify the validity of our analysis and demonstrate that our scheme performs comparably or even better compared with the existing strategies, which require information exchange among different femtocells. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the pilot design based on the mutual incoherence property (MIP) for sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) systems. With respect to the length of channel impulse response (CIR), we first derive a sufficient condition for the optimal pilot pattern generated from the cyclic different set (CDS). Since the CDS does not exist for most practical OFDM systems, we propose three pilot design schemes to obtain a near-optimal pilot pattern. The first two schemes, including stochastic sequential search (SSS) and stochastic parallel search (SPS), are based on the stochastic search. The third scheme called iterative group shrinkage (IGS) employs a tree-based searching structure and removes rows in a group instead of removing a single row at each step. We later extend our work to multiple-input–multiple-output (MIMO) systems and propose two schemes, i.e., sequential design scheme and joint design scheme. We also combine them to design the multiple orthogonal pilot patterns, i.e., using the sequential scheme for the first several transmit antennas and using the joint scheme to design the pilot pattern for the remaining transmit antennas. Simulation results show that the proposed SSS, SPS, and IGS converge much faster than the cross-entropy optimization and the exhaustive search and are thus more efficient. Moreover, SSS and SPS outperform IGS in terms of channel estimation performance. [ABSTRACT FROM PUBLISHER]

As the binary sensing model is a coarse approximation of reality, the probabilistic sensing model has been proposed as a more realistic model for characterizing the sensing region. A point is covered by sensor networks under the probabilistic sensing model if the joint sensing probability from multiple sensors is larger than a predefined threshold $\varepsilon$. Existing work has focused on probabilistic point coverage since it is extremely difficult to verify the coverage of a full continuous area (i.e., probabilistic area coverage). In this paper, we tackle such a challenging problem. We first study the sensing probabilities of two points with a distance of $d$ and obtain the fundamental mathematical relationship between them. If the sensing probability of one point is larger than a certain value, the other is covered. Based on such a finding, we transform probabilistic area coverage into probabilistic point coverage, which greatly reduces the problem dimension. Then, we design the $\varepsilon$-full area coverage optimization (FCO) algorithm to select a subset of sensors to provide probabilistic area coverage dynamically so that the network lifetime can be prolonged as much as possible. We also theoretically derive the approximation ratio obtained by FCO to that by the optimal one. Finally, through extensive simulations, we demonstrate that FCO outperforms the state-of-the-art solutions significantly. [ABSTRACT FROM PUBLISHER]

In this paper, a novel one-bit feedback algorithm by exploiting cumulative positive feedback information is proposed to achieve carrier phase alignment at the receiver for distributed transmit beamforming. The proposed synchronization algorithm speeds up the convergence process by introducing a mechanism to increase the perturbation size adaptively, especially in situation with sudden change. Moreover, the proposed algorithm has the ability to track time-varying channels without the knowledge of channel state information. In situation with time-varying channels, the proposed algorithm not only has better convergence speed, but also provides better steady performance by compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Within self-organizing networks (SON), the cell outage compensation (COC) functionality is one of the most important use cases in self-healing in mobile communication networks. The state of the art has proposed different COC techniques, each of them to be indistinctly applied to all cells in outage. Conversely, this paper presents an important improvement of the COC function by adapting different COC strategies to different cell outage situations. With this objective, a novel COC methodology is proposed. When a cell outage occurs in a network, a detailed analysis of the faulty situation is carried out. The result of this analysis allows us to classify the degradation produced by the cell outage in the neighboring cells. Depending on this degradation, different COC algorithms should be applied to each affected neighboring cell. In addition, as another contribution, some COC algorithms based on handover parameters modifications have been applied to a COC problem. Results have shown that, by adapting the strategy to the outage impact on neighboring cells, the proposed method outperforms classic strategies. [ABSTRACT FROM AUTHOR]

As a response to the challenge of data traffic explosion in wireless networks, content caching in device-to-device (D2D) communication networks has emerged as a promising solution. However, in practical deployment, D2D content caching has its own problems. In particular, not all of the user devices are willing to share the content with others due to numerous concerns, such as security, battery life, and social relationship. In this paper, we consider the factor of social relationship in the deployment of D2D content caching. First, we apply stochastic geometry theory to derive an analytical expression of downloading performance for the D2D caching network. Specifically, a social relationship model with respect to the physical distance is adopted in our analysis to obtain the average download delay performance using random and deterministic caching strategies. Second, to achieve a better performance in more practical and specific scenarios, we develop a socially aware distributed caching strategy based on a decentralized learning automaton, to optimize the cache placement operation in D2D networks. Different from the existing caching schemes, the proposed algorithm not only considers the file request probability and the closeness of devices as measured by their physical distance but also takes into account the social relationship between D2D users. Our simulation results show that the proposed algorithm can converge quickly and outperforms the random and deterministic caching strategies. With these results, our work sheds insights on the design of D2D caching in the practical deployment of 5G networks. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of distributed throughput maximization in an opportunistic spectrum access network with multiple secondary users (SUs) and multiple primary channels is investigated. To address the challenges in designing efficient solutions in a dynamic and unknown environment, we formulate the optimization problem as a noncooperative game, which is further proved to be an ordinal potential game. We then propose a best-response-based algorithm to achieve the Nash equilibrium points (NEPs) of the formulated game, given that there exists a coordinator for SUs to work in a round-robin fashion and a common control channel for SUs to exchange their information. To further relieve the system overhead due to information exchange among SUs, we design a new stochastic learning automata (SLA)-based algorithm, called N-SLA, which can converge to the pure-strategy NEPs of the formulated ordinal potential game in a fully distributed way. To our best knowledge, we are the first to address the convergence issue of the SLA-based algorithms for general ordinal potential games. Simulation results validate the effectiveness of our proposed algorithms. [ABSTRACT FROM PUBLISHER]

