This paper proposes a novel dual-intelligent reflecting surface (IRS) aided interference-limited simultaneous wireless information and power transfer (SWIPT) system with independent power splitting (PS), where each receiving antenna applies different PS factors to offer an advantageous trade-off between the useful information and harvested energy.We separately establish the near- and hybrid-field channel models for IRS-reflected links to evaluate the performance gain more precisely and practically. Specifically, we formulate an optimization problem of maximizing the harvested power by jointly optimizing dual-IRS phase shifts, independent PS ratio, and receive beamforming vector in both near- and hybrid-field cases. In the near-field case, the alternating optimization algorithm is proposed to solve the non-convex problem by applying the Lagrange duality method and the difference-of-convex (DC) programming. In the hybrid-field case, we first present an interesting result that the AP-IRS-user channel gains are invariant to the phase shifts of dual-IRS, which allows the optimization problem to be transformed into a convex one. Then, we derive the asymptotic performance of the combined channel gains in closed-form and analyze the characteristics of the dual-IRS. Numerical results validate our analysis and indicate the performance gains of the proposed scheme that dual-IRS-aided SWIPT with independent PS over other benchmark schemes.

The recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation, which is a delay-Doppler (DD) domain multi-carrier (DDMC) modulation scheme based on the DD domain orthogonal pulse (DDOP), is studied. We first revisit the linear time-varying (LTV) channel model for the wireless channel, and review the conventional multi-carrier (MC) modulation schemes and their design guidelines for both linear time-invariant (LTI) and LTV channels. We then focus on the representation of the LTV channel in an equivalent sampled DD (ESDD) domain, and propose an impulse-function-based transmission strategy for the ESDD channel. Next, we take an in-depth look into the DDOP and show that it achieves orthogonality with respect to the fine time and frequency resolutions in the ESDD domain thusbehaves likean impulse function. This allows us to unveil the unique input-output relation of the resultant ODDM modulation over the ESDD channel. We point out that the conventional MC modulation design guidelines based on the Weyl-Heisenberg (WH) frame theory can be relaxed without compromising its orthogonality or violating the WH frame theory. More specifically, for a practical communication system with bandwidth and duration constraints, MC modulation signals can be designed considering so-calledlocal or sufficient (bi)orthogonality,which refers to the (bi)orthogonality among a WH subset for the MC signal within a specific bandwidth and duration. This is different from the conventional MC modulation waveform design guidelines (such as for orthogonal frequency division multiplexing and orthogonal time frequency space) based on the global (bi)orthogonality, which is the (bi)orthogonality among a WHfull setcorresponding to the MC signal occupying the entire TF domain. This novel design guideline could potentially open up opportunities for developing future waveforms required by new applications such as communication systems associated with high delay and/or Doppler shifts, as well as integrated sensing and communications.

In the study of packet-level random linear network coding (RLNC) in wireless broadcast, RLNC over GF(2L) is known to asymptotically achieve the optimal completion delay with increasingL. Effective utilization of guessing random additive noise decoding (GRAND) at the physical layer can help leverage RLNC packets to generate syndromes so as to reduce packet erasure probabilities and thus further improve the completion delay performance. Prior to this work, only a few studies investigated GRAND-assisted RLNC and they restricted to GF(2)-coding. In this paper, we first provide a general framework to formulate the decoding process of GRAND-assisted RLNC over GF(2L) forL≥ 1. Even for GRAND-assisted GF(2)-RLNC, the formulation is more complete than previous considerations in the sense that it takes the a priori information of which packets have errors into consideration. Moreover, we propose a novel GRAND-assisted GF(2L)-RLNC scheme whose computational overhead introduced by GRAND is negligible. In particular, a subset of GF(2L) is carefully designed for random coding coefficient selection. For the novel scheme, we theoretically derive lower bounds on the distribution as well as an upper bound on the expected value of the completion delay. Numerical results demonstrate not only a reduction in average completion delay for the novel scheme, but also the advantage of random coding coefficient selection from the specially designed subset for GRAND-assisted RLNC schemes.

In this work, we propose the orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation with frequency modulated continuous wave (FMCW) (ODDM-FMCW) waveform to enable integrated sensing and communication (ISAC) with a low peak-to-average power ratio (PAPR). We first propose a square-root-Nyquist-filtered FMCW (SRN-FMCW) waveform to address limitations of conventional linear FMCW waveforms in ISAC systems. To better integrate with ODDM, we generate SRN-FMCW by embedding symbols in the DD domain, referred to as a DD-SRN-FMCW frame. A DD chirp compression receiver is designed to obtain the channel response efficiently. Next, we construct the proposed ODDM-FMCW waveform for ISAC by superimposing a DD-SRN-FMCW frame onto an ODDM data frame. A comprehensive performance analysis of the ODDM-FMCW waveform is presented, covering peak-to-average power ratio, spectrum, ambiguity function, and Cramér-Rao bound for delay and Doppler estimation. Numerical results show that the proposed ODDM-FMCW waveform delivers excellent ISAC performance in terms of root mean square error for sensing and bit error rate for communications.

This paper proposes a new orthogonal delay-Doppler division multiplexing (ODDM) system, which is designed to operate without the need of using a cyclic prefix (CP) in the transmitted signals. The CP-free ODDM is enabled by exploiting the unique structures of the ODDM prototype pulses, which are constructed through repetitions of finite-duration elementary pulses. The system eliminates the need of CP by designing a new receiving filter through temporally extending the transmission prototype pulse. The elementary pulse repetition at the transmitter along with the temporal filter extension at the receiver introduce a wrap-around effect in the equivalent channel spreading function in the dealy-Doppler (DD) domain. The wrap-around effect leads to a block-circulant-like structure of the DD-domain channel matrix that is the same as conventional ODDM systems with CP. Thus CP-free ODDM can employ the same receiver as conventional ODDM systems, yet it yields a higher energy efficiency as no energy needs to be allocated for CP symbols. The wrap-around effects are theoretically demonstrated by deriving the exact analytical expressions of the cross-ambiguity functions of practical transmission and receiving prototype pulses. In addition, theoretical analysis of the cross-ambiguity function verifies that the CP-free ODDM waveforms satisfy local bi-orthogonality in the delay and Doppler domains. This bi-orthogonality property along with the wrap-around effects of the channel spreading function enables the design of a low complexity iterative receiver, which performs interference cancellation and coherent matrix combining (CMC) in the delay domain and minimum mean squared error (MMSE) detection in the Doppler domain. The proposed receiver can collect the diversity in both the delay and Doppler domains while simultaneously suppressing the negative impacts of DD-domain intersymbol interference (ISI) introduced by the doubly-selective fading channels.

In this paper, we investigate the fundamental trade-off between communication and sensing performance ofad hocintegrated sensing and communication (ISAC) wireless networks. Specifically, we consider thatnnodes are randomly located in an extended network with areanand transmit ISAC signals. Under the pure path loss channel gain model and the condition that the transmission power scales according to the communication distance, we fully characterize the optimal scaling law trade-off between throughput and sensing distance by proposing an achievable scheme and proving its converse. Our results can be interpreted as follows: by reducing the throughput by a factor of a function ofn, the sensing range order improves according to the same function ofn, raised to the power of the ratio between the path loss factors in communication and sensing. We prove that the same result also holds true for ISAC networks with random fading, despite the uncertainty on the connectivity and power level created by random fading. In addition, we show that the scaling law tradeoff cannot be improved by allowing the transmission power and communication distance to scale freely. To the best of our knowledge, this is the first work formally formulating and characterizing the communication and sensing performance scaling law tradeoff ofad hocISAC networks.

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently emerged as a promising paradigm for ensuring reliable communications in doubly-selective channels. This work investigates the spectra and orthogonality characteristics of analog (direct) and approximate digital implementations of ODDM systems. We first determine the time and frequency domain representations of the basis functions for waveform in analog and approximate digital ODDM systems. Thereafter, we derive their power spectral densities and show that while the spectrum of analog ODDM waveforms exhibits a step-wise behavior in its transition regions, the spectrum of approximate digital ODDM waveforms is confined to that of the ODDM sub-pulse. Next, we prove the orthogonality characteristics of approximate digital ODDM waveforms and show that, unlike analog ODDM waveforms, the approximate digital ODDM waveforms satisfy orthogonality without the need of additional time domain resources. Additionally, we examine the similarities and differences that implementations of approximate digital ODDM share with the other variants of DD modulations, focusing on the domain changes the symbols undergo, the type of pulse shaping and windowing used, and the domains and the sequence in which they are performed. Finally, we present numerical results to validate our findings and draw further insights.

In this paper, we investigate the design of low-complexity, high-performance transmission and detection schemes for the emerging affine frequency division multiplexing (AFDM) waveform in doubly selective channels. We first propose the time-domain zero-padding (TZP-)AFDM transmission frame, which simplifies time-domain input-output (IO) relation, while preserving the spectral efficiency and retaining a relatively compact discrete affine Fourier transform (DAFT)-domain IO relation via aphase-rotated overlap-add(PROLA) technique. Based on the derived IO relations, we develop a novel two-stage detector. In the first stage, low-complexity, initial linear minimum mean square error (MMSE) detection is performed in the time domain, with statistics efficiently computed and passed to the second stage. For the second stage, we introduce several iterative MMSE detection schemes that offer a trade-off between error performance and detection complexity. In particular, our cross-DAFT-and-time-domain iterative detector enhances error performance while exploiting the time-domain IO relation to reduce complexity. As an alternative, a DAFT-domain iterative detector is proposed for the second stage utilizing the DAFT-domain IO relation derived, which further improves the error performance albeit with increased complexity. Simulation results show that the proposed TZP-AFDM and detectors achieve superior error performance, higher spectral efficiency and lower computational overhead compared to existing approaches.

This paper introduces a novel method to estimate doubly selective channels for single-carrier (SC) communication systems in high-mobility environments. We propose a transmission frame structure featuring adaptive pilot insertions tailored to the Doppler spread, and an adaptive threshold-based channel estimation method that leverages the delay-Doppler (DD) domain channel characteristics. To mitigate the peak-to-average power ratio (PAPR) and reduce pilot overhead, we further generalize the frame structure from single-pilot scheme to multiple-pilot scheme and propose the design criteria for the pilot. By employing zero correlation zone (ZCZ) sequences with ZCZ sizes equal to or greater than the normalized maximum Doppler shift of the channel, we show that the multiple-pilot scheme effectively enhances estimation accuracy without inducing significant PAPR and pilot overhead increment. For both schemes, we also derive the asymptotically optimal thresholds for DD channel tap detection to minimize mean square error (MSE) of the estimation. Moreover, the closed-form approximation of Cramer-Rao Lower Bound for the channel estimation error is also derived, serving as a performance benchmark. Comparative analysis demonstrates that our proposed method significantly outperforms conventional interpolation techniques in estimating doubly selective fading channels, thereby illustrating its efficacy and practical utility in SC systems.

Delay-Doppler (DD) domain modulation such as orthogonal DD division multiplexing (ODDM) has been recently explored for communications over doubly selective channels. This paper derives equivalent sampled (on-grid) DD (ESDD) channel models for the effective discrete-time channels in ODDM systems over highly-spread off-grid physical channels with delay and Doppler shifts that can exceed the subpulse spacing and subtone spacing, respectively, of the DD orthogonal pulse (DDOP). The derived ESDD models account for i) off-grid delay and Doppler shifts present in practical physical channels, ii) sample-wise pulse shaping at the transmitter, and iii) matched filtering and windowing at the receiver. We then investigate the supports of the ESDD models and their implications on the input-output (IO) relation of ODDM adopting more general pulses and windows over highly-spread physical channels. In particular, we show that the digital sequences of ODDMcouple finelywith on-grid discrete-time DD channels, which leads to compact IO relation. We also analyze the folding and aliasing of the ESDD channel and their influence on the fading effect and predictability of effective channels experienced by the DD-domain symbols of ODDM. Based on the results, we identify conditions under which the ESDD channels can be estimated directly from embedded DD-domain pilots in a single ODDM frame. Finally, numerical results are provided to further demonstrate the findings of this paper.

Numerous multicarrier modulation schemes have been proposed recently to enhance the performance in narrowband doubly dispersive channels for emerging high-mobility applications. However, the ultra-reliable modulation framework in wideband linear time-varying (LTV) channels remains an open problem, where the time dilations and contractions brought by the high mobility cannot be ignored for the baseband signal to obtain the constant Doppler shift across the whole transmission band. To solve this problem, we propose the hyperbolic frequency multicarrier (HFMC) waveform in this paper based on the inspiration from affine frequency division multiplexing (AFDM) modulation, where the delay and Doppler shift are absorbed into a 1D shift in the affine domain to provide a compact characterization of doubly dispersive discrete-time channels. By adopting the passband representation of wideband LTV channels and hyperbolic frequency modulated (HFM) signals, we reveal that the Doppler scaling factor brought by the relative mobility can be absorbed into an equivalent delay. The basic principle of HFMC modulation is established by investigating the approximate orthogonality among HFMC subcarriers, which are generated from a basic HFM signal by utilizing uniformly spaced equivalent delay. The spectrum of HFMC subcarriers is also analyzed to evaluate the system capacity, where the overlapping nature in the frequency domain can be observed. The input-output characterization in wideband LTV channels is then executed to confirm the 1D integration of time delay and Doppler scaling factor for each path, which demonstrates the ability to exploit potential multipath diversity. The parameter optimization based on the input-output relation and spectrum analysis is finally developed to balance the efficiency and reliability. Numerical results demonstrate the excellent bit error rate (BER) performance of the proposed HFMC waveform in wideband LTV channels.

