The space–air–ground integrated network (SAGIN) has emerged as a promising architecture for future wireless communication systems. However, its open and heterogeneous nature introduces significant physical-layer security risks. To overcome the security concerns in SAGIN, we propose a constant-envelope orthogonal frequency division multiplexing with index modulation (CE-OFDM-IM) based transmission framework, where the constant-envelope property ensures compatibility with hardware-constrained SAGIN environments, while the index modulation mechanism inherently supports covert signaling via implicit subcarrier activation patterns. To enable reliable detection at legitimate receivers, we design both optimal and low-complexity receiver architectures, and further introduce a clipping-based technique to suppress phase wrapping and enhance demodulation robustness. Additionally, the average bit error probability (ABEP) is analytically characterized through performance analysis. Finally, simulation results demonstrate that the proposed CE-OFDM-IM system achieves robust transmission with zero peak-to-average power ratio (PAPR), offering a practical and energy-efficient solution for secure communications in SAGIN environments.

Artificial noise (AN) has been recognized as an effective physical-layer security scheme impairing the eavesdropper (Eve). Recently, artificial noise elimination (ANE) has emerged as a promising strategy to mitigate the impact of AN at Eves. However, conventional ANE schemes rely on prior knowledge, such as legitimate channel state information (CSI) or classification information, which may limit their practical applicability. To address these practical challenges, we propose an ANE scheme beyond prior knowledge (BPK) by leveraging machine learning algorithms. Firstly, a coarse projection is applied to partially eliminate the impact of AN using maximum likelihood estimation on the equivalent AN matrix. Secondly, a density clustering algorithm is introduced to obtain classification information based on the coarsely-projected observed vectors. Thirdly, a generalized principal component analysis (PCA)-based ANE algorithm is developed to effectively mitigate the residual AN using the obtained classification information. Furthermore, the artificial-noise-to-signal ratio (ANSR) and computational complexity are analyzed for performance revaluation, and a redefinition of several AN design principles is provided for scenarios involving a powerful Eve equipped with the BPK-ANE scheme by deriving the validity boundary. Finally, numerical results reveal key insights into four principles of AN: 1) Allocating less power to AN; 2) Reducing the randomness of AN; 3) Increasing the number of transmit antennas; and 4) Increasing the modulation order.

Reinforcement learning (RL) has emerged as a promising anti-jamming solution in frequency-hopping (FH) communication systems, owing to its dynamic anti-jamming capability independent of FH patterns. Nevertheless, conventional RL-based methods may require extensive retraining when previously encountered jamming signals reappear after a long period because of the obliviousness of RL. To address this issue, this paper proposes a memory-driven anti-jamming approach for FH communications. Inspired by reinforced continual learning (RCL), the proposed approach employs an adaptive expansion mechanism of sub-deep neural networks to efficiently capture and retain jamming patterns. Simulation results demonstrate that the proposed approach can make more rapid anti-jamming decisions compared to conventional methods when encountering previously observed jamming patterns.

Ambient backscatter communication (AmBC) is a crucial technology for addressing the energy consumption challenge in green Internet of Things through the reflection or absorption of surrounding radio frequency (RF) signals. Nevertheless, it grapples with the intricacies of ambient RF signal and the round-trip path loss. For the traditional detectors, the incorporation of pilot sequences results in the reduction in spectral efficiency. Furthermore, traditional energy-based detectors are inherently susceptible to a notable error floor issue, attributed to the co-channel direct link interference (DLI). Consequently, this paper proposes a blind symbol detector without the prior knowledge concerning the channel state information, signal variance, and noise variance. Leveraging the intra-symbol differential amplitude shift keying (IDASK) scheme, this detector effectively redirects the majority of DLI energy towards the largest eigenvalue of the received sample covariance matrix, thereby utilizing the second largest eigenvalue for efficient symbol detection. Simulation results demonstrate that the proposed blind detector exhibits a significant enhancement in symbol detection performance compared to traditional counterparts.

