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Multiple-input-multiple-output (MIMO) systems are of interest for meeting the expected demand for higher data rates and lower delays in future wireless packet data systems. In such systems, it is optimal to simultaneously transmit to multiple users compared with a single user in a single-input-single-output system. In addition, multicarrier systems are of interest to combat frequency-selective fading that is experienced over the larger bandwidth that these future broadband systems will use. The use of dirty paper coding further complicates the matter, because the order in which the users are encoded will affect the rates that they can achieve. A well-designed cross-layer scheduling algorithm must take into account the multiple dimensions of this resource-allocation problem and other quality-of-service (QoS) parameters to fully exploit the communications channel. The scheduling problem is often expressed in terms of optimizing some utility function. Unfortunately, the search space for this optimization problem is extremely large, which prohibits an optimal exhaustive search. To this end, we investigate the use of genetic algorithms to reduce the complexity of the scheduling. This paper builds upon prior work that implements scheduling via genetic algorithms in the context of single-carrier systems using zero-forcing beamforming (ZFB). In this paper, we investigate how the genetic algorithm can be adapted to account for the effect of encoding order on the scheduling and how the scheduling can be extended to a multicarrier system. In particular, we investigate the maximum throughput and proportionally fair scheduling criteria. We demonstrate that the performance of the genetic algorithm is near optimal compared with an exhaustive search at a greatly reduced computational complexity. Furthermore, in the case of a multicarrier orthogonal frequency-division multiplexing (OFDM) system, an increase in capacity is shown relative to the single-carrier case. [ABSTRACT FROM AUTHOR]

In traditional spectrum sensing, binary hypothesis testing has been used to detect whether a frequency band is being occupied by the primary user or not. In this correspondence paper, we investigate $M$ -ary hypothesis testing for spectrum sensing to further identify the signal type of the primary user. Linear cooperation among spatially distributed cognitive radios is applied to combine the observation statistics and make a final decision. The problem of data fusion is formulated as minimizing the total probability of misclassification subject to the constraint on the probability of successful classification. To deal with the non-convex problem formulated, we develop two solutions in this paper. In the first solution, we transform the problem into an unconstrained one, and adopt a zooming-based search algorithm to iteratively update multiple continuous variables until convergence. This solution requires searching all possible combinations of variables. To reduce the computation complexity and achieve closed-form analytical expressions, in the second solution, we decompose the original problem into multiple subproblems, each with the objective of minimizing an individual probability of misclassification. These subproblems can lead to closed-form expressions, and the weights are chosen as the best ones that correspond to the minimum total probability of misclassification. The proposed solutions are examined numerically, and the results show that the decomposition-based solution can achieve performance comparable to the zooming-based one but with much less complexity. [ABSTRACT FROM AUTHOR]

The growing interest in concepts such as smart cities and smart mobility is giving more and more importance to place of interest (POI) information, which proves to be crucial in providing efficient and tailored location-based services (LBSs). Though plenty of solutions exist for recognizing indoor places, the literature lacks of approaches aimed at recognizing big outdoor places without the GPS employment. Even if GPS-based solutions assure great accuracy, they have a strong request in terms of energy necessary to achieve such result. As a consequence, if LBSs are thought on the move (e.g., mobile devices such as smartphones are used) energy consumption is a key constraint. This paper proposes a POI recognition algorithm called enhanced location recognition algorithm for automatic check-in applications (E-LRACI). It is an evolution of LRACI (Location Recognition Algorithm for automatic Check-In applications, originally reported in [I. Bisio, F. Lavagetto, M. Marchese, and A. Sciarrone, ''GPS/HPS-andwifi fingerprint-based location recognition for check-in applications over smartphones in cloud-based LBSS,'' IEEE Transactions on Multimedia, vol. 15, no. 4, pp. 858–869, Jun. 2013]) which aims at recognizing big outdoor places by only exploiting radio beacons emitted by WiFi access points. In terms of contributions, this paper first, proposes a novel fingerprint algorithm; second, solves the problem of big outdoor POI recognition without using GPS by leveraging the concept of spot; and third, compares the results obtained by E-LRACI and other reference works both in terms of recognition accuracy and computational complexity. The obtained numerical results, carried out on real data (acquired with Android-based smartphones), prove that E-LRACI provides the best results since it is able to guarantee the highest accuracy (95% versus, at most, 89%) at a lowest computational complexity with respect to the existing POI recognition algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the intercell interference coordination (ICIC) problem in heterogeneous cellular networks with randomly deployed small-cell base stations (BSs). Current research on ICIC mainly focuses on optimizing the spectrum and power allocations at BSs, whereas the user–BS association is treated as a separate issue. Nevertheless, the user–BS association problem is an important issue in ICIC and should be jointly optimized with resource allocations to achieve global optimality. In this paper, with the objective of maximizing the system sum rate in a distributed manner, we propose a novel belief propagation (BP) algorithm to jointly optimize user association, subchannel assignment, and power allocation. We first develop a factor graph model to decompose the network-wide objective and constraints into multiple local utilities. Then, we transform the maximization of local utilities into the estimations of marginal distributions and propose a distributed BP algorithm to solve the estimations. Simulations show that our distributed BP algorithm dramatically improves the performance compared with the benchmark scheme. [ABSTRACT FROM AUTHOR]