In this paper, we present a novel orthogonal Doppler frequency modulation (ODFM) to meet the challenging requirements of high-mobility communications. Firstly, we revisit the basic concepts of multicarrier modulations (MCMs) and discuss the capability to explore both time and frequency diversity in doubly selective channels for the most popular MCMs. We then illustrate how ODFM maps information symbols into Doppler-frequency domain and converts them into time-delay domain for transmission, where we show ODFM has the potential to exploit both delay space (frequency) and Doppler space (time) diversity. Specifically, we propose two types of ODFM schemes using the conventional time-frequency domain orthogonal pulses (TFOP-ODFM) and the novel delay-Doppler domain orthogonal pulses (DDOP-ODFM). We give exact channel input-output relations (IORs) in the Doppler-frequency domain of these two ODFM schemes and show their properties in efficient channels, implementation complexity, and others. Subsequently, we simulate the performance of these ODFM schemes with other MCMs, such as orthogonal frequency division multiplexing (OFDM), and orthogonal time frequency space (OTFS) modulation. In particular, we investigate the performance of the TFOP-ODFM in the physical downlink shared channel of the Five-Generation New Radio (5G NR) link, where TFOP-ODFM show better performance than OFDM and OTFS.

In this paper, we propose a novel transmission and reception scheme based on zero correlation zone (ZCZ) sequences, aiming to tackle the compelling challenge of ultra-reliable communications in doubly selective channels with low-complexity receivers. Our input-output relation analysis reveals that the proposed transmission scheme enables the receiver to extract independent delay-impaired copies of the transmitted signal. Leveraging this property, we design a single-tap equalization that effectively exploits diversity of the channel with minimal inter-symbol interference. To further enhance practicality, we develop low-complexity transmitter and receiver implementations with a comparable complexity to OFDM and delay-Doppler multicarrier (DDMC) systems. Simulation results show that our approach outperforms OFDM and DDMC repetition coding schemes in error performance, while maintaining a low computational overhead without iterative detections.

The recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation has been demonstrated to enjoy excellent reliability over doubly-dispersive channels. However, most of the prior analysis tends to ignore the interactive dispersion caused by the wideband property of ODDM signal, which possibly leads to performance degradation. To solve this problem, we investigate the input-output relation of ODDM systems considering the wideband effect, which is also known as the Doppler squint effect (DSE) in the literature. The extra delay-Doppler (DD) dispersion caused by the DSE is first explicitly explained by employing the time-variant frequency response of multipath channels. Its characterization is then derived for both reduced cyclic prefix (RCP) and zero padded (ZP)-based wideband ODDM systems, where the extra DD spread and more complicated power leakage outside the peak region are presented theoretically. Numerical results are finally provided to confirm the significance of DSE. The derivations in this paper are beneficial for developing accurate signal processing techniques in ODDM-based integrated sensing and communication systems.

The orthogonal time frequency space (OTFS) modulation is a promising technique to provide reliable communications in high-mobility scenarios. However, the performance analysis for coded OTFS systems is not available in the literature. This paper investigates the extrinsic information transfer (EXIT) behaviour of LDPC coded OTFS systems with message passing (MP) detection. Different from conventional EXIT analysis, which is normally obtained by Monte Carlo simulation, the exact distribution of the extrinsic information for MP detection is presented in this paper. Then, the EXIT function for the LDPC decoder is given with the prior information of the MP detector, which illustrates the convergence behaviour of the LDPC coded OTFS systems. Finally, an algorithm is proposed for calculating the decoding threshold for LDPC coded OTFS modulation, which can be used to design LDPC codes for OTFS systems. Numerical results verify the accuracy of the EXIT analysis, and the 5G NR LDPC code optimised by the proposed method demonstrates 1 dB to 1.5 dB performance gains over the original codes.

Deep learning (DL) has emerged as a key enabler for unlocking the potential of integrated sensing and communication (ISAC). Despite recent progress, current DL methods primarily handle sensing and communication as independent tasks, overlooking potential performance enhancement through a joint approach. Moreover, existing methods rely on fully annotated data for training, which is often challenging to obtain, especially in multi-task scenarios where labeled data may be scarce or only exist for a subset of tasks. Motivated by these shortcomings, this paper proposes a novel scheme, MTL-DFM, to enable simultaneous sensing and communication with partially labeled training data, which leverages multi-task learning (MTL) and a diffusion model (DFM). In particular, we introduce an initial feature extraction module (IFEM) to jointly capture shared information across tasks and explore inherent cross-task connections for enhanced feature extraction. Next, we design a signal denoising with incomplete labeling (SDIL) module to effectively remove noise from extracted information and construct comprehensive feature representations for all tasks with partially labeled datasets, which is difficult for conventional DL methods. Simulation results verify the superior performance offered by MTL-DFM over prior state-of-the-art methods.

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently been proposed as a promising paradigm for communications in doubly-selective channels. In this work, we propose a novel ODDM with frequency modulated continuous wave (FMCW) (ODDM-FMCW) signal to enable integrated sensing and communication (ISAC) with a low peak-to-average power ratio (PAPR). We first propose the DD-domain embedded root-raised-cosine filtered FMCW (DD-RRC-FMCW) signal, where digital chirp compression is introduced for efficient radar signal processing. By superimposing this signal onto an ODDM data frame, we obtain the ODDM-FMCW signal for the proposed ISAC system. Next, we introduce a modified orthogonal matching pursuit algorithm for data-aided sensing. The algorithm is then combined with the soft successive interference cancellation with minimum mean square error detector to perform joint channel estimation and data detection. Our numerical results show that the proposed ODDM-FMCW signal delivers excellent normalized mean square error and bit error rate performance for ISAC.

The orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has been proposed as a promising paradigm for ensuring reliable communications in doublyselective channels. Although ODDM functions as a multicarrier modulation on the DD plane, academia and industry are more accustomed to understanding and altering the time and frequency resources of signals and waveforms. Recognizing this, in this work, we investigate the time and spectral occupancies of the transmitted signals in both analog (direct) and digital (approximate) implementations of ODDM systems. We begin by explicitly determining the basis functions for both ODDM systems in time and frequency domains, and highlight their phase term difference in the time domain and their envelope difference in the frequency domain. We then derive the spectral responses of their transmit signals. We reveal that while the spectral response of signals in analog ODDM systems exhibits a step-wise behavior in its transition regions, the spectral response of signals in digital ODDM systems is confined to that of the ODDM sub-pulse. Finally, through numerical results, we verify our findings and show that out-of-band-emission of ODDM systems can be further improved by increasing the duration of the ODDM sub-pulse.

In this work, we investigate computational complexity reduction for the detection of the emerging Orthogonal DelayDoppler Division Multiplexing (ODDM), where existing time domain detection schemes become computationally demanding when the channel delay spread is large. We start by deriving the frequency domain input-output relation for ODDM over general physical channels. Based on this relation, we develop a frequency domain iterative MMSE detection scheme, which effectively reduces detection complexity in channels with large delay spread. To leverage the unique advantages of both time and frequency domain detections, we further propose an adaptive ODDM detection scheme that selects the optimal detection domain based on channel conditions. Simulation results show that our adaptive time/frequency domain detector reduces computational overhead by multiple folds with minimal impact on error performance or power efficiency, compared to conventional non-adaptive detectors under practical system and channel parameters.

In this paper, we investigate the design of lowcomplexity, high-performance transmission and detection schemes for the emerging affine frequency division multiplexing (AFDM) waveform to combat doubly selective channels. We first introduce a new time domain zero-postfix (TZP) AFDM transmission frame, which simplifies the time domain input-output (IO) relation for AFDM while preserving the spectral efficiency. Based on this IO relation, we design a novel two-stage detector. The first stage performs a low-complexity time domain linear MMSE initial detection. In the second stage, we develop a cross-domain iterative MMSE detector, which utilizes statistics from the first stage, incorporates discrete affine Fourier transform (DAFT) domain symbol constraints to enhance error performance, and exploits the time domain IO relation for complexity reduction. Simulation results show that the proposed TZP-AFDM and detector significantly outperform existing methods by achieving superior error performances, higher spectral efficiency and lower computational overhead.

This paper examines orthogonal delay-Doppler (DD) division multiplexing (ODDM) systems over highly-spread channels characterized by delay and Doppler shifts which can exceed, respectively, the sub-pulse separation and sub-tone separation of the DD orthogonal pulse employed by ODDM. We first derive an equivalent sampled DD (ESDD) channel model with on-grid delay and Doppler shifts specified by the sampling interval and frame duration of the transmission scheme. Our model accounts for off-grid delay and Doppler present in practical channels, samplewise pulse shaping at the transmitter, and matched filtering and windowing at the receiver. By examining their interaction, we show that the time-domain sequences of ODDM implemented approximately (digitally) using discrete Fourier transform (DFT) and inverse DFT (IDFT) and the discrete-time on-grid DD channel are finely coupled, and this leads to compact input-output (IO) relation for ODDM over on-grid channels. We then leverage the ESDD model to describe the IO relation of ODDM adopting more general pulses and windows over highly-spread off-grid channels, which reveals the potential folding (aliasing) of the ESDD channel and its implication on the signal model. We finally present numerical results. It is observed that pulses with shorter duration and windows with lower sidelobes in their spectrum lead to sparser ESDD channels.

Long-range (LoRa) can provide highly energy-efficient and cost-effective communications for low power wide area networks, playing an indispensable role in the Internet of Things (IoT). However, terrestrial LoRa networks cannot guarantee pervasive connectivity, especially in rural and remote areas. To tackle this problem, exploiting LoRa-based low Earth orbit (LEO) satellite IoT has garnered a growing interest in both academia and industry. In this paper, we provide a novel analytical framework based on spherical stochastic geometry (SG) for characterizing the uplink access probability of LoRa-based LEO satellite IoT. For practical modeling, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of near-Earth satellite communications and the unique features of LoRa network are considered to derive closed-form analytical expressions for the uplink access probability. Numerical simulations validate the accuracy of our theoretical analysis and provide insightful guidelines for the practical design and implementation of LoRa-based LEO satellite IoT.

Advanced security solutions are essential for sustainable and autonomous networking in 6G systems. Secure backscatter communications (BSC) are crucial to protecting data integrity and confidentiality against evolving threats in green communication networks. This form of wireless communication involves devices reflecting signals to the source and extends beyond existing security solutions. Physical layer security (PLS) solutions have emerged as a promising avenue for developing adaptive, robust, and lightweight security measures. We propose a unique approach involving beamforming at the multiantenna transceiver, known as the reader, to enhance data confidentiality in multi-tag monostatic backscattering systems. Our innovative framework protects against security breaches due to eavesdroppers and optimizes the sum-secrecy rate among the tags by integrating transceiver beamforming and artificial noise techniques. Given the non-convex nature of the original problem, we propose two efficient solutions: a transmitter design for a given combiner using fractional programming and a closed-form optimal receiver design for a specified precoder configuration. Comprehensive numerical analyses validate the proposed solutions and verify key analytical claims while also quantifying the significant performance gains achieved over benchmark schemes across various system parameters.

Unmanned aerial vehicles (UAVs) are now being used to efficiently collect data from passive backscattering tags and support existing terrestrial links in the Internet of Things (IoT) when these links become overloaded. However, securing UAV-aided backscatter communication (BSC) in non-terrestrial networks is challenging due to the hardware limitations of passive tags. This paper introduces a secure BSC system utilizing a UAV for radio frequency (RF) signal transmission and data collection. Batteryless tags or backscatter devices (BDs) harness these RF signals to communicate with the UAV, even under untrusted scenarios where the BDs are mutually untrusted. We enhance the uplink fair-secrecy rate of the BDs by jointly optimizing the transmit and received beamforming vectors, artificial noise (AN), and power allocation while achieving the energy harvesting requirements and UAV flight constraints. Block coordinate descent (BCD) and fractional programming (FP) algorithms are used to address the non-convexity and obtain a fast converging solution. Simulation results verify the analysis, provide valuable insights, and demonstrate the substantial performance gains of our design for UAV-aided secure BSC in improving the fair-secrecy rate. Specifically, our proposed design achieves 0.19%, 2.08%, and 69.78% higher performance compared to three benchmarks.

Orthogonal Chirp Division Multiplexing (OCDM) is a multi-carrier scheme based on the chirp spread spectrum (CSS) that has been introduced to overcome the limitations of conventional multiplexing techniques. In this paper, we derive the input-output relationship (IOR) for the OCDM scheme over frequency-selective fading channels. We consider two cases: when the guard interval (GI) between successive OCDM symbols is longer than the channel delay spread (sufficient) or not (insufficient). First, we demonstrate that the IOR of OCDM is equivalent to that of the single-carrier (SC) scheme with a sufficient GI. Next, we show that for OCDM with an insufficient GI, the intersymbol interference (ISI) at the receiver follows a Gaussian distribution. We then derive the signal-to-interference-plus-noise ratio (SINR) of the OCDM scheme at the receiver. By leveraging this SINR analysis, we present a novel single-tap linear minimum mean square error (MMSE) based frequency domain equalizer (FDE). Our simulation results, which highlight the significant performance improvement of the proposed MMSE one-tap equalizer, demonstrate that by exploiting derived SINR, its effectiveness in enhancing the performance of OCDM compared with the conventional MMSE receiver. We also evaluate the bit error rate (BER) performance and validate our analysis. Our results provide novel insights into the achievable gains by our interference-aware FDE for OCDM over the existing benchmarks for different values of key system parameters.

In this paper, we investigate the communication and sensing performance tradeoff of ad hoc integrated sensing and communication (ISAC) wireless networks. Specifically, we consider that$n$nodes are randomly located in an extended network with area$n$and transmit ISAC signals. Our goal is to answer the following questions: what is the tradeoff between the throughput and sensing range of an ISAC network and how does it scale with the network size or node numbers? Under the condition that the transmission power scales according to the communication distance, we fully characterize the scaling law tradeoff between throughput and sensing distance by proposing an achievable scheme and proving its converse. Interestingly, our results reveal that by reducing the throughput by a factor of a function of$n$, the sensing range order improves according to the same function of$n$, raised to the power of the ratio between the path loss factors in communication and sensing. We also show that the scaling law tradeoff cannot be improved by allowing the transmission power and communication distance to scale differently. To the best of our knowledge, this is the first work formally formulating and characterizing the communication and sensing performance scaling law tradeoff of ad hoc ISAC networks.