We propose a robust transceiver design for a covert integrated sensing and communications (ISAC) system with imperfect channel state information (CSI). Considering both bounded and probabilistic CSI error models, we formulate worst-case and outage-constrained robust optimization problems of joint transceiver beamforming and radar waveform design to balance the radar performance of multiple targets while ensuring the communications performance and covertness of the system. The optimization problems are challenging due to the non-convexity arising from the semi-infinite constraints (SICs) and the coupled transceiver variables. In an effort to tackle the former difficulty, S-procedure and Bernstein-type inequality are introduced for converting the SICs into finite convex linear matrix inequalities (LMIs) and second-order cone constraints. A robust alternating optimization framework referred to alternating double-checking is developed for decoupling the transceiver design problem into feasibility-checking transmitter- and receiver-side subproblems, transforming the rank-one constraints into a set of LMIs, and verifying the feasibility of beamforming by invoking the matrix-lifting scheme. Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm in improving the performance of covert ISAC systems.

In this paper, we explore robust transceiver design for an integrated sensing and communications system with bounded channel estimation error. To maximize the minimum sensing performance of multiple targets while satisfying communications requirements, we study the worst-case robust op-timization problem by jointly optimizing the transmitter and receiver variables. The formulated problem is challenging due to the non-convexity arising from the semi-infinite constraints (SICs) and coupled variables. To overcome these difficulties, we adopt the S-procedure to convert the SICs into finite convex linear matrix inequalities (LMIs). Using the alternating opti-mization technique, we decouple the robust transceiver design problem into feasibility-checking subproblems. By exploiting matrix lifting, we transform the rank-one constraints into a set of LMIs, which is leveraged to further check the feasibility of the obtained beamforming scheme. Numerical results are provided to demonstrate the robustness and effectiveness of the proposed algorithm in combating channel errors and improving the performance of ISAC systems.

In this paper, we study the beamforming schemes via the novel frequency diverse array (FDA) on enhancing covert communications performance. We first consider the ideal scenario where the channel state information (CSI) is perfectly known at the transmitter. Then we characterize the key role of minimizing the correlation of the communication and detection channels in boosting the covertness of the system, which also provides a theoretical design principle for FDA-specific carrier frequency scheduling. By exploiting the optimization framework block successive upper bound minimization (BSUM), we propose a frequency scheduling method which leads to a two-phase beamforming scheme to facilitate the covert transmission. Subsequently, we extend the scenario to the more practical one with partial CSI. By leveraging the convex hull, we manage to transform the formulated semi-infinite programming problem to an equivalent semi-definite one which can be solved optimally. In addition, we present a process to construct the optimal beamforming vector. To mitigate the channel correlations in this scenario, we generalize the steps of BSUM and propose an algorithm to schedule the frequencies efficiently. Afterwards, a three-phase robust beamforming scheme is summarized, which boosts the covert rate significantly. Numerical results are provided to demonstrate the superiority of the proposed schemes.

This work employs an unmanned aerial vehicle (UAV) as a jammer to aid a covert communication from a transmitter Alice to a receiver Bob, where the LAV transmits artificial noise (AN) with random power to deliberately create interference to a warden Willie. To maximize the system performance, we formulate an optimization problem to jointly design the UAV’s trajectory and Alice’s transmit power. The formulated optimization problem is non-convex that is difficult to tackle directly. To this end, this work, for the first time, develops a geometric (GM) method to solve the optimization problem. Our examination shows that the GM method can significantly outperforms a benchmark method in terms of achieving a higher average covert rate and the complexity of the GM method is lower than that of the benchmark method.

In this paper, we investigate multi-antenna covert communications, where the transmitter Alice equips multiple antennas, the legitimate receiver Bob and the eavesdropper Willie equips single antenna. Through analyzing the detection performance and the covert rate, we prove that a positive covert rate is achievable with a certain number of antennas in Alice. Numerical results are shown to illustrate the correctness of our results and the performance of MISO covert communication.

