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This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

A two-user Gaussian broadcast channel (GBC) with finite input constellations is addressed in this paper. The constellation-constrained capacity region for the two-user GBC by recently proposed bit-division multiplexing (BDM) is derived. BDM can be seen as a practical implementation of constellation-constrained nonlinear superposition coding (SC). For comparison, the constellation-constrained capacity region for the two-user GBC by traditional constellation-constrained linear SC is also computed. With similar implementation complexity, BDM outperforms traditional constellation-constrained linear SC with lower shaping loss in the capacity defined in this paper. Analytical results show that the upper boundary of the capacity region for the two-user GBC can be well approached by BDM with amplitude phase-shift keying modulation, which could be considered as the preliminary demonstration on the applicability of BDM in practical systems. [ABSTRACT FROM PUBLISHER]

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]

Recently chaos theory has emerged as a powerful tool to address forecasting problems of nonlinear time series, since it is able to meet the dynamical and geometrical structures of very complex systems, reaching higher accuracy on the prediction values than the classical approaches. This paper aims at applying the chaos theory principles to different problems, in order to pursue high levels of accuracy on the predicted results. After the verification of the chaotic behavior of the datasets taken into analysis through the largest Lyapunov exponent research, the detection of the suitable embedding dimension and time delay has been carried out, in order to reconstruct the phase space of the underlying dynamical systems. Three different predictive methods have been proposed for different datasets. Finally, the performance comparison with the moving average model, a deep neural network based strategy, and a chaos theory based algorithm recently proposed in literature has been provided. [ABSTRACT FROM AUTHOR]

In this paper, we study the multiuser detection in a millimeter-wave (mm-wave) wireless communication system using hybrid beamformers, where uplink transmissions are considered under limited scattering. For a base station, with the condition that the number of radio-frequency (RF) chains is relatively smaller than that of users, we consider opportunistic beamforming with analog beams for multiple-signal detection and derive a closed form expression for system sum rate. By applying the minimum mean square error (MMSE)-filter with the proposed opportunistic hybrid beamforming algorithm, the system performance can be further improved. Through simulation results, it is shown that the proposed MMSE-based multiuser detection approaches are attractive for hybrid mm-wave systems to support a large number of users with a small number of RF chains. [ABSTRACT FROM AUTHOR]

In this paper, we investigate antenna selection (AS) methods for bidirectional (BD) full-duplex multiple-input–multiple-output (MIMO) systems in which antennas at each node can be selected to either transmit or receive. In this configuration, we first analyze the average sum rate of the optimal AS scheme that finds the best antenna set solution by examining all the possible candidates. The result provides insight into which transmit and receive antenna configuration improves the average sum-rate performance of the AS scheme. Then, we present a new AS algorithm that achieves near-optimal sum-rate performance with much reduced complexity compared with the optimal AS scheme. From simulation results, we verify that our sum-rate analysis for the optimal AS scheme matches the numerical results and confirm that, by employing the proposed AS algorithm, a performance gain of 15% is achieved. [ABSTRACT FROM PUBLISHER]

Spinal codes are a new family of rateless codes which have been proved to be capacity-achieving over both the additive white Gaussian noise (AWGN) channel and the binary symmetric channel (BSC). However, over the Rayleigh fading channel between the vehicle nodes, the error performance of Spinal codes is not satisfactory since the tail message blocks of Spinal codes are prone to error. To solve this issue, this paper proposes a new practical partial self-concatenation coding structure of Spinal codes, named $N$ tail-protected Spinal codes, to pointedly protect tail message blocks of Spinal codes over the flat Rayleigh fading channel. Theoretical performance analysis and simulation results are also provided to validate and verify the effectiveness of the proposed $N$ tail-protected Spinal codes. [ABSTRACT FROM AUTHOR]

Sphere decoding (SD) based detection schemes for sparse code multiple access (SCMA) systems have recently received attention due to their promising features. However, the existing SD-based schemes can only be applied to SCMA systems with constellations that possess a certain structure. In this paper, we propose a novel SD-based detection scheme, namely improved SD (ISD), for SCMA that achieves the optimal maximum likelihood detector for any arbitrary regular or irregular constellations. To overcome the rank deficiency problem of the SCMA channel matrix, we fix a portion of the transmitted symbols and obtain an optimal detection problem that is equivalent to the original SCMA detection problem for all types of constellations. Moreover, due to the sparse nature of SCMA, the partial metric at each layer is evaluated in such a way that is independent of users assigned to each resource element. This, in turn, reduces the average complexity of ISD. [ABSTRACT FROM AUTHOR]

High-speed and low-latency communications are one of the major requirements for connected vehicle environments. Safety messages in vehicular networks must arrive on time for each vehicle, and the optimization of driving paths also strongly depends on low-latency traffic information. To comply with the demands, a base station take important roles to provide a short service delay for the vehicles, even at the expense of performance. The relationship between delay time and decoding performance at receiver sides in physical layers can be explained by the performance/complexity tradeoff as follows. To achieve better performance in terms of the minimization of word error rates in decoding, the receiver usually requires higher complexity, which results in larger delays. We analyze this tradeoff based on the receiver queue model. This paper proposes a novel algorithm to adaptively select one of receiver candidates under the constraint of queue stability so as not to degrade low-latency communications. Based on the configurable architecture for multi-input multioutput (MIMO) detectors, this paper shows that the proposed algorithm works well on adaptive MIMO detector selection. [ABSTRACT FROM PUBLISHER]