This paper analyzes the formation of minimum-weight codewords (MWCs) in cyclic redundancy check polar (CRC-polar) codes, known for their competitive performance, and demonstrates how the number of MWCs significantly reduces relative to polar codes, which is the reason for the superiority of CRC-polar codes. Inspired by the reason behind the significant reduction of MWCs, we propose a modified reliability-based rate-profile for polarization-adjusted convolutional (PAC) codes, termed Profile-Shifted PAC (PS-PAC) codes, designed to achieve a similar reduction in MWCs through precoding. The results demonstrate a significant improvement in error performance compared to CRC-polar codes, achieving up to a 0.5 dB power gain with short PS-PAC codes. Furthermore, we enhance CRC-Polar codes by leveraging convolutional precoding in PAC codes to implement a continuous deployment (masking) of parity check bits over frozen bits, called Continuous CRC-Polar codes. This approach enhances performance for medium-length codes, with an overall improvement of 0.12 dB.

This paper presents a new class of spatially coupled turbo-like codes (SC-TCs), namely half spatially coupled braided convolutional codes (HSC-BCCs) and half spatially coupled parallel concatenated codes (HSC-PCCs). Different from the conventional SC-TCs, the proposed codes have simpler and deterministic coupling structures. Most notably, the coupling of HSC-BCCs is performed by re-encoding the whole coupling sequence in the component encoder of one time instant, rather than spreading the coupling bits to component encoders of multiple time instants. This simplification not only addresses the window decoding threshold loss issue in existing BCCs, but also allows the proposed codes to attain very close-to-capacity performance with a coupling memory as small as 2. Both theoretical and numerical results are provided to demonstrate the performance advantages of the proposed codes over existing spatially coupled codes.

Backscatter communication (BackCom) is a promising technology that enables ultra-low-power wireless communication by reflecting RF signals. We propose novel Binary polarization Shift Keying (BPolSK) and Differential polarization Shift Keying (DPolSK) in Bistatic BackCom. Here, the backscatter tag modulates the information by changing the polarization state of the incident RF carrier. We derive the closed-form expression for Bit Error Rate (BER) and analyze the performance of BPolSK and DPolSK. Our results compare the performance of BPolSK and DPolSK and verify that they can achieve low BER, demonstrating their reliability for BackCom applications.

Backscatter tag-to-tag networks offer a sustainable and energy-efficient solution for large-scale Internet-of-Things (IoT) applications. In this paper, we propose a novel backscatter tag-to-multiple-tag network protocol based on a dual-phase system, partitioning the operational time into energy-harvesting and backscatter-communication phases. By utilising a multi-antenna reader and jointly optimising the beamforming vectors for the dual-phase problem, our design maximises the sum-throughput and substantially enhances overall system performance. To solve the non-convex transceiver optimisation problem, we derive closed-form solutions for the first phase and employ fractional programming with semidefinite relaxation for the second phase. Simulation results validate the effectiveness of the proposed solution and provide valuable insights for practical deployment, achieving an enhancement of around 3 dB over the benchmarks.

As mobile networks increasingly support sustainable and green Internet of Things (IoT) applications, energy-efficient solutions that address coverage constraints have become paramount. Although backscatter communication (BSC) offers a low-power option for IoT devices, it can suffer from limited coverage. To overcome this, we leverage unmanned aerial vehicles (UAVs) and non-orthogonal multiple access (NOMA) to enhance both coverage and spectral efficiency. Motivated by vehicular communication applications, this paper investigates a NOMA-enabled UAV-assisted BSC framework to maximise system throughput by jointly optimising power allocation and trajectory scheduling. We derive a closed-form solution for the UAV's optimal collection location and apply the Karush-Kuhn-Tucker (KKT) conditions to obtain the power allocation. The numerical and simulation results demonstrate sum-throughput improvements of 620.278% and 7.795% compared to two benchmark schemes, underscoring the potential of our approach for large-scale IoT deployments.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation enhances transmission in high-mobility scenarios but also increases detection complexity, especially when fractional Doppler shifts are present. To tackle these issues, we propose a low-complexity sparse pre-conditioned conjugate gradient-linear minimum mean square error (SPCG-LMMSE) detector, which can obtain the LMMSE solution without needing matrix inversion. Based on the sparsity and quasi-banded structure of the ODDM time-domain equalization matrix, the algorithm utilizes an improved conjugate gradient method to approach the optimal solution quickly. Simulation results demonstrate that the detector can significantly reduce complexity while maintaining excellent performance, with additional gains in multipath and high-order modulation scenarios.

In this paper, we propose a novel phased array with movable antennas (PAMA). By replacing traditional phase shifters with movable antennas, PAMA eliminates insertion loss of phase shifters and enhances antenna performance by leveraging the increased degrees of freedom in antenna positioning. We construct a periodic approximation-based beampattern synthesis (PABS) method. It leverages the approximate periodicity of the PAMA array response to obtain a high-quality initial solution through discrete optimization with quadruple-frequency sampling, which is subsequently refined using projected gradient descent. Simulations demonstrate the effectiveness of the proposed approach for multi-beampattern synthesis and highlight the impact of antenna movement range on synthesis performance. The results show that PAMA-based beampattern synthesis enhances beamforming accuracy and improves efficiency, especially in practical scenarios where digital phase shifters are used.

The orthogonal time frequency space with index modulation (OTFS-IM) offers flexible tradeoffs between spectral efficiency (SE) and bit error rate (BER) in doubly selective fading channels. While OTFS-IM schemes demonstrated such potential, a persistent challenge lies in the detection complexity. To address this problem, we propose the hierarchical mode-based index modulation (HMIM). HMIM introduces a novel approach to modulate information bits by IM patterns, significantly simplifying the complexity of maximum a posteriori (MAP) estimation with Gaussian noise. Further, we incorporate HMIM with the recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation, namely ODDM-HMIM, to exploit the full diversity of the delay-Doppler (DD) channel. The BER performance of ODDM-HMIM is analyzed considering a maximum likelihood (ML) detector. Our numerical results reveal that, with the same SE, HMIM can outperform conventional IM in terms of both BER and computational complexity. In addition, we propose a successive interference cancellation-based minimum mean square error (SIC-MMSE) detector for ODDM-HMIM, which enables low-complexity detection with large frame sizes.

Recently, Long-Range (LoRa) based low Earth orbit (LEO) satellite Internet of Things (IoT) has garnered growing interest from both academia and industry, since it can guarantee pervasive connectivity in an energy-efficient and cost-effective manner. In this paper, we provide a novel spherical stochastic geometry (SG) based analytical framework for characterizing the uplink access probability of LoRa-based LEO satellite IoT system. Specifically, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of LoRa EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of the satellite-to-Earth communications and the unique features of the LoRa network are considered to derive closed-form analytical expressions for the uplink access probability of such a new paradigm. Moreover, the non-trivial impact of the spreading factor, the ED’s density, the orbit altitude, and the satellite effective beamwidth on the system performance is thoroughly investigated. Extensive numerical simulations are conducted, which not only validate the accuracy of our theoretical analysis but also provide useful insights into the practical design and implementation of LoRa-based LEO satellite IoT system.

Orthogonal time frequency space (OTFS) modulation, a delay-Doppler (DD) domain communication scheme exhibiting strong robustness against the Doppler shifts, has the potentials to be employed in LEO satellite communications. However, the performance comparison with the orthogonal frequency division multiplexing (OFDM) modulation and the resource allocation scheme for multiuser OTFS-based LEO satellite communication system have rarely been investigated. In this paper, we conduct a performance comparison under various channel conditions between the OTFS and OFDM modulations, encompassing evaluations of sum-rate and bit error ratio (BER). Additionally, we investigate the joint optimal allocation of power and delay-Doppler resource blocks aiming at maximizing sum-rate for multiuser downlink OTFS-based LEO satellite communication systems. Unlike the conventional modulations relying on complex input-output relations within the Time-Frequency (TF) domain, the OTFS modulation exploits both time and frequency diversities, i.e., delay and Doppler shifts remain constant during a OTFS frame, which facilitates a DD domain input-output simple relation for our investigation. We transform the resulting non-convex and combinatorial optimization problem into an equivalent difference of convex problem by decoupling the conditional constraints, and solve the transformed problem via penalty convex-concave procedure algorithm. Simulation results demonstrate that the OTFS modulation is robust to carrier frequency offsets (CFO) caused by high-mobility of LEO satellites, and has superior performance to the OFDM modulation. Moreover, numerical results indicate that our proposed resource allocation scheme has higher sum-rate than existing schemes for the OTFS modulation, such as delay divided multiple access and Doppler divided multiple access, especially in the high signal-to-noise ratio (SNR) regime.

The orthogonal delay-Doppler division multiplexing (ODDM) modulation is a recently proposed multi-carrier modulation that features a realizable pulse orthogonal with respect to the delay-Doppler (DD) plane’s fine resolutions. In this paper, we investigate the performance of ODDM systems with imperfect channel estimation considering three detectors, namely the message passing algorithm (MPA) detector, iterative maximum-ratio combining (MRC) detector, and successive interference cancellation with minimum mean square error (SIC-MMSE) detector. We derive the post-equalization signal-to-interference-plus-noise ratio (SINR) for MRC and SIC-MMSE and analyze their bit error rate (BER) performance. Based on this analysis, we propose the MRC with subtractive dither (MRC-SD) and soft SIC-MMSE initialized MRC (SSMI-MRC) detector to improve the BER of iterative MRC. Our results demonstrate that soft SIC-MMSE consistently outperforms the other detectors in BER performance under perfect and imperfect CSI. While MRC exhibits a BER floor above$10^{-5}$, MRC-SD effectively lowers the BER with a negligible increase in detection complexity. SSMI-MRC achieves better BER than hard SIC-MMSE with the same detection complexity order. Additionally, we show that MPA has an error floor and is sensitive to imperfect CSI.

Power line communication is a critical component of smart grids, which are vulnerable to eavesdropping. To address this challenge, we investigate a cooperative relay hybrid power line and wireless communication system where multiple eavesdroppers are considered. We propose an elaborate artificial noise (AN)-aided beamforming (BF) scheme to improve physical layer security. Our scheme maximizes the outage-constrained secrecy rate (OCSR) of the legitimate link while restricting the capacity of the eavesdroppers to a reasonable region. However, due to the imperfect channel state information of the wiretap channel and secrecy outage probability constraint, the robust OCSR problem becomes intractable because of the non-concave secrecy objective function and the non-convex constraints. To solve this issue, we utilize semidefinite programming and Bernstein-type inequality to transform the robust OCSR nonconvex problem into two convex sub-problems, which a block-coordinated descent algorithm can solve. Simulation results showcase the effectiveness of our robust AN-aided secure BF scheme and show that the proposed scheme outperforms the benchmark scheme in a security performance gain under various channel conditions, even in the worst case.

This paper proposes a novel parallel coding transmission strategy and an iterative detection and decoding receiver signal processing technique for orthogonal delay-Doppler division multiplexing (ODDM) modulation. Specifically, the proposed approach employs a parallel channel encoding (PCE) scheme that consists of multiple short-length codewords for each delay-Doppler multicarrier (DDMC) symbol. Building upon such a PCE transmission framework, we then introduce an iterative detection and decoding algorithm incorporating a successive decoding feedback (SDF) technique, which enables instant information exchange between the detector and decoder for each DDMC symbol. To characterize the error performance of the proposed scheme, we perform density evolution analysis considering the finite blocklength effects. Our analysis results, coupled with extensive simulations, demonstrate that the proposed PCE scheme with the SDF algorithm not only showcases a better overall performance but also requires much less decoding complexity to implement, compared to the conventional benchmark scheme that relies on a single long channel code for coding the entire ODDM frame.

The orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has been recently proposed as a promising modulation scheme for next-generation communication systems with high mobility. Despite its benefits, ODDM modulation and other DD domain modulation schemes face the challenge of excessive equalization complexity. To address this challenge, we propose time domain Tomlinson-Harashima precoding (THP) for the ODDM transmitter, to make the DD domain single-tap equalizer feasible, thereby reducing the equalization complexity. In our design, we first pre-cancel the inter-symbol-interference (ISI) using the linear time-varying (LTV) channel information. Second, different from classical THP designs, we introduce a modified modulo operation with an adaptive modulus, by which the joint DD domain data multiplexing and time-domain ISI pre-cancellation can be realized without excessively increasing the bit errors. We then analytically study the losses encountered in this design, namely the power loss, the modulo noise loss, and the modulo signal loss. Based on this analysis, BER lower bounds of the ODDM system with time domain THP are derived when 4-QAM or 16-QAM modulations are adopted for symbol mapping in the DD domain. Finally, through numerical results, we validate our analysis and then demonstrate that the ODDM system with time domain THP is a promising solution to realize better BER performance over LTV channels compared to orthogonal frequency division multiplexing systems with single-tap equalizer and ODDM systems with maximum ratio combining.

Orthogonal time frequency space (OTFS) has been widely acknowledged as a promising wireless technology for challenging transmission scenarios, including high-mobility channels. In this paper, we investigate the uplink multi-user OTFS transmission designs based on statistical channel information. Specifically, we investigate the pilot power allocation based on the a priori statistical channel state information (CSI) only, where performance on channel estimation is considered. We first derive the a posteriori Cram$\acute {\text {e}}$r-Rao bound (PCRB) based on the a priori channel information of each user. We unveil that the PCRB only relates to the user’s pilot signal-to-noise ratio (SNR) and the maximum of delay and Doppler shifts under the practical power-delay and power-Doppler profiles. Furthermore, a pilot power allocation scheme is proposed to minimize the average PCRB of different users, whose closed-form optimal allocation solution is derived. Moreover, we study the impact of statistical CSI on transmission rates, where a tight approximation of the sum-rate is derived. Particularly, the approximated sum-rate only relates to the user’s symbol SNR and the maximum of delay and Doppler shifts. More importantly, we propose a power allocation for different users based only on the statistical CSI to maximize the achievable sum-rate while ensuring user fairness. The optimal power allocation solution is obtained by a fractional programming approach. Our numerical results verify the derived PCRB and the sum-rate analysis, where a roughly 3 dB improvement in terms of channel estimation accuracy and a significant rate improvement can be obtained.