This paper considers the antenna selection (AS) problem for a multiple-input multiple-output nonorthogonal multiple access system. In particular, we develop new computationally efficient AS algorithms for two commonly used scenarios: NOMA with fixed power allocation (F-NOMA) and NOMA with cognitive radio-inspired power allocation (CR-NOMA). For the F-NOMA scenario, a new max–max–max AS (A$^3$ -AS) scheme is first proposed to maximize the system sum-rate. This is achieved by selecting one antenna at the base station (BS) and corresponding best receive antenna at each user that maximizes the channel gain of the resulting strong user. To improve the user fairness, a new max–min–max AS (AIA-AS) scheme is subsequently developed, in which we jointly select one transmit antenna at BS and corresponding best receive antennas at users to maximize the channel gain of the resulting weak user. For the CR-NOMA scenario, we propose another new AS algorithm, termed maximum-channel-gain-based antenna selection (MCG-AS), to maximize the achievable rate of the secondary user, under the condition that the primary user's quality-of-service requirement is satisfied. The asymptotic closed-form expressions of the average sum-rate for A $^3$-AS and AIA-AS and that of the average rate of the secondary user for MCG-AS are derived. Numerical results demonstrate that the AIA-AS provides better user fairness, whereas the A$^3$-AS achieves a near-optimal sum-rate in F-NOMA systems. For the CR-NOMA scenario, MCG-AS achieves a near-optimal performance in a wide signal-to-noise-ratio regime. Furthermore, all the proposed AS algorithms yield a significant computational complexity reduction, compared to exhaustive search-based counterparts. [ABSTRACT FROM PUBLISHER]

In a two-way relay channel network (TWRC), the integration of channel coding into symmetric physical layer network coding (PNC) has been well studied, where both sources use exactly the same channel coding and modulation schemes and the relay decodes and reencodes the codewords obtained from the superimposed signals. How to integrate the channel coding into heterogeneous modulation PNC (HePNC), where the sources apply different modulations, is an open issue. In this paper, we propose a channel coded HePNC (CoHePNC) scheme under asymmetric TWRC. For repeat-accumulate (RA) codes applied at the sources, a full-state sum-product decoding algorithm is proposed which enables the relay to decode the superimposed signals from the sources to the raw decoding results firstly, and then re-encode and obtain the network-coded codewords by mapping the raw decoding results according to the proposed bit-level mapping functions. We further optimized the bit-level mapping functions according to the two source-relay channel conditions. Extensive simulation results demonstrated that the proposed CoHePNC outperforms the existing channel coded PNC schemes in terms of the relay decoding error rate and the end-to-end bit error rate under asymmetric TWRC scenarios. [ABSTRACT FROM AUTHOR]

Wideband spectrum sensing is a highly desirable feature in cognitive radio systems when the aim is to increase the probability of exploring spectral opportunities. Sub-Nyquist sampling has attracted significant interest for wideband spectrum sensing, while existing algorithms can only work with a sparse spectrum. In this paper, we propose a sub-Nyquist wideband spectrum sensing algorithm that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters (ADCs) based on sparse fast Fourier transform. To lower signal spectrum sparsity while maintaining the channel state information, we preprocess the received signal through a proposed permutation and filtering algorithm. The proposed wideband spectrum sensing algorithm subsamples the time-domain signal and then directly estimates its frequency spectrum. We derive and verify the proposed algorithm by numerical analysis and test it on real-world TV white space signals. The results show that the proposed algorithm achieves high detection performance on sparse and nonsparse wideband signals with reduced runtime and implementation complexity in comparison with the conventional wideband spectrum sensing algorithms. [ABSTRACT FROM PUBLISHER]

A two-step user selection algorithm for multiuser precoding based on the minimum-mean-square error criterion is proposed in this paper. The first step is the sequential user selection algorithm, where the focus is on minimizing the increase in the mean square error (MSE) when adding a new user to the service user set. During the first step, previous selection results can be used in the subsequent operation to reduce the computational complexity of the algorithm. In the second step, the user index regeneration algorithm is performed to further lower the MSE. Analysis and simulation results show that the proposed two-step user selection algorithm has better performance and lower complexity than the existing iterative algorithm. Compared with the decremental algorithm, the proposed algorithm has marginal performance loss with much lower computational complexity. The given advantages are also verified in a scenario with channel estimation errors. In a round-robin scheme, where all the available users are scheduled, the simulation result shows that the capacity of the proposed two-step algorithm is a little higher than that of the decremental algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, an adaptive beam tracking algorithm for interference alignment (IA) in time-varying multiple-input–multiple-output (MIMO) interference channels is presented. It is shown that obtaining a set of interference-aligning transmit beamforming matrices is equivalent to minimizing a certain Rayleigh quotient, and an approach based on the conjugate gradient (CG) method combined with metric projection is applied to this minimization problem to construct an adaptive algorithm for interference-aligning beam design. The convergence of the proposed algorithm in static channels is established, and the steady-state behavior of the proposed algorithm in time-varying channels is investigated by numerical simulations. The performance of the proposed algorithm is evaluated numerically, and numerical results show that the proposed algorithm performs well with low computational complexity. [ABSTRACT FROM PUBLISHER]