Recently, vehicle-to-vehicle (V2V) based localization has attracted much attention due to its potential to achieve high accuracy. However, the error caused by non-line-of-sight (NLOS) propagation significantly affects the localization performance. In this paper, a novel method combining V2V communication and NLOS identification is proposed to improve accuracy of vehicle localization in NLOS scenarios. First, the algorithm identifies NLOS links with statistical methods. In this step, identification is conducted by decision theory or z-test, which depends on whether priori NLOS information is known or not. After that, NLOS links are discarded. Using the information including length of remaining links and GPS positions of surrounding vehicles, the estimated positions of target vehicles can be obtained by multilateration. It is shown by simulations that the proposed algorithm outperforms several classical methods in accuracy. Specifically, when priori NLOS information is available, 80% of vehicles have a localization error less than 5 m. For the case where NLOS information cannot be obtained, the algorithm still have good performance, and the ratio of vehicles having error within 5 m reaches 70%. [ABSTRACT FROM AUTHOR]

In the existing millimeter-wave (mmWave) wireless positioning systems, the method based on sparse Bayesian learning (SBL) uses the channel sparsity to estimate the parameters required for positioning, such as angle of arrival (AOA) and time delay, but most existing SBL solutions only consider the angle sparsity. In this paper, we consider the joint sparsity of the angle domain and time delay domain to propose an improved SBL algorithm by using a new two-dimensional adaptive grid refinement method in the SBL framework. This algorithm solves the grid mismatch problem caused by the fixed grid in the traditional SBL method, and reduces the algorithm complexity of the off-grid SBL (OGSBL) algorithm. We also obtain the Cramér-Rao bound (CRB) of AOA, time delay and position estimation based on the mmWave multipath signals to analyze the estimation errors. Simulation results show that the performance of the proposed algorithm is better than existing algorithms and can approach CRB. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the design and analysis of the Analog Fountain Code (AFC) for short packet communications. We first propose a density evolution (DE) based framework, which tracks the evolution of the probability density function of the messages exchanged between variable and check nodes of AFC in the belief propagation decoder. Using the proposed DE framework, we formulate an optimisation problem to find the optimal AFC code parameters, including the weight-set, which minimises the bit error rate at a given signal-to-noise ratio (SNR). Our results show the superiority of our AFC code design compared to existing designs of AFC in the literature and thus the validity of the proposed DE framework in the asymptotically long block length regime. We then focus on selecting the precoder to improve the performance of AFC at short block lengths. Simulation results show that lower precode rates obtain better realised rates over a wide SNR range for short information block lengths. We also discuss the complexity of the AFC decoder and propose a threshold-based decoder to reduce the complexity. [ABSTRACT FROM AUTHOR]

The localization system is one of the key components of autonomous vehicles. Widely-used GNSS (low-cost) alone can not meet the centimeter accuracy and is unstable in urban environment. Typically LiDAR-based localization system is very computationally demanding due to the usage of the dense point cloud map. In this paper, we propose a novel lightweight LiDAR-based localization system for autonomous vehicle in this paper. The proposed system only relies on lightweight poles and curbs landmark map, which is highly robust and efficient compared to other localization systems. Poles and curbs are selected as landmarks because of their commonality and stability. We novelly propose a Branch-and-Bound (BnB)-based global optimization method to tackle the data association problem of poles. Motion decoupling is adopted to decouple translation and rotation to improve the efficiency of the BnB-based algorithm. Besides, we propose a new local grid map-based representation for curbs to make better use of curb information. Cost functions for pole and curb are defined respectively and then fused for the subsequent non-linear optimization method to obtain the vehicle location. Experiments on KITTI dataset and our self-collected dataset demonstrate the efficiency and accuracy of our system. [ABSTRACT FROM AUTHOR]

In this paper, we propose low-complexity algorithms for downlink traffic scheduling in green vehicular roadside infrastructure. In multiple roadside unit (RSU) deployments, the energy provisioning of the RSUs may differ, and it is therefore desirable to balance RSU usage from a normalized min-max energy viewpoint. This paper considers both splittable RSU assignment (SRA) and unsplittable RSU asssignment (URA) scheduling. An offline integer linear programming bound is first derived for normalized min-max RSU energy usage. We then show that in the SRA case, there is a polynomial complexity 2-approximation bound for the normalized min-max energy schedule. This paper then proposes several online scheduling algorithms. The first is a greedy online algorithm that makes simple RSU selections, followed by minimum-energy time slot assignments. A normalized min-max algorithm is then proposed [2-approximation online algorithm (TOAA)], which is an online version of the 2-approximation bound. Two algorithms are then introduced based on a potential function scheduling approach. The 1-objective algorithm uses an objective based on normalized min-max energy, and we show that it has an upper bounded worst-case competitive ratio performance. The 2-objective algorithm uses the same approach but incorporates a total-energy secondary objective as well. Results from a variety of experiments show that the proposed scheduling algorithms perform well. In particular, we find that in the SRA case, the TOAA algorithm performs very close to the lower bound but at the expense of having to reassign time slots whenever a new vehicle arrives. In the URA case, our low-complexity 1-objective algorithm performs better than the others over a wide range of traffic conditions. [ABSTRACT FROM PUBLISHER]

$N$-continuous orthogonal frequency-division multiplexing (NC-OFDM) is a promising technique to achieve significant sidelobe suppression for baseband OFDM signals. However, NC-OFDM usually has high implementation complexity that limits its practical applications. Based on conventional NC-OFDM, this paper proposes a new technique, termed time-domain $N$-continuous OFDM (TD-NC-OFDM), which shifts the processing burden from the frequency domain to the time domain. This is achieved by adding a smooth signal, which is linearly combined by rectangularly pulsed OFDM basis signals consisting of a novel basis set. We prove that TD-NC-OFDM with the basis set is essentially equivalent to conventional NC-OFDM. Furthermore, using the time-domain structure, an asymptotic spectrum analysis of $N$-continuous OFDM signals is obtained by developing a closed-form expression related to sidelobe decaying. This paper also examines the impact of TD-NC-OFDM on the received signal-to-interference-plus-noise ratio (SINR) and derives closed-form expressions. [ABSTRACT FROM PUBLISHER]