Designing short Luby transformation (LT) codes with low overhead and good error performance is crucial and challenging for the deployment of vehicle-to-everything networks, which require high reliability, high spectral efficiency, and low latency. In this paper, we investigate the design of globally optimal transmission strategies that consider interactions between feedback for short LT codes using reinforcement learning (RL), where traditional asymptotic analysis based on random graph theory is known to be inaccurate in this context. First, in order to reduce the decoding overhead of short LT codes, we derive the gradient expression for optimizing the degree distribution of LT codes, and propose a RL-based distribution optimization (RL-DO) algorithm for designing short LT codes. Then, to improve the reliability and overhead of LT codes under limited feedback, we model the feedback optimization problem as a Markov decision process, and propose the RL-based joint feedback and distribution optimization (RL-JFDO) algorithm, which aims to design globally-optimal feedback schemes. Simulations show that our methods have lower decoding overhead, error rate, and decoding complexity compared to existing feedback fountain codes.

We consider the uplink multiple access of heterogeneous users, e.g., ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) users. Each user has its own reliability requirement and blocklength constraint, and users transmitting longer blocks suffer from heterogeneous interference. On top of that, the decoding of URLLC messages cannot leverage successive interference cancellation (SIC) owing to the stringent latency requirements. This can significantly degrade the spectral efficiency of all URLLC users when the interference is strong. To overcome this issue, we propose a new multiple access scheme employing discrete signaling and treating interference as noise (TIN) decoding, i.e., without SIC. Specifically, to handle heterogeneous interference while maintaining the single-user encoding and decoding complexities, each user uses a single channel code and maps its coded bits onto sub-blocks of symbols, where the underlying constellations can be different. We demonstrate theoretically and numerically that the proposed scheme employing quadrature amplitude modulations and TIN decoding can perform very close to the benchmark scheme based on Gaussian signaling with perfect SIC decoding. Interestingly, we show that the proposed scheme does not need to use all the transmit power budget, but also can sometimes even outperform the benchmark scheme.

The ordered-reliability bits (ORB) variant of guessing random additive noise decoding (GRAND), known as ORBGRAND, achieves remarkably low time complexity at high code rates compared to other GRAND variants. However, its computational complexity remains higher than other near-ML universal decoders like ordered-statistics decoding (OSD). To address this, we propose segmented ORBGRAND, which partitions the error pattern search space based on code properties, generates syndrome-consistent sub-patterns (reducing invalid error patterns), and combines them in a near-ML order using sub-weights derived from two-level integer partitions of logistic weight. Numerical results show that segmented ORBGRAND reduces the average number of queries by at least 66% across all SNRs and cuts basic operations by over an order of magnitude, depending on segmentation and code rate. Further efficiency gains come from leveraging pre-generated shared sub-patterns, reducing average decoding time. Additionally, with abandonment ($b = 10^{5}$or smaller), segmented ORBGRAND provides a 0.2 dB power gain over ORBGRAND.

In this work, we study the time-frequency (TF) localization characteristics of the prototype pulse of orthogonal delay-Doppler (DD) division multiplexing modulation, namely, the DD plane orthogonal pulse (DDOP). The TF localization characteristics examine how concentrated or spread out the energy of a pulse is in the joint TF domain, the time domain (TD), and the frequency domain (FD). We first derive the TF localization metrics of the DDOP, including its TF area, its time and frequency dispersions, and its direction parameter. Based on these results, we demonstrate that the DDOP exhibits a high energy spread in the TD, FD, and the joint TF domain, while adhering to the Heisenberg uncertainty principle. Thereafter, we discuss the potential advantages brought by the energy spread of the DDOP, especially with regard to harnessing both time and frequency diversities and enabling fine-resolution sensing. Subsequently, we examine the relationships between the time and frequency dispersions of the DDOP and those of the envelope functions of DDOP’s TD and FD representations, paving the way for simplified determination of the TF localization metrics for more generalized variants of the DDOP and the pulses used in other DD domain modulation schemes. Finally, using numerical results, we validate our analysis and find further insights.

With multi-GHz bandwidth, Terahertz (THz) beamforming has drawn increasing attention in the sixth generation (6G) and beyond communications. Existing beamforming designs mainly focus on a compact antenna array where typical communication occurs in the far-field. However, in dense multi-user scenarios, only relying on far-field angle domain fails to distinguish users at similar angles. Therefore, a multi-user widely-spaced array (MU-WSA) is exploited in this paper, which enlarges the near-field region to introduce the additional distance domain, leading to a new paradigm of cross-near-and-far-field (CNFF) communication. Under this paradigm, the CNFF channel model is investigated, based on which the subarray spacing$d_{s}$and the number of subarrays K in MU-WSA are optimized to maximize the channel capacity. Then, in sub-connected (SC) systems, an subarray-based alternating optimization (S-AO) beamforming algorithm is proposed to deal with the special block-diagonal format of the analog precoder. For fully-connected (FC) systems, a low-complexity steering vector reconstruction (SVR)-based algorithm is proposed by constructing specialized steering vectors of MU-WSA. Numerical evaluations show that due to distance domain resolutions, the MU-WSA can improve the SE by over 60% at a power of 20 dBm compared to the compact array. Additionally, the proposed S-AO algorithm in the SC system can achieve over 80% of the sum (SE) of the FC system while reducing the number of phase shifters by$K^{2}$, thereby lowering power consumption. The SVR algorithm in the FC system can achieve over 95% of the upper bound of SE, but it takes only 10% of the running time of the singular value decomposition (SVD)-based algorithms.

The orthogonal delay-Doppler division multiplexing (ODDM) modulation has been widely acknowledged as a potential candidate for supporting ultra-reliable wireless communications under high-mobility scenarios. Nevertheless, the downlink transmission utilizing ODDM modulation remains an open problem due to the high complexity of prior channel estimation designs and complicated multi-user interference in the delay-Doppler (DD) domain. To address this issue, the channel estimation and data detection for downlink DD domain multiple access (DDMA) are investigated in this paper. The fast Fourier transform (FFT) interpolation is first presented for estimating each equivalent channel delay tap. To further promote the channel estimation accuracy, an orthogonal matching pursuit (OMP)-enabled scheme is also developed, where the normalized delay time and Doppler shift for each virtual path are determined separately to reduce the computational complexity. To ease the data detection at the user equipment (UE) side, we propose the orthogonal precoding for data symbols to provide the Gaussian distribution as the prior knowledge thanks to the central limit theorem, which can support the highly efficient interference cancellation without sharing the constellation information among UEs. Simulation results confirm the excellent performance of the proposed downlink DDMA schemes, where comparable reliability with optimal interference cancellation by sharing the knowledge of constellation among all UEs while the flexibility and efficiency can be guaranteed.

In the vehicular edge networks (VEN), task migration is complicated by issues like vehicle movement, diverse resource allocation, and integrating sensing with communication technologies. This paper presents a task migration strategy to optimize task flow under limited resources in PMN-assisted VEN. Vehicles can send public and private tasks to roadside units (RSUs), constrained by bandwidth, computational power, and storage space. Public tasks aim at data collection for road transportation management, while private tasks cover a spectrum of services from work to entertainment. To address the limitations imposed by resource scarcity and meet the demands of task migration, we have developed a dual-layer graph neural network (GNN) that leverages vehicle mobility patterns. In particular, the first layer of GNN acquires vehicle information and the latest surrounding information, and sends it to the nearby RSU. Considering the variety of tasks and multi-dimensional resource constraints, the second GNN layer forecasts RSU resource availability and vehicular trajectories. Subsequently, a task-based maximum flow algorithm (T-MFA) is proposed to refine task migration paths and resource allocation strategies to maximize task flow. Simulation experiments validate the efficacy of the proposed algorithm, demonstrating its capability to achieve optimal task migration by accommodating differences in tasks, resources, and capacities.

Despite narrow beams with strong anti-interception capabilities, terahertz communications still face eavesdropping risks in short-range indoor networks. This paper investigates physical-layer security of downlink terahertz communications for indoor three-dimensional (3D) networks comprised of a large number of access points (APs), users, human blockages, and eavesdroppers. We propose two different artificial noise (AN)-assisted terahertz secure transmission schemes, namely the full-AN (F-AN) scheme and partial-AN (P-AN) scheme, under the nearest line-of-sight association (NLA) strategy. The F-AN scheme involves full APs emitting AN to deteriorate the reception of eavesdroppers, and the P-AN scheme selects only those APs with blocked links to the typical user to emit AN based on the unique blocking feature of terahertz. We first obtain the expression for association probability. Then, we determine the eavesdropping region covered by the 3D beam on the ground. We derive the connection outage probability and secrecy outage probability for the two schemes by calculating the Laplace transform of aggregate interference. Our results provide interesting insights into how the secrecy performance is influenced by various system parameters, including the densities of APs and blockages. Moreover, we show that the P-AN scheme outperforms the F-AN scheme regarding the average number of perfect links per unit area.

This paper addresses the challenges of “double-fading” effect and coverage limitations encountered by passive intelligent reflecting surface (IRS) by introducing a novel IRS architecture, termed the dual-sided active-intelligent omni-surface (DSA-IOS). This architecture is capable of processing incident signals on both sides with controllable amplitudes and phases. Furthermore, the DSA-IOS is deployed in a multi-cell multiple-input single-output system to alleviate inter-cell interference and combat potential wiretapping from multi-antenna eavesdroppers. Considering eavesdroppers’ statistical channel state information, we introduce a system metric, the expected secrecy rate (ESR), to capture the tradeoff between secrecy rate (SR) and secrecy outage probability (SOP). Our objective is to maximize the system’s expected secrecy energy efficiency by jointly optimizing the beamformers and artificial noise at the base stations and the reflection and transmission coefficients for both sides at the DSA-IOS. To address the design problem, we propose a low-complexity alternating optimization scheme to acquire an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IOS outperforms other advanced IRS architectures in enhancing secure performance due to additional degrees of freedom for superior resource utilization. Our results also validate that the proposed ESR metric effectively balances the tradeoff between SR and SOP by customizing SOP thresholds for individual users.

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) has been recently proposed as a promising multicarrier modulation scheme to tackle Doppler spread in high-mobility environments. Accurate channel estimation is of paramount importance to guarantee reliable communication for the ODDM, especially when the delays and Dopplers of the propagation paths are off-grid. In this paper, we propose a novel grid refinement and adjustment-based sparse Bayesian inference (GRASBI) scheme for DD domain channel estimation. The GRASBI involves first formulating the channel estimation problem as a sparse signal recovery through the introduction of a virtual DD grid. Then, an iterative process is proposed that involves (i) sparse Bayesian learning to estimate the channel parameters and (ii) a novel grid refinement and adjustment process to adjust the virtual grid points. The grid adjustment in GRASBI relies on the maximum likelihood principle to attain the adjustment and utilizes refined grids that have much higher resolution than the virtual grid. Moreover, a low-complexity grid refinement and adjustment-based channel estimation scheme is proposed, that can provides a good tradeoff between the estimation accuracy and the complexity. Finally, numerical results are provided to demonstrate the accuracy, the convergence, and the efficiency of the proposed channel estimation schemes.

For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed. The key ideas of DAM aredelay pre-compensationandpath-based beamforming. However, existing research on DAM is mainly based on fully digital beamforming, which requires the number of radio frequency (RF) chains to be equal to the number of antennas. This paper proposes the hybrid analog/digital beamforming based DAM, including both fully and partially connected structures. The analog and digital beamforming matrices are designed to achieve performance close to DAM based on fully digital beamforming. While DAM was considered for the path-based channel model with integer delays in the previous work, this paper extends DAM to a more general tap-based model that accounts for fractional path delays. To further reduce the cost of channel estimation and improve the performance for wireless channels with fractional delays, DAM with codebook-based beam alignment and DAM-orthogonal frequency division multiplexing (DAM-OFDM) with hybrid beamforming are proposed. The effectiveness of the proposed techniques is verified by extensive simulation results.

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently emerged as a promising candidate for ensuring reliable communications over high mobility channels. One of the key challenges faced by systems based on ODDM modulation and other DD domain modulation schemes (e.g., orthogonal time frequency space modulation), is the excessive receiver complexity. To address this challenge, we propose time domain Tomlinson-Harashima precoding (THP) for the ODDM systems to make the single-tap equalizer feasible, thereby significantly reducing the receiver complexity. Different from previous THP designs, we first propose intersymbol-interference (ISI) pre-cancellation based on the linear time-varying (LTV) channel information. Second, we propose a modified modulo operation with an adaptive modulus to realize DD domain data modulation and time domain ISI precancellation. We analytically investigate the bit error rate (BER) performance of our proposed ODDM system with time domain THP. Particularly, we investigate three types of losses that can degrade the performance, namely the modulo signal loss, the power loss, and the modulo noise loss. Based on these, a lower bound for the BER of our proposed ODDM system with time domain THP under the DD domain single-tap equalizer is derived. Through numerical simulations, our analysis is first validated and finally, we show the significance of our design to attain a low complex receiver and superior BER performance over LTV channels.

The orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently been proposed as a cutting-edge and promising paradigm to ensure reliable communications in high mobility scenarios. In this work, we investigate the time-frequency (TF) localization characteristics of the DD plane orthogonal pulse (DDOP), which is the prototype pulse of ODDM modulation. The TF localization characteristics reveal the concentration or spread of pulse energy in the joint TF domain. We first derive the TF localization metric, TF area (TFA), for the DDOP. Based on this result, we provide insights into the energy spread of the DDOP in the joint TF domain. Then, we explore the potential advantages conferred by the energy spread of the DDOP, particularly in harnessing time and frequency diversities, as well as enabling high-resolution sensing. Furthermore, we determine the TFA for the recently proposed generalized design of the DDOP. Finally, we validate our analysis through numerical results and show that the energy spread of the generalized design of the DDOP in the joint TF domain exhibits a step-wise increase as the duration of sub-pulses increases.

In traditional Internet-of-Things (IoT) networks, devices generate their own signals to transmit data, which consumes more power. However, monostatic backscatter communications (BSC) can perform modulation and signal processing using an external signal from a reader, rather than generating signals from the device itself. Radio Frequency Identification (RFID) systems operate on this monostatic backscattering technology. Despite its benefits, BSC is vulnerable to exploitation by cyber attackers, primarily due to the limited hardware capabilities of passive RFID tags. Jamming attacks can disrupt legitimate BSC systems, enabling illegal activities or allowing competitors to gain advantages. This novel empirical investigation proposes two methods, power control and location control, to enable a typical RFID system to read data even in the presence of a powerful jamming attack. The study analyzed the relationships among reader power, the reader-to-tag (R2T) distance, the jammer-to-tag (A2T) distance, and power gain. The performance was analytically characterized and evaluated through computer simulations and hardware experiments. To the best of our knowledge, this is the first work to empirically quantify the impact of jamming attacks on RFID read rates at the reader and to assess the efficacy of physical layer security techniques in mitigating their impact. Lastly, experimental validation utilizing commodity hardware provides novel insights into optimal power and topology control for securing passive tags in sustainable IoT environments against jamming attacks.

The modern world is characterized by the Internet of Things (IoT), which requires efficient and eco-friendly energy solutions like backscattering communication. However, wireless communication vulnerabilities and high sensitivity expose backscattering systems to risks, particularly from proactive eavesdropping attacks due to the hardware limitations of the passive tags. This empirical study explores the effects of such attacks on radio frequency identification (RFID) backscattering communication in practical settings using commodity hardware. Specifically, we investigate the two-fold impact of proactive eavesdropping attacks, which decrease legitimate read rates due to jamming and data leakage caused by eavesdropping activities. The novel experimental demonstration proposes two defence mechanisms, power and location control, to protect RFID backscattering against proactive eavesdropping. Our nontrivial findings reveal that the impact of jamming is more challenging to eliminate than eavesdropping in the case of RFID backscattering. Moreover, location control is more effective in reducing data leakage than power control. This innovative research extends its analysis from single-tag scenarios to multiple-tag scenarios by using off-the-shelf hardware, which supports our proposed backscattering framework. Overall, this paper provides substantive insights that contribute to advancing green, energy-efficient security solutions for IoT communication networks.

In this paper, to address the “double fading” effect and coverage limitations encountered by conventional passive intelligent reflecting surface (IRS), we propose a novel IRS hardware architecture, termed the dual-sided active (DSA)-IRS, which is capable of simultaneously processing dual-sided incident signals with controllable both amplitude and phase. Furthermore, the DSA-IRS is deployed in a multi-cell multiple-input single-output (MISO) system to alleviate inter-cell interference. Our design objective is to maximize the weighted sum-rate (WSR) among all users by jointly optimizing the beamformers at the base stations (BSs) and the reflection and transmission coefficients for both sides at the DSA-IRS, which is formulated as a non-convex optimization problem. To address the problem, we propose an alternating optimization (AO) scheme to obtain an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IRS outperforms other advanced IRS architectures in enhancing system performance in multi-cell communications due to the additional degrees of freedom for superior resource utilization.

A low complexity receiver is developed for orthogonal time frequency space (OTFS) systems by exploring the special structure of the delay-Doppler (DD) domain channel matrix. Based on the system architecture of OTFS, we propose to shuffle the received samples in the DD domain such that samples experiencing the same delay but different Doppler spreads are grouped together. It is shown through theoretical analysis that the proposed shuffling operation yields a special block-circulantlike structure of the DD domain channel matrix. Enabled by the special structure, we propose to develop an iterative receiver that performs coherent matrix combining (CMC) with minimum mean squared error (MMSE) detection in the DD domain. The proposed receiver can collect the diversity in both the delay and Doppler domains while simultaneously suppress the negative impacts of DD domain intersymbol interference (ICI). Simulation results show that the proposed DD-CMC-MMSE receiver achieves significant performance gain over the commonly used message passing (MP) receiver for OTFS systems.

Convolutional precoding in polarization-adjusted convolutional (PAC) codes is a recently introduced variant of polar codes. It has demonstrated an effective reduction in the number of minimum weight codewords (a.k.a error coefficient) of polar codes. This reduction has the potential to significantly improve the error correction performance. From a codeword formation perspective, this reduction has limitations. Capitalizing on the understanding of the decomposition of minimum-weight codewords, this paper studies reverse precoding that can effectively reduce minimum-weight codewords more than in PAC codes. We propose a look-ahead list decoding for the reverse PAC codes, which has the same order of complexity as list decoding in PAC codes. Through numerical analysis, we demonstrate a notable reduction in error coefficients compared to PAC codes and polar codes, resulting in a remarkable improvement in the block error rate, in particular at high code rates.

The structure of a linear block code is pivotal in defining fundamental properties, particularly weight distribution, and code design. In this study, we characterize the Type II structure of polar codewords with weights less than twice the minimum weight Wmin, utilizing the lower triangular affine (LTA) transform. We present a closed-form formula for their enumer-ation. Leveraging this structure and additionally characterizing the structure of weight 2 Wmin, we ascertain the complete weight distribution of low-rate and, through the utilization of dual codes properties, high-rate polar codes, subcodes of Reed-Muller (RM) codes, and RMxPolar codes. Furthermore, we introduce a partial order based on the weight distribution and briefly explore its properties and applications in code construction and analysis.

In the study of packet-level random linear network coding (RLNC) in wireless broadcast, RLNC over$\mathbf{GF}(2^{L})$is known to asymptotically achieve the optimal completion delay with increasing$L$. Utilization of guessing random additive noise decoding (GRAND) at physical layer can help leverage RLNC packets to generate syndromes so as to reduce packet erasure probabilities and thus further improve the completion delay performance. Prior to this work, only few studies investigated GRAND-assisted RLNC and they restricted to GF(2)-coding. In this paper, we first provide a general framework to formulate the decoding process of GRAND-assisted RLNC over$\mathbf{GF}(2^{L})$for$L\geq 1$. Even for GRAND-assisted GF(2)-RLNC, the formulation is more complete than previous considerations in the sense that it takes the a priori information of which packets have errors into consideration. In addition, we propose a novel GRAND-assisted$\mathbf{GF}(2^{L})$-RLNC scheme whose computational overhead introduced by GRAND is negligible. We theoretically derive lower bounds on the distribution as well as an upper bound on the expected value of the completion delay of the proposed scheme. Numerical results also demonstrate a reduction in average completion delay for the proposed new GF(28)-RLNC scheme, when compared to existing approaches.

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver empowered integrated sensing and communications (ISAC) system is proposed for future multi-demand terminals. To address interference management, we implement rate-splitting multiple access (RSMA), where the common stream is independently designed for the sensing service. We introduce the sensing quality of service (QoS) criteria based on this structure and construct an optimization problem with the sensing QoS criteria as the objective function to optimize the sensing stream precoding matrix and the communication stream precoding matrix. Due to the coupling of optimization variables, the formulated problem is a non-convex optimization problem that cannot be solved directly. To tackle the above-mentioned challenging problem, alternating optimization (AO) is utilized to decouple the optimization variables. Specifically, the problem is decoupled into three subproblems about the sensing stream precoding matrix, the communication stream precoding matrix, and the auxiliary variables, which is solved alternatively through AO until the convergence is reached. For solving the problem, successive convex approximation (SCA) is applied to deal with the sum-rate threshold constraints on communications, and difference-of-convex (DC) programming is utilized to solve rank-one non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in terms of improving the communication and sensing QoS.

Traditional communications focus on regular and orthogonal signal waveforms for simplified signal processing and improved spectral efficiency. In contrast, the next-generation communications would aim for irregular and nonorthogonal signal waveforms to introduce new capabilities. This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924, with the aim of enhancing performance in Industrial Internet of Things (IIoT). In time-critical IIoT applications, low-latency and time-jitter tolerance are two critical factors that significantly impact the performance and reliability. Recognizing the inevitability of latency and jitter in practice, this work aims to propose a waveform technique to mitigate these effects via reducing latency and enhancing the system robustness under time jitter effects. The utilization of irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Integrating the irSinc in a two-stage framework, a single-carrier nonorthogonal frequency shaping (SC-NOFS) waveform is developed, showcasing perfect compatibility with fifth generation (5G) standards, enabling the direct integration of irSinc in existing industrial Internet of things (IoT) setups. Through 5G standard signal configuration, our signal achieves faster data transmission within the same spectral bandwidth. Hardware experiments validate an 18% saving in timing resources, leading to either reduced latency or enhanced jitter tolerance.

Supporting multiuser communication and localization is a typical scenario in Integrated sensing and communications (ISAC). However, the problem of multi-echo induced by multipath and multiuser makes it hard to determine the relationship between user equipments (UEs) and these echoes. Thus, applying traditional estimation algorithms at the radar receiver inevitably leads to weak communication and localization performances due to the mismatch between echoes and UEs. In this paper, aiming to achieve multiuser association and localization under doubly dispersive multipath channels, we construct an ISAC unified waveform based on the orthogonal delay-Doppler division multiplexing (ODDM) principle and develop an off-grid cluster sparse Bayesian learning estimation (OG-CSBL) algorithm. Particularly, we focus on the mono-static setup, where the base station (BS) expects to communicate with multiuser while sensing their locations. We utilize the high-resolution range profile (HRRP) to characterize the physical features of UEs and establish associations with their echoes by exploiting the inherent cluster structure. To estimate parameters, we design a hybrid Dirichlet process (DP)-Gaussian hierarchical prior distribution and propose a variational Bayesian inference (VBI)-EM strategy. Additionally, we develop a backtrack echo identification scheme to facilitate precise UE localization. Simulation results demonstrate that the proposed scheme achieves superior NMSE performance, offers meter-level localization accuracy, and obtains better BER performance in the complex multiuser coexistence scenario.

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing, coupled with the surge of machine learning (ML)/artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this article, we provide a tutorial, survey, and outlook on past, emerging, and future MA techniques and pay particular attention to how wireless network intelligence and multifunctionality will lead to a rethinking of those techniques. This article starts with an overview of orthogonal, physical-layer multicasting, space domain, power domain (PD), rate-splitting, code-domain MAs, MAs in other domains, and random access (RA), and highlights the importance of conducting research in universal MA (UMA) to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, and receiver designs, for different MA schemes, and MA for AI such as federated learning (FL)/edge intelligence and over-the-air computation (AirComp). We then discuss MA for network multifunctionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications (SeComs), virtual reality (VR), and smart radio and reconfigurable intelligent surfaces (RISs), before presenting a roadmap toward 6G standardization. Throughout the text, we also point out numerous directions that are promising for future research.

We demonstrate that the proposed optical orthogonal time frequency space (O-OTFS) is capable of improving the bandwidth-/power-/energy-efficiencies of optical orthogonal frequency-division multiplexing (O-OFDM). The bandwidth-efficiency is improved because only a single cyclic prefix (CP) is needed for an entire O-OTFS frame. The power-efficiency is enhanced thanks to the diversity gain achieved by its symplectic finite Fourier transform (SFFT), which also leads to a reduced peak-to-average power ratio (PAPR), hence improving its energy-efficiency. These features are facilitated by the proposed layered asymmetrically clipped O-OTFS (LACO-OTFS), which is capable of removing the direct current (DC) bias while retaining the full optical throughput. Nonetheless, there exists an inherent trade-off, where increasing the O-OTFS frame size leads to a commensurately reduced CP percentage at the cost of an increased PAPR. In order to mitigate this, we propose to perform discrete Fourier transform based spreading (DFT-S) in the delay-Doppler (DD)-domain. Furthermore, we demonstrate that regardless of the choice of domain in which the information is modulated (i.e. O-OFDM/O-OTFS with/without DFT-S), the frequency-selectivity of the quasi-static but dispersive optical channel can always be equalized by single-tap frequency-domain equalization (FDE). Moreover, the channel estimation techniques are conceived to operate in the time-/frequency-/DD-domains for both O-OFDM and O-OTFS. Our simulation results demonstrate that for a multi-user optical wireless system associated withM= 64 subcarriers and the OTFS frame length ofN= 64, LACO-OTFS is capable of achieving a 7 dB power-efficiency gain over LACO-OFDM, where the CP overhead is reduced by a factor ofN= 64. DFT-S-LACO-OTFS is also capable of providing a 7 dB power-efficiency gain over DFT-S-LACO-OFDM, where the low PAPR of single-carrier transmission is retained.

In this study, a joint blind frame synchronization and decoding method is proposed based on the normalized syndrome satisfaction probability (NSSP) provided by the soft Low-Density Parity-Check (LDPC) codes decoder. To reduce complexity, a stopping criterion is introduced into the iterative process through the convergence of NSSP evolution patterns. In addition to achieving an excellent synchronization performance, this method also eliminates the redundancy caused by the pilot sequence. Furthermore, it is compared with the optimal pilot-based and other code-aided frame synchronization algorithms. According to analytical and simulation results, the proposed technique outperforms other advanced code-aided frame synchronization solutions in synchronization, which makes it applicable to achieve comparable performance to the pilot-based methods in specific coding gains. Due to the introduction of a novel stopping criterion, the average number of joint detection & decoding is reduced by up to 90%. Analog probability processing technology integrates sub-threshold region circuits with probability domain-based iterative message-passing algorithms inspired by high energy efficiency and low complexity. Hence, hardware implementation is presented based on the analog LDPC decoder chip as fabricated in a 0.35-μm CMOS technology for our proposed algorithms. The experimental results demonstrate the effectiveness of the proposed method and the realization loss is within 0.2 dB compared with the theoretical circuit simulation results.