Abstract The design of dynamic wireless networks assisted by reconfigurable intelligent surfaces (RIS) mounted on aerial platforms (RIS‐APs) is conceived, where the connection status among users and RIS‐APs are selected according to the average channel quality dynamically and timely. Taking into account the time‐varying selection status and the mobility of users, we construct a long‐term dynamic process. The goal is to minimize the time‐averaged power consumption under the requirements of the time‐averaged minimum rate for users as well as the constraint of the maximum transmit power for the base station (BS), via jointly optimizing the active beamforming at the BS and passive beamforming at RIS‐APs. With the aid of Lyapunov concept‐based drift‐plus‐penalty (DPP) algorithm, the long‐term optimization problem is transformed into short‐term sub‐problems related to each other at each frame. Subsequently, the fractional programming method based on Lagrangian dual theory is applied to derive the solutions for active‐passive beamforming in a closed form. Finally, simulation results validate the convergence and effectiveness of the proposed algorithm.

This work employs an unmanned aerial vehicle (UAV) as a jammer to aid a covert communication from a transmitter Alice to a receiver Bob, where the UAV transmits artificial noise (AN) with random power to deliberately create interference to a warden Willie. In the considered system, the UAV’s trajectory is critical to the covert communication performance, since the AN transmitted by the UAV also generates interference to Bob. To maximize the system performance, we formulate an optimization problem to jointly design the UAV’s trajectory and Alice’s transmit power. The formulated optimization problem is non-convex and is normally solved by a conventional iterative (CI) method, which requires multiple approximations based on Taylor expansions and an initialization on the UAV’s trajectory. In order to eliminate these requirements, this work, for the first time, develops a geometric (GM) method to solve the optimization problem. By analyzing the covertness constraint, the GM method decouples the joint optimization into optimizing the UAV’s trajectory and Alice’s transmit power separately. Our examination shows that the GM method can significantly outperform the CI method in terms of achieving a higher average covert rate and the complexity of the GM method is lower than that of the CI method.

In this paper, to obtain a higher information rate in the block fading channel, we propose an LDL-decomposition-based linear precoded faster-than-Nyquist (FTN) signaling with power allocation. We further propose an LDL-decomposition-based linear precoded FTN signaling with truncated power allocation (LDL-TPA-FTN) to extend the application range of the acceleration factor. Moreover, we derive the corresponding maximum average information rate, outage probability, and outage capacity of the proposed schemes. Also, the complexity of the proposed schemes is analyzed theoretically and compared with classic ones, which shows the proposed schemes have lower complexity. Numerical results show that the proposed schemes have higher outage capacity and achievable rate than Nyquist signaling in the block fading channel. Also, the proposed schemes can reduce the incurred inter-symbol interference (ISI) and have the same application range of the acceleration factors as the singular value decomposition-based FTN. Moreover, a trade-off should be made between the side-lobe suppression of the power spectral density of the transmit signal and threshold selection in LDL-TPA-FTN to obtain higher performance gain for small acceleration factors.

This paper considers the problem of covert communication over a multiple-input single-output channel (MISO) in the case of the imperfect channel state information. The transmitter wants to communication with the receiver while the eavesdropper detects the existence of the transmission. We first study the scenario that the accurate channel state information (CSI) is known and show that the condition achieving the positive rate by the beamforming vector. However, because of the non-cooperation between the transmitter and the eavesdropper, we consider the CSI is imperfect. There are estimation errors for the transmitter. When the positive rate can not be achieved, the objective is to maximize the communication rate. We discuss the deterministic bounded CSI errors for the covert communication. Robust beamforming and the transmitter power is investigated, which can be transformed as a second order cone programming (SCOP) problem. We consider the line-of-sight (LoS) channel and the linear array antenna model. Comparing with the perfect CSI, the results show that the strategy of the transmitter is more conservative under the imperfect CSI. The capacity declines by the estimation errors.

This paper investigates the long-term effectiveness and stability of an integrated unmanned aerial vehicles (UAV)-intelligent reflecting surface (IRS) relaying dynamic system in the context of time-varying system states. Consequently, a dynamic optimization problem is constructed to minimize the frame-average transmit power by joint active beamforming at the base station (BS) and passive beamforming at the IRS under frame-average rate constraints. The original problem as an infinite-horizon time-average one can be solved by introducing the drift-plus-penalty (DPP) algorithm and then the optimal active beamforming and passive beamforming can be obtained in an iterative manner. Simulation results demonstrate the theoretical analysis and assess the performance of the dynamic system.