It is widely recognized that traditional single radio frequency (RF)-chain-aided spatial modulation (SM) does not offer any transmit diversity gain. As a remedy, constellation randomization (CR), relying on transmit prescaling (TPS), has been shown to provide transmit diversity for single-RF-chain-aided SM. In this paper, we propose a low-complexity approach to SM with the aid of constellation randomization (SM-CR) that considerably improves the transmit diversity gain of SM at a reduced computational burden compared with conventional SM-CR. While conventional SM-CR performs a full search among a set of candidate TPS factors to achieve the maximum minimum Euclidean distance (MED) in the received SM constellation, here, we propose a thresholding approach, where, instead of the maximum MED, the TPS aims to satisfy a specific MED threshold. This technique offers a significant complexity reduction with respect to the full maximization of SM-CR, since the search for TPS is terminated once a TPS set is found that satisfies the MED threshold. Our analysis and results demonstrate that a scalable tradeoff can be achieved between transmit diversity and complexity by appropriately selecting the MED threshold, where a significant complexity reduction is attained, while achieving a beneficial transmit diversity gain for the single-RF SM. [ABSTRACT FROM PUBLISHER]

Sparse signatures have been proposed for the code-division multiple-access (CDMA) uplink to reduce multiuser detection complexity, but they have not yet been fully exploited for its downlink counterpart. In this paper, we propose multicarrier CDMA (MC-CDMA) downlink communication whereby regular sparse signatures are deployed in the frequency domain. Taking the symbol detection point of view, we formulate a problem appropriate for the downlink with discrete alphabets as inputs. The solution to the problem provides a power-efficient precoding algorithm for the base station (BS), subject to minimum symbol error probability (SEP) requirements at the mobile stations (MSs). In the algorithm, signature sparsity is shown to be crucial for reducing precoding complexity. Numerical results confirm system-load-dependent power reduction gain from the proposed precoding over the zero-forcing (ZF) precoding and the regularized ZF (RZF) precoding with optimized regularization parameter under the same SEP targets. For a fixed system load, it is also demonstrated that sparse MC-CDMA with a proper choice of sparsity level attains almost the same power efficiency and link throughput as that of dense MC-CDMA yet with reduced precoding complexity due to the sparse signatures. [ABSTRACT FROM PUBLISHER]

Multiple-input–multiple-output (MIMO) technology can efficiently use the spectrum to increase the communication throughput. Designing low-complexity detection algorithms with high performance for the MIMO system has been an important issue. In this paper, we propose efficient detection algorithms for MIMO systems based on differential metrics. We first define differential metrics and give their recursive calculation of different orders. Based on differential metrics, we give the principle of gradient search. We then propose a gradient search algorithm (GSA) that can provide a tradeoff between performance and complexity. The GSA applies the indicative functions such that we can determine in advance some maximum-likelihood (ML) bits of the initial sequence and reduce the searching range. The GSA also uses a stop condition with which we can stop the search if the proper condition is satisfied. The GSA does not need QR decomposition (QRD) or matrix inversion. The multiplicative operations are only necessary before the searching process, during which only the additive operations are needed. For large-scaled MIMO systems, we also give a simple searching algorithm based on differential metrics. Finally, we propose a fixed-complexity gradient algorithm (FCGA), which has a fixed number of operations during the searching process and is appropriate for pipelined hardware implementation. The simulation results validate the efficiency of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

To efficiently trade off system sum rate and link fairness, power allocation in wireless spectrum-sharing networks often concentrates upon proportional fairness. The corresponding problem has been proved to be convex for a single tone but NP-hard for more than two tones. However, in the two-tone case, the complexity of the problem for achieving proportional fairness has not been addressed yet. In this paper, we prove that the issue of proportional-fairness optimization for the two-tone situation is NP-hard by reducing the problem of finding the maximum independent set in an undirected graph to it. Moreover, a computationally efficient algorithm is proposed to provide an efficient suboptimal solution. Simulation results are presented to illustrate the effectiveness of our proposal. [ABSTRACT FROM PUBLISHER]

Multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) receivers with an electronically steerable passive array radiator (ESPAR) antenna can improve bit error rate (BER) performance and obtain additional diversity gain without increasing the number of radio-frequency (RF) front-end circuits. In this scheme, a minimum-mean-square-error (MMSE) channel estimator is employed for obtaining the channel state information. However, its drawbacks include insufficient estimation accuracy and high computational complexity due to the inverse matrix calculation. In this paper, we propose using a low-complexity compressed-sensing-based channel estimation for the MIMO-OFDM receiver with an ESPAR antenna. We also propose a low-complexity detection scheme to further reduce the computational cost required for the detection. The simulation results show that with using the proposed channel estimation method, the BER performance is improved, while its complexity is reduced. The simulations also show that the proposed detection scheme successfully reduces the required computational cost while minimizing the degradation in the BER. [ABSTRACT FROM PUBLISHER]

Large-scale generalized spatial modulation (GSM) technology has great potential in constructing low-complexity massive multiple-input multiple-output wireless systems, with a simplified structure which employs only a few radio frequency (RF) chains. However, detection is a very complex problem for large-scale GSM systems. In this correspondence paper, we have proposed an innovative probability sorting based approximate message passing (PS-AMP) detector. Specifically, in the proposed scheme, an approximate message passing method is used to obtain the probability for activation of each transmit antenna, and a method based upon probability sorting is developed to ascertain the signal search space. Simulation results demonstrate that the proposed PS-AMP detector exhibits a better bit error rate performance with a reduced complexity compared to its conventional counterparts. [ABSTRACT FROM AUTHOR]