The weight distribution of an error correction code is a critical determinant of its error-correcting performance. In the case of polar codes, the minimum weight wmin(equal to the minimum distanced) is the only weight for which an explicit enumerator formula is currently available. Having closed-form weight enumerators for polar codewords with weights greater than the minimum weight not only simplifies the enumeration process but also provides valuable insights towards constructing better polar-like codes. In this paper, we contribute towards understanding the algebraic structure underlying higher weights by analyzing Minkowski sums of orbits. Our approach builds upon the lower triangular affine (LTA) group of decreasing monomial codes. Specifically, we propose a closed-form expression for the enumeration of codewords with weight 1.5wmin. The key insight for code design is that the enumeration of codewords with weight wmin and 1.5wminrelies on the set of maximum degree monomials. This set corresponds to the indices of minimum weight rows of the polar transformGNbelonging to the information setI. Consequently, reducing the cardinality of this set can lead to a reduction of the number of codewords in both weight categories.

Recently, orthogonal time frequency space (OTFS) modulation has garnered considerable attention due to its robustness against doubly-selective wireless channels. In this paper, we propose a low-complexity iterative successive interference cancellation based minimum mean squared error (SIC-MMSE) detection algorithm for zero-padded OTFS (ZP-OTFS) modulation. In the proposed algorithm, signals are detected based on layers processed by multiple SIC-MMSE linear filters for each sub-channel, with interference on the targeted signal layer being successively canceled either by hard or soft information. To reduce the complexity of computing individual layer filter coefficients, we also propose a novel filter coefficients recycling approach in place of generating the exact form of MMSE filter weights. Moreover, we design a joint detection and decoding algorithm for ZP-OTFS to enhance error performance. Compared to the conventional SIC-MMSE detection, our proposed algorithms outperform other linear detectors, e.g., maximal ratio combining (MRC), for ZP-OTFS with up to 3 dB gain while maintaining comparable computation complexity.

We investigate the coexistence of orthogonal time-frequency space (OTFS) modulation with current fourth- and fifth-generation (4G/5G) communication systems that primarily use orthogonal frequency-division multiplexing (OFDM) waveforms. We first derive the input-output-relation of OTFS in the considered coexisting system. In this derivation, we consider 1) the inclusion of multiple cyclic prefixes (CPs) with unequal lengths to the OTFS signal and 2) edge carrier unloading (ECU), to account for the impacts of CP length, frame structure, and subcarrier arrangement described in 3GPP standards for 4G/5G systems. Our analysis reveals that the inclusion of multiple CPs to the OTFS signal and ECU lead to the channel response exhibiting spreading effects/leakage along the Doppler and delay dimensions, respectively. Consequently, the effective sampled delay-Doppler (DD) domain channel model for OTFS in coexisting systems may exhibit reduced sparsity. We also show that the effective DD domain channel coefficients for OTFS in coexisting systems are influenced by the unequal lengths of CPs. Subsequently, we propose an interference cancellation-based channel estimation (CE) technique for OTFS in coexisting systems. Through numerical results, we validate our analysis, highlight the importance of not ignoring the unequal lengths of CPs during signal detection, and show the significance of the proposed CE technique.

This paper investigates the characteristics and performance of orthogonal delay-Doppler division multiplexing (ODDM) modulation over doubly selective physical channels with general delay and Doppler. Assuming that the implementation of the ODDM is based on IDFT/DFT and sample-wise pulse shaping/receiver filtering, we study the input-output (IO) relation for ODDM and characterize the equivalent sampled delay-Doppler (ESDD) domain channel in terms of the parameters of the physical channel and the DD plane orthogonal pulse (DDOP). The established IO relation can describe the patterns of inter-symbol-interference (ISI) and inter-carrier-interference (ICI) for more general delay and Doppler shifts of the physical channel. Based on the results, we also examine the influence of the transmitter configuration on the sparsity of the ESDD channel and on the resulting performance-complexity tradeoff of ODDM. We further introduce a pilot-assisted method of estimating the physical channel parameters by leveraging the derived IO relation and the root-MUSIC algorithm. We also present a low-complexity symbol detector for ODDM systems based on conjugate gradients (CG). Simulation results under various settings show that the error performance of ODDM based on the estimate of the physical channel approaches that with perfect channel state information (CSI) at a low-to-medium signal-to-noise ratio (SNR), but has an increased gap from the perfect CSI case when the SNR increases.

Drawing inspiration from the advantages of intelligent reflecting surfaces (IRS) in wireless networks, this paper presents a novel design for intelligent omni surface (IOS) enabled integrated sensing and communications (ISAC). By harnessing the power of multi-antennas and a multitude of elements, the dual-function base station (BS) and IOS collaborate to realize joint active and passive beamforming, enabling seamless 360-degree ISAC coverage. The objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of multi-target sensing while ensuring the multi-user multi-stream communications. To achieve this, a comprehensive optimization approach is employed, encompassing the design of radar receive vector, transmit beamforming matrix, and IOS transmissive and reflective coefficients. Due to the non-convex nature of the formulated problem, an auxiliary variable is introduced to transform it into a more tractable form. Consequently, the problem is decomposed into three sub-problems based on the block coordinate descent algorithm. Semidefinite relaxation and successive convex approximation methods are leveraged to convert the sub-problem into a convex problem, while the iterative rank minimization algorithm and penalty function method ensure the equivalence. Furthermore, the scenario is extended to mode switching and time switching protocols. Simulation results validate the convergence and superior performance of the proposed algorithm compared to other benchmark algorithms.

The recently emerged multiple-input multiple-output over orthogonal delay-Doppler division multiplexing (MIMO-ODDM) is gaining paramount interest as a promising solution to provide reliable communication performance for high-mobility systems. To achieve its full potential, signal detection becomes a critical issue, while the performance of existing methods is yet to be satisfactory. In this paper, we develop a novel signal detection approach for MIMO-ODDM systems by leveraging the spatial-based generative adversarial network, namely SG-ODDM, for accurate, interference-resilient and environment-robust performance. We creatively design a spatial-based generative adversarial network (spatial-based GAN) for comprehensive feature extraction and interference mitigation. We propose a transfer learning-based adaptive updating (TAU) to enhance environmental robustness by reducing the frequency and effort in updating the detection model. Extensive simulation results verify that the proposed SG-ODDM is considerably superior to state-of-the-art related works, in terms of detection accuracy, interference resilience and updating effort reduction.

Orthogonal time frequency space (OTFS) as a newly proposed two-dimensional modulation scheme outperforms the orthogonal frequency division multiplexing in high-speed scenarios. Most recent studies focus on off-grid channel estimation with the ideal pulse which needs to satisfy the bi-orthogonality robustness condition but does not exist. In this paper, we consider an OTFS system with rectangular pulses and propose a grid evolution based off-grid sparse Bayesian inference (GESBI) by updating the virtual delay-Doppler grid to improve the accuracy of the channel estimation. In particular, different from the recently proposed off-grid channel estimation algorithms in OTFS where the virtual grid is fixed and uniform, the proposed channel estimation method consists of an external and internal iteration, where the grid evolution method performs in the external iteration to update the virtual grid to be non-uniform by utilizing the estimated on-grid and the off-grid information in the internal iteration. In addition, the grid evolution-based efficient sparse Bayesian inference with the student’s T distribution prior (T-GEESBI) is proposed to reduce the channel estimation complexity while improving the channel estimation accuracy. Specifically, the matrix inversion is avoided by approximating the posterior distribution. Then the equal non-convex problem is handled by utilizing the block coordinate descent method in a majorization-minimization framework. Furthermore, the two types of genie bounds on the mean squared error of the estimated channel coefficients are derived. Finally, both theoretical and numerical analyses demonstrate low complexity, convergence, and the efficiency of the proposed channel estimation approach.

Orthogonal time-frequency space (OTFS) is a two-dimensional (2D) modulation technology for processing high mobility communication signals. The integer delay and Doppler shifts can lead to significant modeling error in the delay-Doppler (DD) domain. To obtain accurate estimation of fractional DD domain channel, we propose a novel two-step off-grid channel estimation using Bayesian compressive sensing (BCS) for OTFS systems. First, we analyze the vectorized form of fractional DD domain input-output relationship and derive a simplified expression form to adapt compressive sensing model. Then, to tackle the problem of fractional parameters estimation, we build an off-grid compressive model in light of high-order Taylor expansion. The channel parameters can be obtained by our proposed off-grid BCS (OG-BCS) algorithm, where we edge out the noise parameters and adopt a hierarchical Laplacian scale mixture (LSM) prior for OTFS signal. At the same time, based on likelihood maximization we derive the off-grid update expressions and incorporate it into our optimized BCS framework. To further reduce the computational complexity, we propose a fast BCS (FBCS) algorithm via LSM prior. In our proposed FBCS algorithm, the hyper-parameters modeling the sparse signal are separated for calculation, and only a single hyper-parameter is considered for updating in each iteration. Specially, we analyze the condition of maximization for obtaining the solution of hyper-parameters and derive its update expressions. Simulation results illustrate that our proposed algorithm has better computational efficiency and precise channel estimation performance.

Reconfigurable intelligent surface (RIS) has recently been widely investigated in wireless communication systems due to its low deployment cost and high-performance gain. In this work, we study the multiple-RIS-assisted uplink multiple-user multiple-input multiple-output (MU-MIMO) communication systems, where each user’s signal is sent to the base station via both the direct and the reflected links. To obtain informative insight into the considered system with the statistical channel information, we first derive the closed-form expression for the ergodic sum rate of the MU-MIMO systems by applying the operator-valued free probability theory. Then, the covariance matrices of the transmit signals and the phase shifts of the RIS elements are jointly optimized to maximize the derived asymptotic ergodic sum rate via alternating optimization (AO). Specifically, the AO procedure is composed of a water-filling algorithm and a gradient descent algorithm over the Riemannian manifold and the two algorithms iterate until convergence. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulation and the superiority of the proposed AO algorithm compared to the benchmark. Furthermore, the rank deficiency of the MIMO channel can be significantly improved by the deployment of multiple RISs and the proposed AO algorithm.

The multiple-input multiple-output over orthogonal delay-Doppler division multiplexing (MIMO-ODDM) has recently attracted great interest as a promising solution for high-mobility systems. To achieve its full potential, signal detection becomes a critical issue, while the performance of the existing methods is yet to be satisfactory. To address this issue, we propose a novel signal detection approach called SG-ODDM, which utilizes a spatial-based generative adversarial network (spatial-based GAN) for accurate and interference-resistant performance. We creatively design a spatial-based GAN for comprehensive feature extraction and interference mitigation. In the spatial-based GAN, we develop an attention-based generator with multi-domain feature (AGMF) to effectively reconstruct signals for detection by extracting and utilising signal characteristics across multiple domains, e.g., delay, Doppler, and spatial domains. Moreover, we develop a self-attention-based discriminator with multi-domain feature (SDMF) to guide AGMF to mitigate the impact of interference in MIMO systems, thereby improving the quality of the generated/reconstructed data from AGMF. Additionally, we design a novel hybrid loss function to fully exploit signal features in the multiple domains for detection. Through extensive simulations, we demonstrate that SG-ODDM outperforms state-of-the-art related works regarding detection accuracy and interference resilience.

Convolutional precoding in polarization-adjusted convolutional (PAC) codes, a recently introduced variant of polar codes, has demonstrated an effective reduction in the number of minimum weight codewords (a.k.a error coefficient) of polar codes. This reduction has the potential to significantly improve the error correction performance. From a codeword formation perspective in cosets, this reduction has a limitation in the PAC coding which depends on the rows of the generator matrix involved in the formation of codewords. To overcome this limitation, capitalizing on the understanding of the decomposition of minimum-weight codewords, this paper introduces a novel precoding scheme that strategically disrupts the formation of a majority of minimum-weight codewords. This scheme significantly enhances the error coefficient without compromising the minimum distance of the code. Through numerical analysis, we demonstrate a noteworthy reduction in error coefficients compared to PAC codes and polar codes, resulting in a remarkable improvement in the block error rate of short codes.

In this paper, we study the pulse shaping for delay-Doppler (DD) communications. We start with constructing a basis function in the DD domain following the properties of the Zak transform. Particularly, we show that the constructed basis functions are globally quasi-periodic while locally twisted-shifted, and their significance in time and frequency domains are then revealed. We further analyze the ambiguity function of the basis function, and show that fully localized ambiguity function can be achieved by constructing the basis function using periodic signals. More importantly, we prove that time and frequency truncating such basis functions naturally leads to approximate delay and Doppler orthogonalities, if the truncating windows are periodic within the support. Motivated by this, we propose a DD Nyquist pulse shaping scheme considering signals with periodicity. Finally, our conclusions are verified by using various strictly or approximately periodic pulses.

This paper focuses on secure backscatter transmission in the presence of a passive multi-antenna eavesdropper through a symbiotic radio (SR) network. Specifically, a single-antenna backscatter device (BD) aims to transmit confidential information to a primary receiver (PR) by using a multi-antenna primary transmitter's (PT) signal, where the received symbols are jointly decoded at the PR. Our objective is to achieve confidential communications for BD while ensuring that the primary system's quality of service (QoS) requirements are met. We propose an alternating optimisation algorithm that maximises the achievable secrecy rate of BD by jointly optimising primary transmit beamforming and power sharing between information and artificial noise (AN) signals. Numerical results verify our analytical claims on the optimality of the proposed solution and the proposed methodology's underlying low complexity. Additionally, our simulations provide nontrivial design insights into the critical system parameters and quantify the achievable gains over the relevant benchmark schemes.