Internet of Things (IoT) as a prospective platform to develop mobile applications, is facing with significant challenges posed by the tension between resource-constrained mobile smart devices and low-latency demanding applications. Recently, mobile edge computing (MEC) is emerging as a cornerstone technology to address such challenges in IoT. In this paper, by leveraging social ties in human social networks, we investigate the optimal dynamic computation offloading mode selection to jointly minimize the total tasks' execution latency and the mobile smart devices' energy consumption in MEC-aided low-latency IoT. Different from the previous studies, which mostly focus on how to exploit social tie structure among mobile smart device users to construct the permutation of all the feasible modes, we consider dynamic computation offloading mode selection with social awareness-aided network resource assignment, involving both the computing resources and transmit power from heterogeneous mobile smart devices. On the one hand, we formulate the dynamic computation offloading mode selection into the infinite-horizon time-average renewal-reward problems subject to time average latency constraints on a collection of penalty processes. On the other hand, an efficient solution is also developed, which elaborates on a Lyapunov optimization-based approach, i.e., drift-plus-penalty (DPP) algorithm. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed dynamic social-aware computation offloading mode selection method considering different configurations of the IoT network parameters.

Linear frequency modulated (LFM) signal is widely applied in many fields for its excellent characteristic of long time interval and wide frequency band. The chirp rate is the key parameter in LFM signal. The traditional search-based chirp rate estimation algorithms have the contradiction between estimation performance and complexity. The phase-based algorithms have good estimation performance in the case of high SNR, but the complexity is high. In order to solve these problems, a new chirp rate estimation algorithm by multiple discrete polynomial phase transform (DPT) and weighted combination is proposed. In this algorithm, the chirp rate estimation is simplified to multiple frequency estimations by multiple DPT. Then, in frequency estimation, a new unbiased interpolator is proposed to eliminate the contradiction of search-based algorithms. Finally, the multiple estimates are combined through the optimal weighting factors which are obtained by theoretical derivation to minimize the root mean square error. Both theoretical analysis and simulation results demonstrate that the proposed algorithm bears a relatively low complexity and its estimation performance basically coincides with the Cramer-Rao lower bounds and has no error floor, which is much better than the existing algorithms.

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.

It is known that random network coding (RNC) can be used to improve the performance of wireless multicast networks. However, in the delay sensitive applications, no useful information can be recovered if a user cannot collect a full set of the encoded packets. This becomes more severe for multicast hard deadline constrained prioritized data because of the delivery time limitation and packet interdependency. Meanwhile, the performance of a multicast network is also limited by its bottleneck user(s) due to the heterogeneity of the underlying physical channels. Accordingly, we propose a cross-layer transmission scheme that utilizes beamforming at physical layer and adaptive RNC (ARNC) at network layer to maximize the overall network throughput. Under this joint-design, a smart antennas array that operates the beamforming is used to dynamically allocate the transmitting power among users, and the ARNC is adopted to achieve the network coding gain. In this way, the performance of each user is well balanced and the overall network throughput is increased observably. Furthermore, we propose a sample-based feedback scheme to reduce the system overhead. The analytical and simulation results are shown that the throughput of the network has increased by 30%~40% under our schemes compared with other RNC-based schemes.

Since it is challenging to position the noncooperative receiver (Rx), especially when the backward frequency band of the Rx cannot be obtained by the anchors. In this paper, we propose a novel noncooperative Rx positioning method using full-duplex relay technique, where the anchors act as full-duplex relays for the Rx. It can trigger the close-loop-power-control (CLPC) between the transmitter (Tx) and the Rx, which contains the information of the Rx's location. By measuring the received signal from the Tx, the anchors can estimate the location of the Rx. Simulation results demonstrate the performance of the proposed Rx positioning method, and the root-mean-square-error (RMSE) can reach about 30%, which is similar to the conventional Tx positioning using received-signal-strength (RSS).