Selected mapping (SLM) schemes are widely used to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency-division multiplexing (OFDM) systems. Various time-domain approaches have been proposed for reducing the number of inverse fast Fourier transform (IFFT) operations required to generate the candidate signals in traditional SLM schemes. However, the resulting time-domain-generated signals are somewhat correlated, and thus, the PAPR reduction performance is seriously degraded. Accordingly, this paper proposes a novel PAPR reduction method in which frequency-domain phase rotation, cyclic shifting, complex conjugate, and subcarrier reversal operations are all employed to increase the diversity of the candidate signals. Furthermore, to circumvent the multiple-IFFT problem, all of the frequency-domain operations are converted into time-domain equivalents. It is shown that the subcarrier partitioning and reassembling processes are key to realizing low-complexity time-domain equivalent operations. Moreover, it is shown theoretically and numerically that the computational complexity of the proposed scheme is significantly lower than that of the traditional SLM method and that the PAPR reduction performance is within 0.001 dB of that of SLM. Overall, the results indicate that among all of the low-complexity architectures proposed in the literature, the method proposed in this paper most closely approximates the PAPR reduction performance of the traditional SLM scheme. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel bit loading algorithm to multicarrier non-orthogonal multiple access (NOMA) systems to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally a non-deterministic polynomial-time (NP)-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users, and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, in this paper, we propose an efficient bit loading algorithm for multicarrier NOMA systems and compare the complexity and throughput with a corresponding OMA system. The obtained results show that for the case of NOMA, bit loading provides an additional degree of freedom that allows non-uniform spectrum sharing among the users. In particular scenarios, the system may experience hybrid modes of operation, where the system switches between NOMA and OMA. The obtained numerical results show that NOMA can outperform OMA by 100% in terms of spectral efficiency, for a two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high complexity associated with the bit error rate (BER) computation in the case of NOMA. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of decoding in large-scale multiple-input–multiple-output (MIMO) systems. In this case, the optimal maximum-likelihood (ML) detector becomes impractical due to an exponential increase in the complexity with the signal and the constellation dimensions. This paper introduces an iterative decoding strategy with a tolerable complexity order. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We propose an ML relaxed detector that minimizes the Euclidean distance with the received signal while preserving a constant \ell1 -norm of the decoded signal. We also show that the detection problem is equivalent to a convex optimization problem, which is solvable in polynomial time. Two applications are proposed, and simulation results illustrate the efficiency of the proposed detector. [ABSTRACT FROM PUBLISHER]

This paper addresses energy efficiency (EE) optimization problems for uplink multiuser multiple-input–multiple-output (MIMO) systems with zero-forcing (ZF) transceivers, subject to a joint power and rate constraint for each user. As the optimization problem considered in this paper is quite different from the traditional one, directly applying the Dinkelbach method to the new EE optimization problem cannot bring an advantage of low complexity. Hence, we try to propose a novel approach inspired by the homotopy algorithms, where we concentrate our attention on the solution path for the EE optimization. Making use of the piecewise-defined property between power allocations and the overall transmission power, we fit the entire path of solution for every value of the overall transmission power by replacing the search variable with a specially selected parameter. By checking only a finite number of breakpoints, we then try to propose a new homotopy-type algorithm to obtain the maximum EE in an exact closed form to reduce the computational complexity significantly. Simulation results verify the performance of the new method. [ABSTRACT FROM PUBLISHER]

This work considers the problem of reliability and secrecy enhancement for wireless content sharing between a content source node and a content requester. By exploiting the social characteristics of multiple nodes in the presence of multiple independent eavesdroppers, this paper first investigates the impact of mobility for source node selection for transmission reliability, within a cooperative wireless network. Furthermore, trustworthiness and social-tie-based jammer node selections are explored for cooperative jamming to provide secrecy, while allocating power appropriately to the source node and the cooperative jammer node to maximize the worst-case ergodic secrecy rate. Specifically, the source transmitter is selected by first narrowing the potential link set to provide the desired transmission success rate while considering the physical channel and social characteristics jointly. Moreover, the effect of jammer dependability based on its social interactive behavior is also investigated. Instead of focusing on full channel state information (CSI), this paper also investigates the more practical case in which only statistical CSI knowledge is known. Our optimization is based on the upper and lower bounds to the secrecy rate to simplify the power optimization problem without noticeable performance loss. Additional performance analysis for the impact of social characteristics is given. Finally, a one-dimensional search is proposed to further reduce the computational complexity of the power optimization. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the construction of a transmit signal for downlink multiuser multiple-input single-output (MU-MISO) systems under the cost-effective 1-bit digital-to-analog converters (DACs). Due to the coarse nonliner property, conventional precoding methods, developed under infinite-resolution DACs, could not yield an attractive performance having a severe error-floor. Finding an optimal transmit signal, moreover, is computationally implausible due to its combinatorial nature. This challenging problem has been partially solved in our earlier work focusing on phase-shift-keying constellations. Our major contribution is to extend it into more practical quadrature-amplitude-modulation constellations in a non-trivial way. We first derive the so-called feasibility condition to guarantee that every user's noiseless received signal belongs to a desired decision region, and then transform it as linear constraints. Taking the robustness to an additive noise into account, the proposed construction method is formulated as a mixed integer linear programming problem. We construct an efficient algorithm to solve it (equivalently, to generate an 1-bit transmit signal). Via simulation results, we demonstrate the superiority of the proposed method with respect to a computational complexity and detection performance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the precoding scheme for multiple-input multiple-output multiple antennas eavesdropper (MIMOME) channels under the constraint of finite-alphabet inputs to ensure secure transmission between the source and the destination in the presence of an eavesdropper. With finite-alphabet inputs, there are no closed-form solutions for mutual information (MI) and its gradient-minimum mean-square error (MMSE) matrix. Therefore traditional precoding schemes based on direct calculation of MI and MMSE matrix through numerical integration involve prohibitive computational burden. To address this problem, this paper first derives asymptotically optimal lower bounds of MI and ergodic MI with which the computational complexity can be significantly reduced compared with the direct computation of MI. With the derived lower bounds, a low-complexity precoding scheme is proposed to maximize the secrecy rate, wherein a gradient descent based algorithm is used to obtain the precoding result. In the low and high signal-to-noise ratio (SNR) regions, we demonstrate that the proposed precoding scheme gives the same result as directly maximizing the secrecy rate, which indicates that the proposed low-complexity precoding scheme is asymptotically optimal. [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]