In order to support the development of 5G technologies, researchers are actively engaged in addressing the challenges accompanying the emerging 5G applications. Unquestionably, an eminent technology gaining significant research attention is edge computing. Vehicular edge computing brings data storage and computing capabilities as well as hosting support applications that comprise emerging vehicular services and applications which demand low-delay processing, to the edge closer to the vehicles, reducing response time and increasing reliability, therefore achieving the holistic vision of the tactile Internet. In this context, this paper considers a vehicular network with edge computing capabilities deployed at road side units, and addresses the problem of workload offloading as well as scheduling of computation tasks on the computing resources available at the edge. The challenge here is the high mobility of the vehicles and hence their short residence time within the coverage range of the road side units hosting the edge computing resources. A joint problem considering the communication and computation resources, as well as the latency requirements of the workload is formulated and the scheduling is shown to be NP-Hard. Subsequently, efficient solutions based on Lagrangian relaxation are derived and presented. We evaluate numerically the proposed methods and show their closeness to the optimal solutions. [ABSTRACT FROM AUTHOR]

In future 5G user-centric ultra-dense networks (UUDN), demands of high data rate and high spectrum efficiency are effectively met by dual connectivity (DC) technology. However, due to huge increase of base stations (BSs) and mobile users (MUs), it becomes difficult for BSs to quickly and precisely select the codeword and provide DC to MUs. Hence, different from some traditional methods, this correspondence paper aims to improve the network performance using the method of machine learning. First, we model the random distribution of BSs by homogeneous Poisson point processes, where each MU is served by millimeter-wave channel. Second, the probabilities that macro cell BS or small cell BS serves the MU are further derived to get the average sum rate (ASR) in UUDN. Third, inspired by ML, we utilize an iterative support vector machine (SVM) classifier to select the codewords of BSs, with sequential minimal optimization (SMO) algorithm used for training all link samples in UUDN. Then, an iterative SVM-SMO classification algorithm is proposed to achieve a highly efficient performance of DC, where the convergence and complexity are also discussed. The sample training and simulation at last are evaluated by Google TensorFlow. The simulation verified that our proposed algorithm gets a higher ASR than the traditional channel estimation based algorithm. In addition, the results also show a lower computational complexity can be achieved by the proposed algorithm as well. [ABSTRACT FROM AUTHOR]

This paper considers the bit loading problem for communication systems that utilize orthogonal frequency division multiplexing (OFDM) in conjunction with precoding (POFDM) or time-domain interleaving (IOFDM). In particular, we propose a new bit loading algorithm for P/I-OFDM that has substantially higher effective throughput and less computational complexity, when compared to bit loading in conventional OFDM systems. The obtained results show that the effective throughput of P/I-OFDM can be more than fourfold the conventional OFDM while the complexity is less than $1.5\%$. Moreover, the results show that the peak-to-average power ratio (PAPR) properties of the considered systems are preserved under the adaptation process. [ABSTRACT FROM AUTHOR]

Being the first codes achieving the capacity of symmetric binary-input discrete memoryless channels, polar codes have been favorable and standardized for enhanced mobile broadband (eMBB) control channels. In future mobile communications, the entire system is required to be flexible and adaptive to varying scenarios. Therefore, efficient software implementation of polar decoder is expected. Existing software successive cancellation list polar decoder can achieve good performance but with a good memory consumption. For short control channel codes, a more memory-efficient alternative is sphere decoder, but its latency gives rise to another issue. Equipped with a breadth-first search to keep a fixed complexity, list sphere decoder (LSD) is also considered. To improve the performance of LSD, a software LSD with synchronous determination is proposed in this paper. We precalculate the synchro sets to improve the decoding, which leads to a performance gain. Meanwhile, the decoding process is simplified due to the precalculation, which attains even lower complexity. Implemented with C++, the proposed decoder can achieve up to 2 dB performance gain compared with the existing LSD. [ABSTRACT FROM AUTHOR]

In this paper, we propose a compressive random access (CRA) scheme using multiple resource blocks (RBs) to support massive connections for machine type communications. The proposed CRA scheme is scalable. As a result, if the number of devices increases, more RBs can be added to support them. Thanks to multiple RBs, we can employ fast retrial between RBs for re-transmissions of collided packets, which can result in short access delay. For stable CRA with fast retrial, we derive conditions (with a rate control scheme), and analyze the steady-state performance to find the throughput and delay. Through analysis and simulation results, we can see that the proposed scheme can perform better than conventional multichannel ALOHA and enjoy a tradeoff between the performance and complexity in terms of the number of RBs. [ABSTRACT FROM AUTHOR]

Faster-than-Nyquist (FTN) signaling is regarded as a potential modulation format of 5G communication system for its high spectral efficiency. To further improve the spectral efficiency, researchers have extended the FTN signaling to the frequency dimension. However, the two-dimensional FTN signaling called multicarrier FTN (MFTN) signaling induces intersymbol interference and intercarrier interference (ICI), which challenges the receiver. Even the subcarriers are orthogonal, the MFTN signaling still induces the ICI. In this paper, we propose a new MFTN signaling transceiver with an FTN mapper and a linear minimum mean square error (LMMSE) equalizer. The FTN mapper is deployed on every subcarriers independently, which means that the MFTN is ICI-free. The proposed transceiver is compatible with the existing orthogonal frequency division multiplexing (OFDM) system and can detect the MFTN signal efficiently. The numerical results show that the bit error rate performance of the transceiver is close to the convolution code line. At the same time, the MFTN system with high-order modulation has power gains than the traditional OFDM system. [ABSTRACT FROM AUTHOR]