In underlay heterogeneous networks (HetNets), the distance between a macro base station (MBS) and a macro user (MU) is crucial for a small-cell based station (SBS) to control the interference to the MU and achieve the coexistence. To obtain the distance between the MBS and the MU, the SBS needs a backhaul link from the macro system, such that the macro system is able to transmit the information of the distance to the SBS through the backhaul link. However, there may not exist any backhaul link from the macro system to the SBS in practical situations. Thus, it is challenging for the SBS to obtain the distance. To deal with this issue, we propose a median based (MB) estimator for the SBS to obtain the distance between the MBS and the MU without any backhaul link. Numerical results show that the estimation error of the MB estimator can be as small as $4\%$.

This paper investigates the performance of amplify-and-forward (AF) cognitive relay networks with partial relay selection (PRS) in a spectrum-sharing context. The PRS schemes can be divided into two different types: source-relay PRS and relay-destination PRS, which are respectively called SR-PRS and RD-PRS for short. Different from most of the existing studies which only consider interference power constraint on secondary source and relay nodes, transmit power constraint on secondary relay nodes is further considered in our work. In this condition, we derive the lower bound on symbol error rate and the upper bound on ergodic capacity of the two schemes aforementioned over Rayleigh fading channels, which has never been studied before. According to the analyses, the power constraints and the number of secondary relay nodes are the main factors affecting the system performance. Furthermore, the performances of the two schemes are compared by simulations and the correctness of the analytical results are verified by Monte Carlo simulations.

In this paper, we model the cognitive radio network with channel reservation for primary users using two-dimensional continuous Markov process. Based on this model, we propose an algorithm to maximize the achievable arrival rate of secondary users (SUs) under specific grade of service (GoS) constraints. Moreover, to reduce the computational complexity of the algorithm, the closed-form expressions of the steady-state probabilities are derived using an approximate approach. Moreover the formulas for the blocking probability and the dropping probability of SUs are obtained. Finally, simulations are conducted to verify the accuracy of our approximation and the correctness of the proposed algorithm.

SUMMARY In cognitive radio networks (CRNs), considering the randomness of primary users' (PUs) arrival and the nonideality of spectrum sensing performed by secondary users (SUs), the collisions between PUs and SUs are unavoidable. Frequent occurrences of collisions will strongly degrade PUs' and SUs' QoS. Therefore, collision statistics, such as the average number of collisions, is a very important performance metric in CRNs. If collision statistics are not considered in the scheduling scheme of the CRNs, some SUs will meet more collisions than the others, which cause unfair experiences among the SUs. Therefore, a fair scheduling scheme based on collision statistics is proposed in this paper, which can improve fairness across all SUs. First, the number of collisions is defined as an important fairness metric for each SU, and the scheduler dynamically adjusts the priorities of the SUs by periodically counting each SU's collision number. Then, a prediction algorithm, based on the continuous‐time Markov chain model, is proposed to predict the idle probabilities of the available channels in the next slot. Considering both the priorities of the SUs and the idle probabilities of the available channels, a rational scheduling scheme will be achieved finally. Assuming the ordered hunt scheduling scheme applied by the primary system, the simulation results show that the proposed scheme can significantly improve the fairness across all SUs with little impact on spectrum utilization. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we investigate the joint optimization of sensing and power allocation for cooperative relay in cognitive radio networks (CRNs). Specifically, in the sensing time slot, the source secondary user (SU) and the relay SUs individually sense the channel, and then the relay SUs send their sensing results to the source SU, in which a fusion rule is employed to determine whether the primary user (PU) is idle or not on the channel. In the data transmission slot, the selective amplify-and-forward (S-AF) cooperative relay scheme is considered to assist the source SU for data transmission. By jointly considering the two slots, the optimization problems are respectively formulated to maximize the CRN's average throughput and minimize the outage probability of secondary transmission under the average transmit power constraint of SUs and the average interference power constraint of PU. The optimal algorithms are developed to acquire the optimal sensing time and power allocation. Moreover, in the S-AF scheme, a relay selection scheme is also considered in the optimization problem. Finally, we provide simulation results to validate our proposed algorithms.