In this paper, a low complexity channel estimation method is proposed for two-dimensional (2-D) massive MIMO systems with uniform rectangular arrays of antennas. Instead of assuming independent fading between different transmit–receive antenna pairs, an array-structure-based physical channel model that characterizes the realistic scattering environments via the angles and gains associated with different propagation paths is studied. A 2-D Fourier-transform (FT) based on elevation and azimuth steering factors is applied to derive the spatial spectrum of received signals in angular domain at base station. Based on the spectrum analysis, the spatial spectrum of the received data is used to estimate the channel parameters, including direction of arrival (incident azimuth and elevation angles) and the path amplitude of each resolvable path. Since the channel coefficients are estimated in spatial domain and the proposed 2-D FT can be realized by fast-Fourier-transform, the computational complexity is reduced significantly. Simulation results are provided to verify the accuracy and the complexity of the proposed scheme. [ABSTRACT FROM AUTHOR]

To support the massive machine-type communications (mMTC) scenario for Internet of Things (IoTs) applications featured by large-scale device connectivity and low device activity, grant-free non-orthogonal multiple access (GF-NOMA) and compressive sensing (CS)-based multi-user detection methods (MUD) are developed. In this paper, we develop two Bayesian CS-based methods, i.e., sparse Bayesian Learning (SBL) and fast inverse-free sparse Bayesian Learning (FI-SBL), for joint MUD and channel estimation (CE) in GF-NOMA with Low-Activity Code Division Multiple Access (LA-CDMA) as the multiple access technology. SBL is investigated for robust MUD and CE by utilizing the parameterized Gaussian prior information. Then to resolve the high computational complexity of SBL, FI-SBL is proposed, which replaces matrix reversion operations with relaxed evidence lower bound. Simulation results show that the two proposed algorithms outperform the traditional methods, and FI-SBL reduces the computational complexity significantly. [ABSTRACT FROM AUTHOR]

Terahertz (THz) frequency band has become one of the popular researches for the short-range broadband, high-rate data communication, reliability, and strong anti-interference ability, which can realize better performance for dense vehicular network scenario. In this paper, we introduce a cache based dense vehicular networks with THz frequency link and focus on the resource allocation optimization problem of subband and power allocation by maximizing energy efficiency (EE). In order to reduce the computational complexity, the original optimization problem is formulated as a mean-field game (MFG) problem, where each cache access point (cAP) can solve its local EE optimization problem by Dinkelbach-style method and Lagrangian dual method. Different access cells interact with MFG through mean subband power. Then a global MFG based EE optimization algorithm is proposed to associate the local strategy of each cell, which guarantee the globality of the optimized solutions. The numerical results suggest the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a joint design of sparse code multiple access (SCMA) codebook and peak-to-average power ratio (PAPR) reduction based on the partial transmit sequence (PTS) technique is proposed for the downlink orthogonal frequency division multiplexing (OFDM) scheme. A benchmark scheme is firstly devised based on a direct extension of the conventional PTS-based OFDM scheme. Based on the characteristics of the near-optimal SCMA codebook designs reported in our previous work, a PTS-based SCMA-OFDM design is proposed by carefully determining the rotation angles of the SCMA codebooks, according to the adopted signal sub-constellations. A receiver which does not need the side information is further devised. Considering the phase ambiguity issue of the receiver, the PTS angles have been carefully refined. When compared to both the original scheme that does not consider PAPR reduction techniques and the benchmark scheme, the proposed scheme achieves a significantly enhanced PAPR performance with approximately the same error performance. In addition, a comparable PAPR performance, an improved error performance, and a lower computational complexity can be achieved when compared to the clipping-based SCMA-OFDM scheme reported in the previous literature. [ABSTRACT FROM AUTHOR]

Radar-embedded communication (REC) is a new type of covert communication whereby communication signals are embedded in radar backscattered echoes. Conventional REC waveform design strategies generally involve transforming the radar sampling sequence into a square matrix that can be eigenvalue decomposed; however, it may lose some original characteristics of the radar signal during this process. In this paper, we propose two REC waveform design methods based on singular value decomposition (SVD), namely, SVD-NDP (non-dominant-processing waveform based on SVD) and SVD-SNDP (shaped-non-dominant-processing waveform based on SVD), and derive the similarities between the radar signal and the proposed waveforms. We then simulate the anti-detection performance, anti-interception performance, and communication reliability of the proposed waveforms and analyze the degrees of freedom and computational complexity of their design. The results show that, compared with conventional REC waveforms, the proposed waveforms could improve communication reliability without sacrificing low probability of intercept (LPI) performance and at least double the design degrees of freedom. SVD-NDP and SVD-SNDP algorithms can reduce computational complexity by more than 30% compared with conventional waveform design algorithms based on eigenvalue decomposition when the number of communication waveforms (symbols) is set to 4 (2 bits), and the proportion of the dominant subspace is set to 50%. [ABSTRACT FROM AUTHOR]

In this paper, we consider nonregenerative multipair two-way relaying systems, where a relay node supports $K$ pairs of two-way communications in the presence of imperfect channel state information (CSI). We employ a stochastic approach to model the channel uncertainties and study a robust beamforming design at the relay. It is shown that the proposed beamformer provides a reduction in the computational complexity, as well as good robustness against channel errors, compared with conventional designs. To reduce the estimation overhead at the users, we also suggest an efficient downlink training method to inform the users of the channel-dependent self-interference cancellation (SIC) parameters. With the training method, the additive noise may incur a malfunction of the SIC. To address this issue, we develop an intelligent selection criterion that decides whether the SIC should be adopted or not. Finally, from simulation results, we demonstrate the efficiency of our proposed schemes. [ABSTRACT FROM PUBLISHER]

Simulation is a valuable way to develop cleaner transportation systems. Nevertheless, the complexity of this kind of system leads to complex models, particularly when taking into account nonlinear aspects of traction power substations (TPSs). Thus, this paper proposes different TPS models based on energetic macroscopic representation. An appropriate model is deduced from comparisons between the different models to realize energetic studies. It is finally applied to the simulation of a simple subway line, which is experimentally validated. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic resource allocation (RA) problem in cooperative OFDMA systems to maximize the average utility of all mobile stations (MSs) under different services. We propose a dynamic optimization framework for RA by considering three dynamic situations: time-varying fading channel, MSs' change of states, and relay stations (RSs)' change of states. Moreover, a dynamic RA algorithm based on discrete particle swarm optimization (DPSO) is proposed. The correlation between the adjacent frames is exploited to improve the performance of the dynamic RA algorithm. Simulation results show that the proposed dynamic algorithm achieves better performance at linear complexity, compared with the existing algorithms under different dynamic environments, while guaranteeing fairness to a proper level. [ABSTRACT FROM AUTHOR]