This paper analyzes the performance of generalized multirelay cooperation (GMRC) protocols, which generalize the conventional single- and multiple-relay cooperative protocols, by allowing the number of relays employed in a cooperative cycle to vary with the instantaneous channel condition. Two variants of GMRC protocols are studied, including the output-detection GMRC (OD-GMRC) based on a decoding result and the output-threshold-test GMRC (OT-GMRC) relying on a threshold test. For both protocols, we derive the average outage probability and the average delay for completing a cooperative cycle, assuming uncoded decode-and-forward (DF) relays with multiple receiving antennas. Through numerical results, we validate the analysis accuracy and identify the ideal operational mode of GMRC under different network configurations. [ABSTRACT FROM PUBLISHER]

In this paper, we study the beam synthesis and management methods for the phased antenna array-based wireless communications systems, e.g., 5 G mmWave communications. First, we propose a novel on-off beam synthesis method that can synthesize any given on-off beam pattern. The proposed method maximizes the beam power towards a given set of on-directions while suppressing the power towards all off-directions. Second, by using a set of beams synthesized by the proposed on-off beam synthesis method, we design a non-interactive hierarchical beam management algorithm that can greatly reduce the radio resource usage, feedback overhead, and the latency of the beam discovery, compared to the exhaustive beam search algorithms. Since the proposed beam management algorithm is non-interactive, it can be applied to the initial access as well as the beam tracking, and is especially useful for supporting a massive number of devices. Finally, we show the validness of the proposed beam synthesis and management methods by conducting experiments on a phased antenna array experimental testbed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel receiver scheme for multiple-input–multiple-output (MIMO) single-carrier frequency-division multiple access (SC-FDMA) systems. Various optimal and near-optimalMIMO detection techniques developed for flat-fading channels are not readily applied to MIMO SC-FDMA systems due to the large dimensions of the effective channels. Recently, the system equation of single-input–single-output (SISO) SC-FDMA systems was divided into a number of disjoint subsystems with moderate dimensions, and a previous MIMO detection technique was applied to the subsystems. In this paper, instead of naively extending the previous scheme for SISO SC-FDMA to MIMO SC-FDMA systems, we first express the large system equation of MIMO SC-FDMA, such that the effective channels are block circulant for an arbitrary number of transmit and receive antennas. The block circulant channel structure is then exploited to lower the computational complexity of projections to construct nonoverlapping subsystems. The proposed circulant projection also takes advantage of the sparse channel structure, offering higher postprojection signal-to-noise ratios (SNRs) of the resulting subsystems than those of a naive projection that does not exploit the channel structure. The simulations confirm the desirable performance of the proposed scheme when a relatively small number of subcarriers are used. The proposed technique is also compared with the previous iterative block decision feedback equalization (IB-DFE) and minimum-mean-square-error–prewhitened-maximum-likelihood (MMSE-prewhitened-ML) detection techniques. [ABSTRACT FROM PUBLISHER]

In this paper, the downlink of a clustered coordinated multicell multiuser multiple-input multiple-output (MIMO) system is considered, and various low-complexity user scheduling algorithms to maximize the sum rate are proposed. The users' data signals are transmitted cooperatively via the base stations (BSs) of a cluster within the sectorized multicell heterogeneous cellular network. Exhaustive search as the optimal scheduling method is extremely complex, particularly for large numbers of users requesting service. We therefore propose suboptimal scheduling algorithms based on the lower complexity metaheuristic stochastic optimization techniques of simulated annealing (SA) and particle swarm (PS). Moreover, a hybrid algorithm combining the traits of PS and a greedy scheduler is also proposed. The performance and complexity of these algorithms are evaluated for a heterogeneous system model employing different precoding methods. Assessing the algorithms' performance via computer simulations, the effectiveness of the proposed schemes in terms of sum rate is demonstrated, yielding performance very close to that of the exhaustive search at much lower complexity. For each precoding method used in the employed system model, the best corresponding approach to scheduling from our proposed algorithms is determined. [ABSTRACT FROM AUTHOR]

In practical applications, an imperfect channel estimate over a noisy channel is inevitable. This paper investigates an interference-aware receiver in which colored-estimation-noise-based maximum-likelihood (CEN-ML) detection is employed to mitigate the impact of imperfect channel estimates. It is shown that the CEN-ML detection, which treats the channel estimation errors as colored noise, has superior performance and is robust to imperfect channel estimation. However, a CEN-ML interference-aware detector usually requires computational efforts that are too complex for multiple-input–multiple-output (MIMO) interference-limited systems. To reduce the complexity, this paper proposes new methods for both the preprocessing and tree-search stages of an interference-aware sphere detector (SD). First, a channel extended matrix is constructed using the information from channel estimation errors in a preprocessing stage. The channel extension model can reduce the complexity without performance loss. Second, two tree pruning (TP) criteria are developed in a tree-search stage to make the implementation of the CEN-ML SD feasible. The first criterion is based on a lower bound of the CEN-ML metric and can achieve the same result as the optimal brute-force method. The second criterion is derived from an approximated metric and can further reduce the complexity with small performance loss. Simulation results for typical Third-Generation Partnership Project Long-Term Evolution (LTE)/LTE-Advanced scenarios show that the new methods provide the ability to deal with the channel estimation errors and to achieve a better tradeoff between complexity and performance (e.g., in terms of throughput or error rate) in interference-limited scenarios. The proposed low-complexity CEN-ML SD can have a gain of more than 10 dB than the other investigated detection for 16-ary quadrature amplitude modulation (QAM) or 64-QAM at a target coded block error rate (BLER) of \mbox10^-2. [ABSTRACT FROM AUTHOR]