The problem of enabling the coexistence of heterogeneous services, e.g., different ultra-reliable low-latency communications (URLLC) services and/or enhanced mobile broadband (eMBB) services, in the uplink is studied. Each service has its own error probability and blocklength constraints and the longer transmission block suffers from heterogeneous interference. Due to the latency concern, the decoding of URLLC messages cannot leverage successive interference cancellation (SIC) and should always be performed before the decoding of eMBB messages. This can significantly degrade the achievable rates of URLLC users when the interference from other users is strong. To overcome this issue, we propose a new transmission scheme based on discrete signaling and treating interference as noise decoding, i.e., without SIC. Guided by the deterministic model, we provide a systematic way to construct discrete signaling for handling heterogeneous interference effectively. We demonstrate theoretically and numerically that the proposed scheme can perform close to the benchmark scheme based on capacity-achieving Gaussian signaling with the assumption of perfect SIC.

The recently introduced maximum-likelihood (ML) decoding scheme called guessing random additive noise decoding (GRAND) has demonstrated a remarkably low time complexity in high signal-to-noise ratio (SNR) regimes. However, the complexity is not as low at low SNR regimes and low code rates. To mitigate this concern, we propose a scheme for a near-ML variant of GRAND called ordered reliability bits GRAND (or ORBGRAND), which divides codewords into segments based on the properties of the underlying code, generates sub-patterns for each segment consistent with the syndrome (thus reducing the number of inconsistent error patterns generated), and combines them in a near-ML order using two-level integer partitions of logistic weight. The numerical evaluation demonstrates that the proposed scheme, called segmented ORBGRAND, reduces the average number of queries (time complexity/latency) to one-third at all SNR regimes. Moreover, the segmented ORBGRAND with abandonment also improves the error correction performance.

This study examines the coexistence of orthogonal time-frequency space (OTFS) modulation with current fourth-and fifth-generation (4G/5G) wireless communication systems that primarily use orthogonal frequency-division multiplexing (OFDM) waveforms. We first derive the input-output-relation (IOR) of OTFS when it coexists with an OFDM system while considering the impact of unequal lengths of the cyclic prefixes (CPs) in the OTFS signal. We show analytically that the inclusion of multiple CPs to the OTFS signal results in the effective sampled delay-Doppler (DD) domain channel response to be less sparse. We also show that the effective DD domain channel coefficients for OTFS in coexisting systems are influenced by the unequal lengths of the CPs. Subsequently, we propose an embedded pilotaided channel estimation (CE) technique for OTFS in coexisting systems that leverages the derived IOR for accurate channel characterization. Using numerical results, we show that ignoring the impact of unequal lengths of the CPs during signal detection can degrade the bit error rate performance of OTFS in coexisting systems. We also show that the proposed CE technique for OTFS in coexisting systems outperforms the state-of-the-art threshold-based CE technique.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation was recently proposed as a promising solution for high-mobility communication systems. To achieve the potential of ODDM, reliable signal detection is essential, hence, in this work, we propose a contrastive learning-based signal detection approach for ODDM systems, named CL-ODDM. Unlike the conventional deep learning-based methods which focus on positive samples alone, we creatively leverage contrastive learning to exploit both positive and negative samples in the training dataset. By doing so, more distinguishable information of signals can be captured and extracted, contributing to reliable detection results. Moreover, we employ a convolutional neural network and recurrent encoder-decoder (CREN) to represent the comprehensive properties and features of ODDM signals. In addition, an adaptive correction method (ACM) is proposed to increase the convergence rate and improve the stability of the detection model. Extensive simulation results validate that the proposed CL-ODDM is significantly superior state-of-the-art related work, regarding the detection accuracy and convergence rate.

In this paper, we consider the downlink broadcast channel under heterogenous blocklength constraints, where each user experiences different interference statistics across its received symbols. Different from the homogeneous blocklength case, the strong users with short blocklength transmitted symbol blocks usually cannot wait to receive the entire transmission frame and perform successive interference cancellation (SIC) owing to their stringent latency requirements. Even if SIC is feasible, it may not be perfect under finite blocklength constraints. To cope with the heterogeneity in latency and reliability requirements, we propose a practical downlink transmission scheme with discrete signaling and single-user decoding, i.e., without SIC. In addition, we derive the finite blocklength achievable rate and use it for guiding the design of channel coding and modulations. Both achievable rate and error probability simulation show that the proposed scheme can operate close to the benchmark scheme which assumes capacity-achieving signaling and perfect SIC.

Recently, grant-free orthogonal time–frequency space-based tandem spreading multiple access (OTFS-TSMA) is proposed for machine-type communications (mMTCs) in smart railways environmental sensing. To achieve massive connections with scarce radio resources, OTFS-TSMA combines the advantages of OTFS and TSMA. It shows high connectivity and reliability under time–frequency-selective fading channels. Meanwhile, smart railways require over-horizon and all-weather environmental sensing based on mMTC, and the implementation of both would cost a lot in terrestrial networks. With the development of low-Earth orbit (LEO) satellites, enabling smart railways mMTC over LEO satellite is a potential diagram. However, in this scenario, due to the larger transmission delay and Doppler frequency shift, the time–frequency resource requirements of the OTFS modulation-based system increase significantly and are difficult to meet. To this end, OTFS-TSMA based on differential Doppler shift is proposed in this article. Specifically, in this article, the satellite-to-ground communication system model consisting of three sections is introduced, and the Doppler shift and differential Doppler shift characteristics of access points (APs) are investigated. Next, it is proven that designing OTFS-based multiple access schemes over the LEO satellite based on differential Doppler shift is not only resource-friendly but also has the advantages of service continuity and controllable multiuser interference. Then, the transceiver of differential-Doppler-shift-based OTFS-TSMA and its improved designs are proposed. Finally, the simulation results demonstrate that the proposed transceiver realizes high resource efficiency, collision resolution capability, and reliability for smart railways mMTC over the LEO satellite.

Rate splitting (RS) and rate splitting multiple access (RSMA) have emerged as a promising and powerful multiple access, interference management, and multi-user strategy for next-generation wireless systems and networks. This Special Issue is entirely dedicated to the theory, design, optimization, and applications of RS and RSMA in various network configurations. It starts with a guest editor-authored tutorial paper [A1] that delineates the basic principles and applications of RS and RSMA. The tutorial paper is then followed by 17 technical papers.

Rate-Splitting Multiple Access (RSMA) has emerged as a powerful multiple access, interference management, and multi-user strategy for next generation communication systems. In this tutorial, we depart from the orthogonal multiple access (OMA) versus non-orthogonal multiple access (NOMA) discussion held in 5G, and the conventional multi-user linear precoding approach used in space-division multiple access (SDMA), multi-user and massive MIMO in 4G and 5G, and show how multi-user communications and multiple access design for 6G and beyond should be intimately related to the fundamental problem of interference management. We start from foundational principles of interference management and rate-splitting, and progressively delineate RSMA frameworks for downlink, uplink, and multi-cell networks. We show that, in contrast to past generations of multiple access techniques (OMA, NOMA, SDMA), RSMA offers numerous benefits: 1) enhanced spectral, energy and computation efficiency; 2) universality by unifying and generalizing OMA, SDMA, NOMA, physical-layer multicasting, multi-user MIMO under a single framework that holds for any number of antennas at each node (SISO, SIMO, MISO, and MIMO settings); 3) flexibility by coping with any interference levels (from very weak to very strong), network loads (underloaded, overloaded), services (unicast, multicast), traffic, user deployments (channel directions and strengths); 4) robustness to inaccurate channel state information (CSI) and resilience to mixed-critical quality of service; 5) reliability under short channel codes and low latency. We then discuss how those benefits translate into numerous opportunities for RSMA in over forty different applications and scenarios of 6G, e.g., multi-user MIMO with statistical/quantized CSI, FDD/TDD/cell-free massive MIMO, millimeter wave and terahertz, cooperative relaying, physical layer security, reconfigurable intelligent surfaces, cloud-radio access network, internet-of-things, massive access, joint communication and jamming, non-orthogonal unicast and multicast, multigroup multicast, multibeam satellite, space-air-ground integrated networks, unmanned aerial vehicles, integrated sensing and communications, grant-free access, network slicing, cognitive radio, optical/visible light communications, mobile edge computing, machine/federated learning, etc. We finally address common myths and answer frequently asked questions, opening the discussions to interesting future research avenues. Supported by the numerous benefits and applications, the tutorial concludes on the underpinning role played by RSMA in next generation networks, which should inspire future research, development, and standardization of RSMA-aided communication for 6G.

The problem of designing downlink transmission schemes for supporting heterogeneous ultra-reliable low-latency communications (URLLC) and/or with other types of services is investigated. We consider the broadcast channel, where the base station sends superimposed signals to multiple users. Under heterogeneous blocklength constraints, strong users who are URLLC users cannot wait to receive the entire transmission frame and perform successive interference cancellation (SIC) due to stringent latency requirements, in contrast to the conventional infinite blocklength cases. Even if SIC is feasible, SIC may be imperfect under finite blocklength constraints. To cope with the heterogeneity in latency and reliability requirements, we propose a practical downlink transmission scheme withdiscrete signalingandsingle-user decoding (SUD), i.e., without SIC. We carefully design the discrete input distributions to enable efficient SUD by exploiting the structural interference. Furthermore, we derive the second-order achievable rate under heterogenous blocklength and error probability constraints and use it to guide the design of channel coding and modulations. It is shown that in terms of achievable rate under short blocklength, the proposed scheme with regular quadrature amplitude modulations and SUD can operateextremely closeto the benchmark schemes that assume perfect SIC with Gaussian signaling.

Deep learning (DL) demonstrates tremendous potential in high-mobility communication systems, especially from the perspective of signal detection. However, most existing DL-based detection methods are data/environment specific and the re-training process is resource intensive. To address these shortcomings, we propose a contrastive learning-based environmentally robust signal detection approach in orthogonal delay-Doppler division multiplexing (CL-ODDM) to achieve fast convergence, high accuracy and strong robustness to variations in wireless environments. Specifically, unlike conventional methods which explore only positive samples in the dataset for detection, in this work, we propose to leverage contrastive learning to fully exploit both positive and negative samples in the training dataset. This enables us to extract more comprehensive features of signals, which can accelerate convergence, improve detection accuracy, and enhance the generalized ability of our CL-ODDM. Moreover, we creatively employ a convolutional neural network and recurrent encoder-decoder (CREN) to represent the underlying properties of ODDM signals and extract high-quality features. To further improve environmental robustness, we propose novel training strategies, i.e., data augmentation (DA) and adaptive updating scheme (AUS). The proposed DA method is expected to increase the diversity of the dataset and represent more effective signal features. The designed AUS leverages transfer learning to adapt partial layers of CREN to make our CL-ODDM suitable for various wireless environments. Numerous simulation results validate that the proposed CL-ODDM significantly outperforms state-of-the-art related works, in terms of detection accuracy, environmental robustness and convergence rate.

Millimeter-wave (mmWave) can provide abundant spectrum resource in vehicular communication networks. Nevertheless, due to the high path-loss and blocking effects in mmWave propagation, and high mobility of vehicles, downlink services for vehicles would be seriously degraded. In this paper, we firstly propose a deep reinforcement learning-based joint beam allocation and relay selection (JoBARS) scheme to mitigate blocking effects and optimize the total transmission rate of the vehicular network, where the mmWave base station (mmBS) provides multi-user services. When downlinks are blocked, the mmBS can select appropriate idle vehicles as relay nodes to enhance service quality from a global perspective. We set the rate punishment restriction in JoBARS scheme to guarantee each vehicle can obtain high-quality service. Besides, a relaying incentive mechanism (RIM) is proposed to avoid vehicles being overly selected for relaying and ensure that relay vehicles have a higher chance of being served in the next round. We demonstrate that JoBARS scheme can effectively enhance the total transmission rate while alleviating transmission outages caused by severe propagation attenuation of mmWave signals. Compared with Greedy Selection scheme, the total rate and average connection probability of vehicles under JoBARS scheme are nearly 17% and 14% higher when blocking effects are severe.

Orthogonal time frequency space (OTFS) modulation is a recently proposed delay-Doppler (DD) domain communication scheme, which has shown promising performance in general wireless communications, especially over high-mobility channels. In this paper, we investigate DD domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output OTFS (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain interference in a symbol-by-symbol manner. We also study the achievable rate performance for the proposed scheme by leveraging the information-theoretical equivalent models. In particular, we show that the proposed scheme can achieve a near optimal performance in the high signal-to-noise ratio (SNR) regime. More importantly, scaling laws for achievable rates with respect to number of antennas and users are derived, which indicate that the achievable rate increases logarithmically with the number of antennas and linearly with the number of users. Our numerical results align well with our findings and also demonstrate a significant improvement compared to existing MU-MIMO schemes on OTFS and orthogonal frequency-division multiplexing (OFDM).

In this paper, we investigate two important issues of faster-than-Nyquist (FTN) signaling, namely, reduced-complexity detection and code design. Different from previous works, we consider these two issues jointly by designing a scheme that increases the minimum squared Euclidean distance of FTN signaling via repetition coding at a cost of an increased complexity. Furthermore, to reduce the rate loss of the repetition, we adopt the idea of spatially-coupling from coding theory to FTN signaling, and the resultant signaling scheme is therefore referred to as spatially-coupled faster-than-Nyquist (SC-FTN) signaling. The signal of SC-FTN signaling is generated in a continuous manner by interleaving and repeating the coded FTN signals and a graph-based iterative sliding-window detector is applied for signal detection. Both bounding and extrinsic information transfer chart analysis are provided to study the error-floor and convergence performances of SC-FTN signaling. These analyses unveil the intrinsic relationship between error floor, decoding threshold, and detection/decoding complexity, which provides guidelines for the designs of practical systems. Simulation results show that the promising error performance can be achieved with a simple FTN detection, where the bit error rate performance of coded SC-FTN signaling outperforms that of state-of-the-art coded FTN systems and the capacity of Nyquist signaling.