In this paper, we study user selection (US) strategies for a multiple-input–multiple-output (MIMO) cognitive radio (CR) downlink network, where the r-antenna underlay CR secondary users (SUs) coexist with a primary user (PU), and all terminals are equipped with multiple antennas. Two main scenarios are considered: 1) The t-antenna cognitive base station (CBS) has perfect or partial channel state information at the transmitter (CSIT) from the CBS to the PU receiver (RX), and 2) the CBS has absolutely no PU CSIT. For these scenarios, we propose and evaluate multiple SU selection schemes that are applicable to both best-effort PU interference mitigation and hard interference temperature (IT) constraints. The computational complexity of the proposed schemes can be significantly smaller than that of an exhaustive search with negligible performance degradation. For the selection of C SUs out of K, whereas an exhaustive search is on the order of \mathcal{O}(\binom{K}{C}C^4r^3). When t$ and r$ are of the same order, the computational complexity of the proposed scheme can be \binom{K}{C}C^4/K times smaller. Mathematical complexity analysis and numerical simulations are provided to show the advantage of our schemes. [ABSTRACT FROM PUBLISHER]

The emerging non-orthogonal multiple access (NOMA) technique for fifth-generation (5G) heterogeneous network (HetNet) yields performance improvement specifically by boosting the network capacity and spectral efficiency. This paper proposes a new NOMA scheme for the uplink HetNet using oblique projection (OP) under perfect and imperfect channel state information at the receiver (CSIR). In this scheme, the OP operation performed at the femtocell base station (FBS) recovers the desired NOMA users’ signal from the composite received signal. Unlike the existing successive interference cancellation (SIC) based NOMA schemes, the proposed scheme does not demand any power control or user clustering techniques for decoding NOMA users’ data at the receiver side. Further, it does not need information exchange between users for decoding and hence the computational complexity and the signaling overhead of the system gets reduced significantly. To evaluate the performance of the proposed scheme under perfect CSIR analytically, we derive the closed-form expression for the probability of error and outage probability at the FBS receiver. We also derive the diversity order, sum-rate, and perform the computational complexity analysis of the NOMA-HetNet system employing the proposed scheme. Simulation results illustrate that the derived analytical results accurately describe the proposed NOMA scheme. We compare the performance of the proposed technique with a prevailing SIC based NOMA scheme and establish its superiority. Performance of the proposed NOMA scheme under imperfect CSIR is also studied and established that the scheme works well under this scenario also. [ABSTRACT FROM AUTHOR]

In the era of the fifth generation (5G) communication networks, massive multiple-input multiple-output (MIMO) systems demand even lower computation complexity and power consumption while catching up with good detection performance. In this paper, a low-complexity nonlinear detection algorithm is proposed for massive MIMO systems, which is based on partial tree search and successive interference cancellation (SIC). The proposed scheme allows us to expedite the detection process by coping with the transmit symbols group by group. As compared to vertical-Bell laboratories layered space time (V-BLAST), the major breakthrough of computation reduction lies in the fact that the partial tree search can assist the detection process to avoid the inversion of the detection matrix required in each recursion of the SIC process. Both computational complexity analysis and simulation results show that our proposed algorithm not only significantly reduces computational complexity, but also has better bit error rate (BER) performance. [ABSTRACT FROM AUTHOR]

To be differentiated from the literature, in this paper, we aim to alleviate the impact of pilot contamination (PC) on the massive MIMO systems by cooperatively scheduling users (rather than just assigning) among neighboring cells to share the limited orthogonal pilots. To this end, we develop the multi-cell cooperative scheduling (MCCS) algorithm together with the cooperative scheduling indexes (COSIs) for maximizing the data rate, maximizing the Jain's fairness index and reaching a better tradeoff in-between. For convenience, they are denoted by the COSIs for the CMDR, CMMF and CPF schedulers, respectively. However, its high computational complexity may somehow resists its from practical applications. Thus, a low-complexity cooperative proportional fairness (LC-CPF) algorithm is designed to well approach the MCCS algorithm using the CPF COSI; and at the meantime, the order of computational complexity can be significantly reduced. Moreover, its ability to reach a better tradeoff can still be maintained under the impact of high spectrum-sharing interference. In addition, it is interesting to find that using the proposed cooperative scheduling methods, the open-loop power control mechanism is no longer required for compensating the differences of received signal quality between users. [ABSTRACT FROM AUTHOR]

A new approach of channel estimation for multiantenna systems is put forward in this paper, which can be adopted in high-mobility situations such as high speed trains. The channel impulse response is abstracted as three domains to improve the modeling accuracy. Both the time-domain preamble and the frequency-domain pilot are adopted in the orthogonal frequency division multiplexing (OFDM) frame. First, the training in time domain is exploited to obtain the partial common support of the channel. Then, the pilot location optimized by the genetic algorithm is employed to build the framework of structured compressive sensing and recover the channel. A novel compressive recovery algorithm called adaptive support-aware block orthogonal matching pursuit for multiple input multiple output-OFDM systems is proposed to solve the problem. It is manifested in the simulation that the scheme in this paper outperforms the traditional ones in both recovery probability, mean square error, and bit error rate over the doubly selective channel with low computational complexity. [ABSTRACT FROM AUTHOR]