The ultra dense deployment of small cells is considered a key technology to achieve the requested capacity in future cellular networks. However, the interference pattern becomes more unpredictable and challenging in these networks. Therefore, a suitable trade-off between spectrum spatial reuse and interference level has to be pursued to achieve good performance in terms of provided throughput. This paper proposes a new method to maximize the achievable throughput of an UDN with a suitable level of spatial spectrum reuse. In particular, the focus is on the small cells tier where the available spectrum is divided into sub-bands and each cell can use some of these to communicate with its associated users. The goal is to find the sub-bands allocation among cells that maximizes the system throughput. However, to limit complexity and signaling overhead that could result unaffordable in an UDN, a new metric to approximate the cell throughput to be optimized is defined. Moreover, the newly defined problem is solved using the Gibbs Sampling approach. The method effectiveness is proven by comparing the achieved results with those of the maximization of the effective system throughput and the optimal solution. [ABSTRACT FROM AUTHOR]

Code division multiplexing (CDM)-based random access, wherein a set of sequences is reserved for random access and a remote station transmits a random access packet using a sequence randomly selected from this set, is employed in numerous practical wireless systems. If multiple remote stations simultaneously transmit random access packets using the same sequence, the performance degrades owing to sequence collision. In addition, the performance is degraded because of interference if multiple remote stations simultaneously transmit random access packets using different sequences. Therefore, the performance of CDM-based random access is dependent on both sequence collision and interference. There have been no previous studies on analyzing the performance of CDM-based random access considering both sequence collision and interference. In this paper, the throughput of CDM-based random access is investigated considering both sequence collision and interference based on a signal-to-interference-plus-noise ratio (SINR) capture model. Analyses and numerical simulations are performed to compare the throughputs of several random access schemes, including conventional and channel-adaptive random access. The results show that channel-adaptive random access can achieve significantly higher throughput than that achieved by conventional random access. Moreover, the results demonstrate that it is possible to analyze the trade-off between the throughput and implementation complexity with the increased the number of sequences. [ABSTRACT FROM AUTHOR]

This paper investigates subchannel allocation based on clustered interference alignment in dense small cell networks when all small cell user equipments (SUEs) have differentiated requirements for data streams. By imposing the condition that each cluster has a size not exceeding the maximum value achieved when each SUE needs only one data stream, we maximize the number of SUEs with guaranteed requirements for data streams, which is NP-hard. Hence, we propose a two-phase efficient solution with much lower complexity and reduced feedback overhead. First, similarity clustering is performed by graph partition, and then, subchannel allocation is done through a coloring algorithm. Numerical results show that the proposed solution offers a performance better than the related schemes and close to the approximate optimal solution. [ABSTRACT FROM AUTHOR]

The millimeter wave (mmWave) communication has drawn intensive attention with abundant band resources. In this paper, we consider the popular content distribution (PCD) problem in the mmWave vehicular network. In order to offload the communication burden of base stations (BSs), vehicle-to-vehicle (V2V) communication is introduced into the PCD problem to transmit contents between on-board units (OBUs), and improve the transmission efficiency. We propose a full-duplex (FD) cooperative scheme based on coalition formation game, and the utility function is provided based on the maximization of the number of received contents. The contribution of each member in the coalition can be transferable to its individual profit. While maximizing the number of received contents in the fixed time, the cooperative scheme also ensures the individual profit of each OBU in the coalition. We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks. Compared with other existing schemes, the proposed scheme has superior performances on the number of possessed contents, and system fairness. Besides, the low complexity of the proposed algorithm is demonstrated by the switch operation number, and CPU time. [ABSTRACT FROM AUTHOR]

Differential amplitude phase shift keying (DAPSK) is a promising modulation technique for use in high-mobility environments, as it does not require channel estimation. In this paper, a novel reduced complexity soft demodulator for high-order DAPSK constellations is proposed. The main idea relies on a two-step approximation of the sum-log demapper. In the first step, a parallel factor analysis (PARAFAC) decomposition is used to approximate log-likelihood ratios. Next, the basis functions obtained through the first step are approximated by piecewise-linear curves. Numerical optimization techniques are employed for the PARAFAC decomposition and its piecewise-linear approximation whose parameters are then stored in a lookup table. It has been verified through computer simulations that the proposed scheme clearly outperforms the scheme proposed in the paper “Reduced-complexity noncoherent soft-decision-aided DAPSK dispensing with channel estimation,” (IEEE Trans. Veh. Technol., vol. 62, pp. 2633–2643, 2013.), while also having lower computational cost. Moreover, it is shown that the performance of the exact sum-log demapper and the proposed demapper is very close for \text2\times \text4 and \text4\times \text16 DAPSK constellations. [ABSTRACT FROM AUTHOR]

Vector perturbation (VP) is a nonlinear precoding technique that achieves near-capacity performance in multiuser multiple-input multiple-output systems at the expense of large complexity due to the search for the optimum perturbation vector. In this paper, we present the hybrid Tomlinson–Harashima VP (TH-VP) algorithm, a novel zero-forcing precoding scheme, which combines TH precoding to remove interuser interference, and VP precoding to equalize each user's multiple spatial streams. We show that the two nonlinear techniques can be integrated in a single optimization and that the proposed algorithm has lower computational requirements than any other. The performance of TH-VP is analyzed and simulation results show that TH-VP outperforms conventional zero-forcing VP and approaches the performance of dirty paper coding. [ABSTRACT FROM AUTHOR]