SUMMARY In an opportunistic spectrum sharing system, secondary users (SUs) opportunistically access the white space spectrum that is not occupied by the primary user (PU). Some analytical performance models have been available based on Markov chain modeling. In these models, SUs and PUs access the spectrum by randomly selecting the available channels with equal probability. However, how SUs and PUs use the spectrum are controlled by the access strategy designed in their MAC layer in an ad‐hoc network or centralized radio resource management layer in an infrastructure‐based network. To analyze the grade of service (GoS) of the secondary system under consideration for the access strategy, we propose an access rule transition matrix to model the access behavior of radio resource management, and apply it into the continuous‐time Markov chain model. It was proved that the proposed model is equivalent to the original models assuming the random access strategy by simulation. Moreover, we analyzed the GoS performance of the secondary system by assuming an ordered hunt access strategy. The results showed that the GoS performance of secondary systems can be improved greatly if it knows the spectrum access strategy of the primary system. Copyright © 2011 John Wiley & Sons, Ltd.

Cooperative spectrum sensing (CSS) is a promising technology in cognitive radio (CR) networks. Among the many existing CSS methods, the cooperation in a single channel is studied extensively. However, CR networks usually deal with multiple channels, therefore multi-channel CSS needs be studied. In this paper, we investigate the problems that how to optimally assign secondary users (SU) to cooperatively sense multiple channels and how to optimally set the sensing time and sensing thresholds. An optimization problem of multi-channel CSS is formulated to maximize the average throughput of CR networks subject to the constraints of probability of detection for each channel. An exhaustive algorithm and a greedy algorithm are proposed to obtain the optimal solutions of the optimization problem. Finally, both analytical and numerical results are presented to demonstrate the effectiveness of our proposed algorithms. It is also shown that the greedy algorithm with a low complexity achieves the same performance as the exhaustive algorithm.

We present a GPS-enabled channel sounding platform for measuring both vehicle-to-vehicle and vehicle-to- roadside wireless channels. This platform was used to conduct an extensive field measurement campaign involving vehicular wireless channels across a wide variety of speeds and line-of-sight conditions. From the data, we present statistical characterizations of several classes of these channels at 5.9 GHz. This analysis suggests that while the proposed DSRC standard may account for Doppler and delay spreads in vehicular channels, large packets may face higher error rates due to time-varying channels.

We consider a cognitive network which is operating with licensed networks simultaneously. One of the major concerns lies in the fact that cognitive networks may cause harmful interference to licensed users. This paper focuses on a joint channel and power allocation scheme that can both protect licensed users and meet the quality of service (QoS) of cognitive radio users with fairness. The problem can be formulated as a nonlinear programming. For reducing complexity in obtaining optimal channel and power allocation scheme, we propose a two-step suboptimal scheme that can achieve good performance with lower complexity. In the first step, resources, such as channels and powers, are allocated by cognitive radio base station (CRBS) based on fairness and QoS requirements to get the maximum available resources of cognitive radio customer premises equipment (CRCPE). In the second step, to get the final resource and reduce algorithm complexity, the allocation task is accomplished by each CRCPE simultaneously, rather than accomplished by CRBS in a serial order. Theoretical analysis and simulation results show that our scheme can support the QoS of CRCPEs with both lower power consumption and fair resource allocation.

In this paper, we address the problem of channel estimation in time-variant multi-path environment in Orthogonal frequency division multiplexing systems(OFDM). Based on the assumption that the channel varies in a linear fashion during an OFDM block duration, a novel iterative channel estimation arithmetic with noise and interference suppression using pilot tones is investigated. Through iterative noise and interference suppression to determine the positions of channel taps, then we compute the time averages and slopes of that to get the time domain channel matrix, so such method can improve the precision of the estimation. To increase the spectral efficiency and reduce the computational complexity, assuming the positions of channel taps are not changed in one frame duration, a frame based on such estimation scheme is presented. Along with the channel estimation technique, we also analyze the optimum pilot tones placement. Theoretical analysis and simulation results show that our assumption is reasonable, and the proposed channel estimation arithmetic has a good performance with low computational complexity and high spectral efficiency.