This paper tackles the joint user association (UA) and power control (PC) problem for dynamic heterogeneous networks (HetNets), where the number of active users varies over time. Since the joint UA and PC problem is a mixed-integer problem, it is challenging to develop a joint UA and PC algorithm that closely achieves the optimal proportional fairness (PF) performance while satisfying the tight computation time budget of recent 3GPP standards. Although previous studies have reduced the computation time on the UA and PC by employing a fully-connected-neural network (FCN), the FCN architecture is not generally applicable to dynamic HetNets because of a varying number of users. In pursuit of scalable neural network design, we propose an unsupervised learning-based UA and PC algorithm using a recurrent neural network (RNN). Notably, in the RNN-based UA, we relax the combinatorial UA variable into a low-dimensional continuous variable, the length of which is invariant to the number of users. The optimality loss in the variable relaxation is upper-bounded by the negligibly small duality gap. Through extensive experiments, we show that the proposed scheme has a 10% higher PF performance than conventional optimization-based methods with the same number of flops and closely achieves the optimal PF performance for various numbers of users. [ABSTRACT FROM AUTHOR]

The paper presents several novel residual recurrent neural network (RNN) models for digital predistortion of radio-frequency power amplifiers. Then, unstructured and structured neural network (NN) pruning algorithms are proposed to reduce the per-sample computational complexity and the required memory, without compromising their modeling performance. The mathematical equations of the complexity and the memory are derived to analyze the hardware cost and design low cost models. An experimental platform is developed to measure the modeling performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error, error vector magnitude (EVM) and bit error rate. The platform uses an envelope tracking PA excited by 80 MHz IEEE 802.11ac signals. Compared with the state-of-the-art RNN model in (Sun et al., 2019), the proposed models reduce the per-sample complexity and required memory by $94\hbox{--}99$ %, they also improve the ACLR and EVM by at least 3 dB and 1 dB at 0.45 dB and 3.15 dB gain compression, respectively. Furthermore, an observation path with time alignment is used to remove the transmit latency in the state-of-the-art approach. At 3.15 dB gain compression, the proposed models outperform the state-of-the-art feedforward and convolutional NN models by at least 1.3 dB in EVM, outperform the general memory polynomial model by at least 3.3 dB in EVM. The above evidences indicate that the proposed RNN models can be implemented with relatively low hardware cost and achieve the best EVM in high power efficiency scenario. [ABSTRACT FROM AUTHOR]

The efficiency of link adaptation in wireless communications relies greatly on the accuracy of channel knowledge and transmission mode selection. In this paper, a novel deep learning based link adaptation framework is proposed for the orthogonal frequency-division multiplexing (OFDM) systems with compressed-sensing-assisted index modulation, termed as OFDM-CSIM, communicating over millimeter-wave (mmWave) channels. To achieve link adaptation, a novel multi-layer sparse Bayesian learning (SBL) algorithm is proposed for accurately and instantaneously providing the required channel state information. Meanwhile, a deep neural networks (DNN)-assisted adaptive modulation algorithm is proposed to choose the best possible transmission mode to maximize the achievable throughput. Simulation results show that the proposed multi-layer SBL algorithm enables more accurate channel estimation than the conventional techniques. The DNN-based adaptive modulator is capable of achieving a higher throughput than the learning-assisted solution based on the $k$ nearest neighbor ($k$ -NN) algorithm, and also the classic average signal-to-noise ratio (SNR)-based solutions. Moreover, analysis shows that both the multi-layer SBL algorithm and the DNN-assisted adaptive modulator achieve better performance than their respective conventional counterparts while at a significantly lower computational complexity cost. [ABSTRACT FROM AUTHOR]

This paper studies a secure multiuser multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS), where multiple colluding eavesdroppers (EVEs) coexist. We aim to maximize the sum secrecy rate (SSR) via jointly optimizing the beamforming vectors, the artificial noise (AN) and the phase shifts at the IRS subject to the maximum transmit power constraint and unit modulus constraints. To address the non-convex optimization problem, we first propose an alternating optimization (AO) algorithm based on semidefinite relaxation (SDR) and obtain a high-quality sub-optimal solution. In order to reduce the high computational complexity, a low-complexity alternating optimization (LC-AO) algorithm is developed, in which the beamforming vectors, AN and the IRS phase shifts are optimized alternately by the generalized power iteration (GPI) and the Riemannian manifold conjugate gradient (RMCG) algorithm, respectively. Simulation results show the advantages of deploying the IRS in improving the system secrecy performance. [ABSTRACT FROM AUTHOR]

Grant-free non-orthogonal multiple access (NOMA) scheme is considered as a promising candidate for the enabling of massive connectivity and reduced signalling overhead for Internet of Things (IoT) applications in massive machine-type communication (mMTC) networks. Exploiting the inherent nature of sporadic transmissions in the grant-free NOMA systems, compressed sensing based multiuser detection (CS-MUD) has been deemed as a powerful solution to user activity detection (UAD) and data detection (DD). In this paper, block coordinate descend (BCD) method is employed in CS-MUD to reduce the computational complexity. We propose two modified BCD based algorithms, called enhanced BCD (EBCD) and complexity reduction enhanced BCD (CR-EBCD), respectively. To be specific, by incorporating a novel candidate set pruning mechanism into the original BCD framework, our proposed EBCD algorithm achieves remarkable CS-MUD performance improvement. In addition, the proposed CR-EBCD algorithm further ameliorates the proposed EBCD by eliminating the redundant matrix multiplications during the iteration process. As a consequence, compared with the proposed EBCD algorithm, our proposed CR-EBCD algorithm enjoys two orders of magnitude complexity saving without any CS-MUD performance degradation, rendering it a viable solution for future mMTC scenarios. Extensive simulation results demonstrate the bound-approaching performance as well as ultra-low computational complexity. [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]