This paper considers multicell scheduling for coordinated multiple-input multiple-output cellular networks. In a multicell scenario, intercell interference, which is the major challenge degrading system performance, can be suppressed via multicell coordination. However, when the number of users is larger than that can be served, user/multimode selection is necessary, and can significantly enhance the performance in such interference-limited environment. Nevertheless, user selection in a multicell scenario incurs high computational complexity and overhead signalling. To this end, in this paper, low complexity greedy algorithms are proposed for user selection. The algorithms are based on the concept of angle between subspaces and intersection of null spaces, and work in partially distributed fashion with limited information exchange. By these algorithms, channel feedback can be greatly reduced with reasonable computational complexity. In addition, multimode scheduling schemes are proposed for this multicell set up, where the spatial streams are adaptively selected. The proposed algorithms are analysed and compared with the optimal solution obtained by exhaustive search. Simulation results show that the algorithms achieve most of the throughput achievable by the optimal solution. [ABSTRACT FROM AUTHOR]

A wideband transmitarray antenna based on the magnetoelectric (ME) dipole element and 2-bit phase compensation scheme is presented for vehicular communications in this paper. A wideband 90° phase shifter is proposed and combined with the rotating method to achieve four discrete phase states. Thanks to the polarization-twisting technique and the simple structure of the phase shifter, the proposed element can be easily designed. For the transmitarray design, a multi-frequency phase compensation strategy is applied to guarantee stable performance over the operating band. To validate the design, a 12 × 12 transmitarray antenna simultaneously covering vehicle-to-satellite band (20 GHz), and 5G millimeter-wave (mmWave) bands (24 GHz/28 GHz) is designed, fabricated and measured. The experimental results show that the prototype can achieve high aperture efficiencies (>40%) within a wide bandwidth (34%). The measured peak aperture efficiency is up to 53% at 23 GHz. These results demonstrate that the proposed 2-bit transmitarray antenna enjoys the advantages of wide operating bandwidth, high aperture efficiency and comparatively low design complexity. With these characteristics, the proposed transmitarray antenna can be a promising candidate for vehicular communications that require reliable satellite links and 5G millimeter-wave connections simultaneously. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the anti-jamming problem of a directional modulation (DM) system with the aid of intelligent reflecting surface (IRS). As an efficient tool to combat malicious jamming, receive beamforming (RBF) is usually designed by using the statistical properties of the jamming received by Bob. Thus, it is very necessary to estimate the receive jamming covariance matrix (JCM) at Bob. To achieve a precise JCM estimation, three JCM estimation methods, including eigenvalue decomposition (EVD), parametric estimation method by gradient descend (PEM-GD) and parametric estimation method by alternating optimization (PEM-AO), are proposed. Here, the proposed EVD is derived according to the rank-2 constraint of JCM. The PEM-GD method fully explores the structure features of JCM and the PEM-AO is proposed to decrease the computational complexity of the former via dimensionality reduction. The simulation results show that the proposed three methods perform better than directly using sample covariance matrix. Additionally, the proposed PEM-GD and PEM-AO outperform EVD method and the clutter and disturbance covariance estimator RCML. [ABSTRACT FROM AUTHOR]

Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception system. The requirements of a frontal environment perception system cannot be satisfied by either of the existing automotive sensors. A commonly used sensor cluster for these functions consists of a mono-vision smart camera and automotive radar. The sensor fusion is intended to combine the data of these sensors to perform a robust environment perception. Multi-object tracking algorithms have a suitable software architecture for sensor data fusion. Several multi-object tracking algorithms, such as JPDAF or MHT, have good tracking performance; however, the computational requirements of these algorithms are significant according to their combinatorial complexity. The GM-PHD filter is a straightforward algorithm with favorable runtime characteristics that can track an unknown and time-varying number of objects. However, the conventional GM-PHD filter has a poor performance in object cardinality estimation. This paper proposes a method that extends the GM-PHD filter with an object birth model that relies on the sensor detections and a robust object extraction module, including Bayesian estimation of objects’ existence probability to compensate for drawbacks of the conventional algorithm. [ABSTRACT FROM AUTHOR]

In recent years, the growing development of Connected Autonomous Vehicles (CAV), Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent of Autonomous Intersection Management (AIM) systems. AIMs present a new paradigm for CAV control in future cities, taking control of CAVs in scenarios where cooperation is necessary and allowing safe and efficient traffic flows, eliminating traffic signals. So far, the development of AIM algorithms has been based on basic control algorithms, without the ability to adapt or keep learning new situations. To solve this, in this paper we present a new advanced AIM approach based on end-to-end Multi-Agent Deep Reinforcement Learning (MADRL) and trained using Curriculum through Self-Play, called advanced Reinforced AIM (adv.RAIM). adv.RAIM enables the control of CAVs at intersections in a collaborative way, autonomously learning complex real-life traffic dynamics. In addition, adv.RAIM provides a new way to build smarter AIMs capable of proactively controlling CAVs in other highly complex scenarios. Results show remarkable improvements when compared to traffic light control techniques (reducing travel time by 59% or reducing time lost due to congestion by 95%), as well as outperforming other recently proposed AIMs (reducing waiting time by 56%), highlighting the advantages of using MADRL. [ABSTRACT FROM AUTHOR]