Massive multiple-input multiple-output (MIMO) is considered as a critical technique in 5 G New Radio (NR), due to its capability to significantly improve data rate and link reliability for the radio link. To make it a reality, it is essential to evaluate massive MIMO performance in realistic spatial fading channel conditions. Specifically, dynamic spatial channel emulation is crucial to evaluate the massive MIMO beam operations. Due to the high cost associated with both the traditional conduced testing method and the standardized multi-probe anechoic chamber (MPAC) based Over-the-Air (OTA) testing setup for massive MIMO performance testing, fading channel emulation for massive MIMO testing using a conductive phase matrix setup has been proposed in the literature. However, it is still a challenging problem to emulate dynamic spatial channels in a cost-effective and efficient manner for the conductive phase matrix setup, since traditional algorithms suffer from high computational complexity and system cost. This paper proposes a fast and accurate framework to emulate dynamic spatial channels in the phase matrix setup, where the basic idea is to mimic a virtual MPAC setup. By doing so, we can mimic MPAC setup with flexible probe configurations. Furthermore, a novel successive cancellation strategy and a closed-form algorithm are proposed to determine the probe locations and probe weights, respectively, which can significantly reduce the computational complexity associated with dynamic channel emulation. Simulation results show that the proposed algorithm is highly accurate and computationally efficient, compared to state-of-art solutions. Further, we apply our developed algorithm to emulate measured non-stationary channels and excellent emulation accuracy can be achieved. The proposed method can be employed for emulating dynamic spatial channels, which is of great importance for massive MIMO performance testing. [ABSTRACT FROM AUTHOR]

IEEE 802.11p vehicle-to-vehicle and vehicle-to-infrastructure communication technology is currently an emerging research topic in both industry and academia. Respective spectrum allocation of 10-MHz channels in the 5.9-GHz band in the United States and Europe allows considering intervehicle transmission of live-video information as a basis, which enables a new class of safety and infotainment automotive applications such as road video surveillance. This paper is first of its kind where such a video transmission system is developed and experimentally validated. We propose a low-complexity unequal packet loss protection and rate control algorithms for scalable video coding based on the 3-D discrete wavelet transform. We show that in comparison with a scalable extension of the H.264/AVC standard, the new codec is less sensitive to packet losses, has less computational complexity, and provides comparable performance in case of unequal packet loss protection. It is specially designed to cope with severe channel fading typical for dynamic vehicular environments and has low complexity, making it a feasible solution for real-time automotive surveillance applications. Extensive measurements obtained in realistic city traffic scenarios demonstrate that good visual quality and continuous playback is possible when the moving vehicle is in the radius of 600 m from the roadside unit. [ABSTRACT FROM PUBLISHER]

In order to overcome the inherent latency in multi-user orthogonal multiple access (OMA) unmanned aerial vehicle (UAV) networks. In this paper, we investigate a UAV-enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by the ground users. To maximize the users’ sum-rate, we formulate a optimization problem, in which the UAV deployment position and the power control are jointly optimized subject to the transmission power constraints and the quality of service (QoS) constraints. However, this problem is a mixed integer non-convex optimization problem and thus it is difficult to solve directly. Therefore, we first transform the non-convex optimization problem into a convex one based on the first-order approximation technique and the penalty function method. Then, a successive convex approximate (SCA) technique based iterative algorithm as well as its initialization scheme are developed to find a suboptimal solution to the original problem. Afterwards, a low-complexity approximate algorithm is proposed to reduce the high computational complexity of the iterative algorithm. Numerical results show that our proposed iterative algorithm and low-complexity approximate algorithm can achieve a similar performance, and they can efficiently improve the sum-rate compared with OMA scheme and conventional NOMA scheme. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal training design for multiple-input multiple-output (MIMO) amplify-and-forward (AF) two-way relay systems in the presence of interference. In order to address the interference issue while accurately estimating the channels, we minimize the total transmit power consumed by two source nodes and a relay subject to the constraint on mean square error (MSE) of the channel estimation, which is a nonconvex problem. To solve this challenging problem, we first derive the optimal structure of the training signals, and then, we convert the original problem into a tractable convex form, from which the optimal training scheme is designed efficiently. We further consider a practical special case, for which the optimal training scheme is designed with a lower complexity and the useful insights into the optimal training design are obtained. Also, to reduce the computational complexity of the optimal training design, we propose an asymptotically optimal training scheme in the high signal-to-interference-plus-noise ratio (SINR) regime with a low complexity. Simulation results demonstrate that the proposed schemes significantly outperform the existing schemes. [ABSTRACT FROM AUTHOR]

Quadrature spatial modulation (QSM) was recently proposed to increase the spectral efficiency of spatial modulation. In QSM, the real and imaginary parts of a symbol are transmitted independently from different antennas. A new spatial modulation technique, termed signed quadrature spatial modulation (SQSM), is introduced in this paper. Unlike QSM, SQSM creates four-dimensional spatial constellation, and hence results in higher system throughput. This technique extends the traditional spatial-constellation dimension into $\pm$ in-phase and quadrature-phase dimensions. The ensuing constellation consists of four antenna indices, which are chosen independently in order to transmit the real and imaginary parts of the modulated symbols and their inverse. The performance of the proposed technique is compared to the most-recent SM techniques, namely, QSM, double SM, and improved QSM (IQSM). Numerical results demonstrate that SQSM provides significant performance gains (3–7 dB), particularly when a large number of transmit antennas is used. This comes with a marginal increase in computational complexity compared to QSM and SM. Also, while IQSM can be competitive with SQSM in some cases, SQSM has a much lower computational complexity, e.g., 46% reduction in complexity in case of 16 transmit antennas and 16 bps/Hz. [ABSTRACT FROM AUTHOR]

In urban vehicular ad-hoc networks (VANET), owing to the high mobility and uneven distribution of vehicles, the rapid and reliable transmission of emergency data is a challenging task. Some bio-inspired intelligent routing protocols are effective in transmitting emergency data. However, their computational complexity and convergence speed may result in large delays. Furthermore, it has been demonstrated that parked vehicles may be used as relay nodes and thus facilitate data packet transmission in VANET. In this paper, we propose a parking-area-assisted spider-web routing protocol (PASRP) for emergency data in urban VANET. In PASRP, a spider-web transmission model is established based on the parking area by using a geographic information system and a digital map. Two control messages, request-spider and confirm-spider, are sent to obtain the transmission path from the source vehicle to the destination vehicle, and the path with the least delay is selected as the transmission path. Then, the emergency data are forwarded on the selected path using a multi-mode greedy algorithm and are prioritized by a dynamic multi-priority scheduling mechanism. Finally, the simulation results demonstrate that the proposed PASRP outperforms the existing greedy perimeter stateless routing and transmission mechanism for emergency data protocols. [ABSTRACT FROM AUTHOR]

This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventional caching algorithms are inapplicable as their computational complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand. To overcome such difficulties, we propose a distributed caching algorithm under a stochastic geometric network model, as well as a spatio-temporal user demand model that characterizes the content popularity dynamics. By leveraging mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate this consistent complexity of the proposed algorithm with respect to the number of SBSs. Also, it shows that the proposed caching algorithm reduces not only the long run average cost of the network but also the redundant cached data respectively by 24% and 42%, compared to a baseline caching algorithm. Additionally, the simulation results show that the proposed caching algorithm is robust to imperfect popularity information while ensuring a low computational complexity. [ABSTRACT FROM AUTHOR]

Recently, classifying the modulation schemes of signals using deep neural network has received much attention. In this paper, we introduce a general model of deep neural network (DNN)-based modulation classifiers for single-input single-output (SISO) systems. Its feasibility is analyzed using maximum a posteriori probability (MAP) criterion and its robustness to uncertain noise conditions is compared to that of the conventional maximum likelihood (ML)-based classifiers. To reduce the design and training cost of DNN classifiers, a simple but effective pre-processing method is introduced and adopted. Furthermore, featuring multiple recurrent neural network (RNN) layers, the DNN modulation classifier is realized. Simulation results show that the proposed RNN-based classifier is robust to the uncertain noise conditions, and the performance of it approaches to that of the ideal ML classifier with perfect channel and noise information. Moreover, with a much lower complexity, it outperforms the existing ML-based classifiers, specifically, expectation maximization (EM) and expectation conditional maximization (ECM) classifiers which iteratively estimate channel and noise parameters. In addition, the proposed classifier is shown to be invariant to the signal distortion such as frequency offset. Furthermore, the adopted pre-processing method is shown to accelerate the training process of our proposed classifier, thus reducing the training cost. Lastly, the computational complexity of our proposed classifier is analyzed and compared to other traditional ones, which further demonstrates its overall advantage. [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]

Recently, model-free power control approaches have been developed to achieve the near-optimal performance of cell-free (CF) massive multiple-input multiple-output (MIMO) with affordable computational complexity. In particular, deep reinforcement learning (DRL) is one of such promising techniques for realizing effective power control. In this paper, we propose a model-free method adopting the deep deterministic policy gradient algorithm (DDPG) with feedforward neural networks (NNs) to solve the downlink max-min power control problem in CF massive MIMO systems. Our result shows that compared with the conventional convex optimization algorithm, the proposed DDPG method can effectively strike a performance-complexity trade-off obtaining 1,000 times faster implementation speed and approximately the same achievable user rate as the optimal solution produced by conventional numerical convex optimization solvers, thereby offering effective power control implementations for large-scale systems. Finally, we extend the DDPG algorithm to both the max-sum and the max-product power control problems, while achieving better performance than that achieved by the conventional deep learning algorithm. [ABSTRACT FROM AUTHOR]

Backscatter communication is an energy-efficient communication technique for the Internet of things (IoT) devices. It enables data transmission by reflecting the incident radio signals. In this paper, we propose a communication mode selection scheme for IoT devices that can communicate using either active transmission or backscattering. In the active transmission mode, the IoT devices can transmit data over narrowband subcarriers using power-domain non-orthogonal multiple access (NOMA). In the backscattering mode, which operates over shorter distance than active transmission, nearby user equipment (UE) devices are used as relays. The UEs receive the backscattered signals from the IoT devices and forward them to the base station. We formulate a connection density maximization problem to select the communication mode used by each IoT device. We determine the IoT device pairing for active transmission mode with NOMA and UE-IoT device association for backscattering mode. The formulated problem is a binary integer programming problem. Although it can be solved optimally, the optimal algorithm incurs exponential computational complexity. Hence, we propose a low-complexity suboptimal algorithm to solve this problem. Results show that our proposed algorithm can enhance the connection density of narrowband IoT systems by up to 64% when compared with using single communication mode. [ABSTRACT FROM AUTHOR]

Cybertwin is a promising networking paradigm that supports complex wireless interconnectivity and enhances reliable communications by using edge capability in different applications. However, the computation-heavy phased-array weight vector generation is the significant barrier for the implementation of physical layer security (PLS) for millimeter-wave (MMWV) communications in cybertwin-driven scenarios, like vehicle-to-everything (V2X) applications. In this paper, we propose a low-complexity phased-array PLS scheme in MMWV wireless communications for cybertwin-driven V2X applications. The computational complexity is much reduced in a cost-effective way, where the transmitting phased-array weight vectors are updated at the symbol rate by randomly swapping the pre-calculated initial weight vector elements instead of directly calculating a new weight vector. Performance evaluation shows the proposed scheme possesses a lower computational complexity than the compared PLS schemes. The simulation demonstrates that the constellations are scrambled at undesired directions, while the constellation at target direction remains clear and constant. Thus, the communication secrecy is preserved for the receiver at the target direction in cybertwin-driven vehicular communications. Finally, we demonstrate that the proposed scheme achieves good security performances in terms of symbol error rate (SER) and secrecy outage probability. [ABSTRACT FROM AUTHOR]