The internet of vehicles needs mobile communication systems for efficient communications. On the physical downlink control channel (PDCCH) of 5G, blind detection is performed to identify the downlink control information (DCI). Due to the uncertainty of DCI formats and PDCCH formats, the existing two-stage blind detection schemes separately decode every PDCCH code block under different DCI formats, leading to a high decoding complexity in the first stage. To reduce the detection complexity, this paper proposes an efficient DCI detection method, where a significance-test strategy is designed to detect the DCI formats. The significance-test strategy can forecast the DCI lengths without separately decoding the whole codeword, thus reducing the decoding complexity by several times. Moreover, the accuracy of the DCI detection is analyzed, with which the test scope of the test statistic is optimized for minimizing the decoding complexity on condition of meeting the detection accuracy requirements. Simulation results show that the proposed method significantly reduces the decoding complexity relatively to the existing two-stage detection schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose one joint interference alignment and subchannel allocation (JIARA) interference management scheme, aiming at maximizing the number of satisfactory users with different degrees of freedom (DoF) demand in ultra-dense wireless networks, while the existing works only consider the same DoF demands. The original JIARA scheme is equivalent to the maximum $K$ -colorable subgraph (MKCS) problem in graph theory for a given $K$ -colors and the multicoloring demand at each vertex, hence NP-hard. Therefore, we propose one graph theory based low complexity sub-optimal JIARA scheme. Firstly, we propose one virtual allocation of subchannel based scheme to construct transformed the conflict graph, which is applicable for the different DoF demands and different size of the feasible IA group. Furthermore, we propose one greedy multi-coloring based algorithm to determine the maximum number of satisfactory users. Simulation results show that the proposed JIARA scheme outperforms the subchannel resource allocation only scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a framework of the mutual information-maximizing (MIM) quantized decoding for low-density parity-check (LDPC) codes by using simple mappings and fixed-point additions. Our decoding method is generic in the sense that it can be applied to LDPC codes with arbitrary degree distributions, and can be implemented based on either the belief propagation (BP) algorithm or the min-sum (MS) algorithm. In particular, we propose the MIM density evolution (MIM-DE) to construct the lookup tables (LUTs) for the node updates. The computational complexity and memory requirements are discussed and compared to the LUT decoder variants. For applications with low-latency requirement, we consider the layered schedule to accelerate the convergence speed of decoding quasi-cyclic LDPC codes. In particular, we develop the layered MIM-DE to design the LUTs based on the MS algorithm, leading to the MIM layered quantized MS (MIM-LQMS) decoder. An optimization method is further introduced to reduce the memory requirement for storing the LUTs. Simulation results show that the MIM quantized decoders outperform the state-of-the-art LUT decoders in the waterfall region with both 3-bit and 4-bit precision over the additive white Gaussian noise channels. For small decoding iterations, the MIM quantized decoders also achieve a faster convergence speed compared to the benchmarks. Moreover, the 4-bit MIM-LQMS decoder can approach the error performance of the floating-point layered BP decoder within 0.3 dB in the moderate-to-high SNR regions, over both the AWGN channels and the fast fading channels. [ABSTRACT FROM AUTHOR]

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]

Device-to-device (D2D) communications underlaying cellular networks have been recently considered as a promising means to enhance resource utilization of the cellular network and local user throughput among devices in proximity to each other. In this paper, we investigate the joint resource block assignment and transmit power allocation problem to optimize the network performance in such a scenario. Specifically, we model the interference relationships among different D2D and cellular communication links as a novel interference graph with unique attributes and propose a corresponding joint resource-allocation scheme that can effectively lead to a near-optimal solution at the base station, with low computational complexity. Simulation results confirm that, with markedly reduced complexity, our proposed scheme achieves a network throughput that approaches the one corresponding to the optimal resource-sharing scheme obtained via exhaustive search. [ABSTRACT FROM PUBLISHER]

This paper develops a spectrum sensing technique for interleaved single-carrier frequency-division multiple access (SC-FDMA) systems. By designing a metric that exploits cyclostationary features of interleaved SC-FDMA signals, we establish a framework for signal detection. Using Gaussian approximation for this metric, the parameters of the metric distributions under two hypotheses are derived, and both hypotheses are examined by the Neyman–Pearson test. We validate the accuracy of the Gaussian approximation by comparing theoretical and simulated metric histograms. The performance of the proposed method is presented for additive white Gaussian noise and multipath Rayleigh fading channels. We also investigate the effect of the block length, the number of users, the metric window length, and the presence of the pilot signals on the detection performance. Through comparative performance evaluation, we demonstrate the superiority of our proposed detection scheme over energy detection and the detection method based on autocorrelation of the cyclic prefix (CP). We obtain similar detection performance to that of the mentioned methods at about 8–13 dB lower signal-to-noise ratio (SNR). It is noteworthy that the complexity of our method is comparable to that of the energy detection and slightly higher than that of CP detection. [ABSTRACT FROM PUBLISHER]

As the first class of constructive channel codes achieving the symmetric capacity of the binary-input discrete memoryless channels, polar codes have been extended to the fading channel. But most constructions of polar codes in fading channel are only suitable for the Rayleigh fading and involve complex iterative-calculation. In this paper, we establish a systematic framework in term of the polar spectrum to analyze and construct polar codes in various fast fading channels, such as Rician, Rayleigh and Nakagami channels. In these three fading channels, by using the polar spectrum, we derive the upper bound of the error probability of the polarized channel and that of block error rate of polar codes. The analysis based on the polar spectrum explicitly reveals the relationship between the diversity order and the codeword weight. Furthermore, we propose two construction metrics, named logarithmic upper-bound weight (LUW) and minimum-weight LUW (MLUW) respectively, to design the polar codes in fast fading channels. These two constructions are simple with a linear complexity and explicit for the practical implementation. In addition, for various fading channels, they can construct polar codes with similar or better performance over those based on traditional methods. [ABSTRACT FROM AUTHOR]