In traditional orthogonal frequency division multiplexing (OFDM) systems, a cyclic prefix (CP) must be used in every block for intersymbol interference (ISI) suppression in time-dispersive channels at a price of severely impaired spectrum efficiency. To deal with this issue, the symbol cyclic shift equalization (SCSE) algorithm was proposed to implement a CP-free OFDM system. In this paper, we aim to further reduce the computational complexity of the SCSE algorithm. In particular, a pulse amplitude modulation (PAM) is used at a transmitter to construct conjugate symmetric OFDM blocks, while only partial samples in an OFDM block are involved in the SCSE algorithm at the receiver side, leading to a significantly reduced implementation complexity if compared to an SCSE quadratic-amplitude modulation(QAM)/phase-shift keying (PSK)-OFDM system, where all samples in an OFDM block must be involved in the detection process. The performance of the proposed SCSE PAM-OFDM scheme is analyzed and compared to traditional OFDM systems. The results obtained from this paper verify the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a heterogenous network in which low-power indoor femtocells are deployed in the coverage area of the existing macrobase station (MBS). This paper proposes a novel coordinated random beamforming and user-scheduling strategy to improve the throughput of users served by the femtocell access point (FAP) while satisfying the quality-of-service (QoS) requirements of users served by both MBS and FAP. The strategy, termed as QoS-aware coordinated scheduling (QACS), requires limited coordination between the MBS and FAP, i.e., only the indexes of the qualified beams are shared. Exact statistical analysis for the ergodic achievable rate of both FAP and MBS with the proposed strategy are presented. Scheduling fairness is also addressed for the proposed QACS. [ABSTRACT FROM PUBLISHER]

When a large number of machine-to-machine (M2M) terminals attempt to access the Long-Term Evolution (LTE)/LTE Advanced (LTE-A) cellular network using the physical random access channel (PRACH), congestion and overload may result, which can lead to serious degradation of performance for both M2M and human-to-human (H2H) terminals. The main cause for this is the inherent complexity of the four-way handshake used for random access, which is well suited for H2H terminal access but unsuitable for massive M2M access. In this paper, we describe an efficient scheme for concurrent M2M and H2H access on the PRACH, which separates the resources for M2M and H2H access at the level of preamble codes and avoids the use of the four-step handshake for M2M terminals by implementing a carrier sense multiple access with collision avoidance (CSMA/CA) overlay network using the designated preamble codes. We analyze the performance of the scheme for both H2H and M2M traffic and show the values of the most important design parameters that enable this scheme to support concurrent access by H2H and M2M terminals with little performance degradation. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the relay selection and power allocation (RSPA) problem in amplify-and-forward (AF) relaying networks. The objective is to minimize the outage probability at the receiver, assuming that only the mean channel gain information is available. By taking advantage of its special structure, we propose to decompose the RSPA problem into two tractable subproblems, which can be implemented in a semidistributed manner. For the relay selection (RS) subproblem, a relay-ordering-based scheme is designed to incrementally select a subset of relays from the candidate nodes according to a specific metric in a centralized manner, which is dominated by the mean channel gain of each individual nodes. For the power allocation (PA) subproblem, a source-driven iterative algorithm is proposed for each relay to decide its optimal transmit power locally by exploiting the convexity of this subproblem. Simulation results are provided to evaluate the outage and signal-to-noise-ratio (SNR) performance of the proposed scheme under different channel conditions. It is shown that the proposed scheme can dynamically select relays and adjust power allocation according to the SNR and channel conditions. It outperforms the other two benchmark schemes, and the performance is close to the optimal scheme with exhaustive search, but the computational complexity is significantly reduced. [ABSTRACT FROM AUTHOR]

The frequency-domain pilot multiplexing technique (FDPMT) has recently emerged as an appealing technique for channel estimation in single-carrier frequency-domain equalization (SC-FDE) systems due to the high spectral efficiency it promises. However, the performance of the FDPMT system is significantly affected by the signal distortion level, which depends on the pilot position selection (PPS) strategy at the transmitter and the signal reconstruction (SR) ability of the receiver. In this paper, the relationship between the interference pattern and the loss of equally spaced tones is discovered. By exploiting this relationship, we propose a novel PPS method and a novel SR method. The proposed PPS method, which is based on the minimization of the most severe intersymbol interference (ISI) experienced by those subblocks decimated from the transmitted signal block, dispenses with a priori knowledge of the channel statistics and imposes very low computational cost to the transmitter. The proposed SR method, which is based on the maximum-likelihood criterion, can be carried out subblock by subblock in parallel and sample by sample within each subblock, saving the computational complexity and time consumption of the receiver. Simulation results in terms of bit error rate (BER) corroborate the superiority of both proposed methods over their existing counterparts in various channels of interest. [ABSTRACT FROM AUTHOR]

The beam training protocol in millimeter-wave cellular systems requires a long processing time that is proportional to the product of the number of Tx beams, number of Rx beams, and number of neighbor base stations. In this paper, a preamble design technique that can significantly reduce the processing time for beam training in an orthogonal-frequency-division-multiplexing-based cellular system is proposed by transmitting multiple beams with their corresponding Tx beam IDs (BIDs) and cell ID (CID). The Zadoff–Chu (ZC) sequence, with its length given by a multiplication of two prime numbers, is proposed for an efficient CID and BID (CBID) search in a preamble design. It is shown by computer simulation that the proposed preamble design technique can obtain a 10- to 20-fold computational complexity gain in CBID detection and achieve a performance (0.1-dB signal-to-noise ratio shift) that is close to that of a ZC sequence with a prime length. [ABSTRACT FROM PUBLISHER]

Faster-than-Nyquist signaling (FTNS) has emerged as a promising technique to increase communication capacity in bandwidth-limited channels. However, the presence of FTN-induced inter-symbol interference (FTN-ISI) in the received observations, is detrimental to channel estimation (CE) and data detection in terms of computational complexity and performance. This paper copes with these problems by incorporating linear pre-equalization (LPE) and composite precoding formed by linear spectral precoding and Tomlinson-Harashima precoding (THP), into the FTNS. Specifically, LPE completely pre-equalizes the FTN-ISI, while spectral precoding resolves the LPE-caused signal spectral broadening by introducing proper artificial ISI, which is pre-equalized by THP. Channel-induced ISI, as the only ISI component in the observations, is estimated and equalized using the classical frequency-domain low-complexity schemes. We show that there are four advantages of the LPE-aided CE over the CE designed for the FTN transmissions without FTN-ISI pre-equalization, namely lower pilot overhead, simpler yet optimal pilot sequence design, lower mean-squared error of CE, and more robust against the FTN-ISI. Simulation results show that our scheme improves the performance of CE and detection, compared to existing FTN frequency-domain CE and equalization schemes. [ABSTRACT FROM AUTHOR]