We investigate the application of reinforcement learning (RL) on online fountain codes, and propose two schemes to reduce the full-recovery overhead with limited feedback. First, we use RL in determining the optimal degree of coded symbols for a given number of feedback, and propose the RL-based degree determination (RL-DD), with the help of theoretical analysis of the relationship between recovery rate and buffer occupancy. Then we propose online fountain codes with no build-up phase using sectioned distribution (OFCNB-SD), where the encoder sends symbols whose degrees are sampled from different sections of an overall distribution, and the decoder is improved to utilize coded symbols that are not immediately decodable. We present theoretical analysis of OFCNB-SD, and introduce RL-based sectioned distribution (RL-SD) scheme where the sectioning of the overall distribution is optimized with RL. Simulation results show that our proposed schemes could achieve lower full-recovery overhead with limited feedback compared to existing schemes.

The reconfigurable intelligent surface (RIS) is useful to effectively improve the coverage and data rate of end-to-end communications. In contrast to the well-studied coverage-extension use case, in this paper, multiple RIS panels are introduced, aiming to enhance the data rate of multi-input multi-output (MIMO) channels in presence of insufficient scattering. Specifically, via the operator-valued free probability theory, the asymptotic mutual information of the large-dimensional RIS-assisted MIMO channel is obtained under the Rician fading with Weichselberger’s correlation structure, in presence of both the direct and the reflected links. Although the mutual information of Rician MIMO channels scales linearly as the number of antennas and the signal-to-noise ratio (SNR) in decibels, numerical results show that it requires sufficiently large SNR, proportional to the Rician factor, in order to obtain the theoretically guaranteed linear improvement. This paper shows that the proposed multi-RIS deployment is especially effective to improve the mutual information of MIMO channels under the large Rician factor conditions. When the reflected links have similar arriving and departing angles across the RIS panels, a small number of RIS panels are sufficient to harness the spatial degree of freedom of the multi-RIS assisted MIMO channels.

Orthogonal time frequency space (OTFS) modulation is an emerging technology to tackle time-frequency (TF) selective channel in high mobility scenarios. In OTFS, resource is multiplexed in the delay-Doppler (DD) domain. Based on the potential sparsity, separability, stability and compactness of the channel spreading function, OTFS is able to realize lower complexity of channel estimation, higher diversity and higher reliability compared with orthogonal frequency division multiplexing (OFDM). However, the channel spreading function for practical communication systems is rarely considered in the current OTFS-related literature. High-speed railway (HSR) is a typical high mobility scenario with trains travelling at over 200km/h, which has the potential to employ OTFS. To this end, the HSR channel spreading function is characterized and the performance of OTFS in HSR is evaluated based on the realistic channel measurement in this article. Firstly, the HSR channel in TF domain is measured based on the long term evolution railway (LTE-R) network. Then, the characteristics of the channel spreading function are analyzed. In particular, the impact of time domain channel fading on the spreading function is investigated. The characteristics of the measured spreading function are analyzed with the proposed metrics in railway viaduct and tunnel scenarios. Based on the above analysis, an algorithm for generating the channel spreading function is proposed. Feasibility of the proposed DD domain channel generation algorithm is verified through comparing metrics of which to those of the measured channel. By simulating the bit error rate (BER) and mean square channel estimation error performances of OTFS modulation in the practical band-limited systems, it is shown that the impacts of Doppler shift, delay, SFFT and time domain channel fading need be considered for the application of OTFS modulation, in contrast to the state-of-art DD domain channel generation scheme based on tap delay link (TDL) model. For example, compared to OTFS modulation under the ideal channel spreading function, OTFS modulation requires a signal gain greater than 5 dB under the practical channel spreading function affected by above factors, to achieve the same BER less than 10−2 under parameters defined in simulation.

This paper studies the estimation of cascaded channels in passive intelligent reflective surface (IRS)-aided multiple-input multiple-output (MIMO) systems employing hybrid precoders and combiners. We propose a low-complexity solution that estimates the channel parameters progressively. The angles of departure (AoDs) and angles of arrival (AoAs) at the transmitter and receiver, respectively, are first estimated using inductive matrix completion (IMC) followed by root-MUSIC-based super-resolution spectrum estimation. Forward-backward spatial smoothing (FBSS) is applied to address the coherence issue. Using the estimated AoAs and AoDs, the training precoders and combiners are then optimized and the angle differences between the AoAs and AoDs at the IRS are estimated using the least squares (LS) method followed by FBSS and the root-MUSIC algorithm. Finally, the composite path gains of the cascaded channel are estimated using on-grid sparse recovery with a small-size dictionary. The simulation results suggest that the proposed estimator can achieve improved channel parameter estimation performance with lower complexity as compared to several recently reported alternatives, thanks to the exploitation of the knowledge of the array responses and low-rankness of the channel using low-complexity algorithms at all the stages.

Terahertz (THz) communications are regarded as a pillar technology for the 6G systems, by offering multi-ten-GHz bandwidth. To overcome the huge propagation loss, THz ultra-massive MIMO systems with hybrid beamforming are proposed to offer high array gain. Notably, the adjustable phase shifters considered in most existing hybrid beamforming studies are power-hungry and difficult to realize in the THz band. Moreover, due to the ultra-massive antennas, full channel-state-information (CSI) is challenging to obtain. To address these practical concerns, in this paper, an energy-efficient dynamic-subarray with fixed phase shifters (DS-FPS) architecture is proposed for THz hybrid beamforming. To compensate for the spectral efficiency loss caused by the fixed phase of FPS, a switch network is inserted to enable dynamic connections. In addition, by considering the partial CSI, we propose a row-successive-decomposition (RSD) algorithm to design the hybrid beamforming matrices for DS-FPS. A row-by-row (RBR) algorithm is further proposed to reduce the computational complexity. Extensive simulation results show that, the proposed DS-FPS architecture with the RSD and RBR algorithms achieves much higher energy efficiency than the existing architectures. Moreover, the spectral efficiency of the DS-FPS architecture with the proposed algorithms is robust to the CSI error.

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial–satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.

Deep learning (DL)-based signal detection techniques have demonstrated significantly superior performance than the conventional methods in orthogonal time frequency space (OTFS) systems. Despite the effectiveness, existing methods using DL techniques are environment-specific. For instance, a detection model trained in one environment may become ineffective if environmental changes occur, e.g., user scheduling, inter-user interference and network scheduling. A re-training process is required to refine the model using numerous samples from the new/unseen environment, which is not always accessible in practice. To address the above concern, in this work, we propose an environment-robust approach to detect OTFS signals, by leveraging the property of the matching network (MatNet), referred as to OTFS-MatNet. Specifically, we propose to employ two functional blocks of MatNet to automatically capture generalized features shared among seen environments and the potential new environment. We also develop a novel loss function and a two-step training strategy to improve the generalized ability and detection accuracy. Therefore, the proposed OTFS-MatNet can realize accurate detection with a limited number of training samples, i.e., one sample from the new environment and the dataset from one seen environment. Numerous simulation results demonstrate that the developed OTFS-MatNet is significantly superior to state-of-the-art OTFS detection methods, in terms of improving detection accuracy and reducing the required number of training samples.

In this paper, we investigate the delay-Doppler (D-D) domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output orthogonal time frequency space (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain inter-ference in a symbol-by-symbol manner. We also demonstrate the theoretical results on the sum-rate performance of the proposed scheme. In particular, we show that the sum-rate of the proposed scheme increases logarithmically with the number of antennas, while increases linearly with the number of users. Our numerical results align well with our findings and also verify the effectiveness of the proposed scheme.

In this paper, we analyze the recently discovered delay-Doppler plane orthogonal pulse (DDOP), which is essential for delay-Doppler plane multi-carrier modulation waveform. In particular, we introduce a local orthogonality property of pulses corresponding to Weyl-Heisenberg (WH) subset and justify the DDOP's existence, in contrast to global orthogonality corresponding to WH set governed by the WH frame theory. Then, sufficient conditions for locally-orthogonal pulses are presented and discussed. Based on the analysis, we propose a general DDOP design. We also derive the frequency domain representation of the DDOP, and compare the DDOP-based orthogonal delay-Doppler division multiplexing (ODDM) modulation with other modulation schemes, in terms of TF signal localization. Interestingly, we show perfect local orthogonality property of the DDOP with respect to delay-Doppler resolutions using its ambiguity function.

In this paper, we propose a frame structure for single carrier transmission to estimate doubly-selective fading channels in high mobility environment. A delay-Doppler (DD) domain channel estimation method with adaptive threshold is introduced, which leverages the DD domain channel characteristics to enhance estimation accuracy. Based on the analysis of missed detection and false alarm probabilities, we derive asymptotically optimum thresholds for determining DD channel taps to minimize the mean square error of estimation. We demonstrate that the proposed method achieves a superior estimation performance gain up to 8–11 dB (dependent on the DD domain channel sparsity) compared to conventional channel interpolation techniques under doubly-selective fading channels.

In this paper, we study the potentials of spatially-spread orthogonal time frequency space (SS-OTFS) modulation for integrated sensing and communication (ISAC) transmissions. The most favourable feature of SS-OTFS modulation is that it forms beams according to a pre-determined angular grid, which is different from the conventional beamforming, where dedicated beams are formed according to the a priori information on the angle of departures (AoDs). According to the delay-Doppler domain channel characteristics, we first derive the input-output relationships for SS-OTFS-enabled ISAC system in a typical downlink multi-user MIMO (MU-MIMO) scenario. Based on those relationships, we further study the angular domain channel features and discuss the system design. Our numerical results have demonstrated the advantages of the proposed scheme over the conventional beamforming counterpart in terms of the signal-to-interference-plus-noise ratio (SINR).

This paper investigates multiuser multi-input single-output downlink symbiotic radio communication systems assisted by an intelligent reflecting surface (IRS). Different from existing methods ideally assuming the secondary user (SU) can jointly decode information symbols from both the access point (AP) and the IRS via multiuser detection, we consider a more practical SU that only non-coherent detection is available. To characterize the non-coherent decoding performance, a practical upper bound of the average symbol error rate (SER) is derived. Subsequently, we jointly optimize the beamformer at the AP and the phase shifts at the IRS to maximize the average sum-rate of the primary system taking into account the maximum tolerable SER constraint for the SU. To circumvent the couplings of variables, we exploit the Schur complement that facilitates the design of a suboptimal beamforming algorithm based on successive convex approximation. Our simulation results show that compared with various benchmark algorithms, the proposed scheme significantly improves the average sum-rate of the primary system, while guaranteeing the decoding performance of the secondary system.

In this paper, we investigate the design of a novel multicarrier (MC) modulation on delay-Doppler (DD) plane, to couple the modulated signal with a doubly-selective channel having DD resolutions. A key challenge for the design of DD plane MC modulation is to find a realizable pulse orthogonal with respect to the DD plane’s fine resolutions. To this end, we first indicate that a feasible DD plane MC modulation is essentially a type of staggered multitone modulation. Then, we propose an orthogonal delay-Doppler division multiplexing (ODDM) modulation, and design the corresponding transmit pulse. Most importantly, we prove that the proposed transmit pulse is orthogonal with respect to the DD plane’s resolutions and therefore a realizable DD plane orthogonal pulse does exist. Finally, we demonstrate the superior performance of the proposed ODDM modulation in terms of out-of-band radiation and bit error rate.

We propose a new family of spatially coupled product codes, called sub-block rearranged staircase (SR-staircase) codes. Each SR-staircase code block is constructed by encoding rearranged preceding code blocks and new information blocks, where the rearrangement involves sub-blocks decomposition and transposition. The proposed codes can be constructed to have each code block size of 1/q to that of the conventional staircase codes while having the same rate and component codes, for any positive integer q. In this regard, we can use strong algebraic component codes to construct SR-staircase codes with a similar or the same code block size and rate as staircase codes with weak component codes. Moreover, both waterfall and error floor performance can be further improved by using a large coupling width. The superior performance of the proposed codes is demonstrated through density evolution and error floor analysis as well as simulation.

Maximum-likelihood (ML) decoding can be used to obtain the optimal performance of error correction codes. However, the size of the search space and consequently the decoding complexity grows exponentially, making it impractical to be employed for long codes. In this paper, we propose an approach to constrain the search space for error patterns under a recently introduced near ML decoding scheme called guessing random additive noise decoding (GRAND). In this approach, the syndrome-based constraints which divide the search space into disjoint sets are progressively evaluated. By employing p constraints extracted from the parity check matrix, the average number of queries reduces by a factor of 2pwhile the error correction performance remains intact.

The number of minimum weight codewords, a.k.a error coefficient, is a good comparative measure for the block error rate (BLER) of a linear block code, in particular in a high SNR regime. The smaller the error coefficient, the lower the BLER. Unlike polar codes, the error coefficient of polarization-adjusted convolutional (PAC) codes cannot be determined easily due to the precoding stage. In this work, we propose an enumeration method that considers the impact of convolutional precoding on the minimum weight codewords of polar codes. This relative enumeration method simplifies the process significantly compared with conventional methods as the complexity analysis shows.

A spatially-coupled faster-than-Nyquist (SC-FTN) signaling is proposed in this paper. The signal of SC-FTN signaling is generated continuously by interleaving and repeating the coded FTN signals and a graph-based iterative sliding-window detector can be applied for signal detection. Both bounding and extrinsic information transfer chart analysis are provided to study the error performance of SC-FTN signaling, where performances of both error floor and convergence are considered. Those analyses unveil the intrinsic relationship between error floor, decoding threshold, and detection/decoding complexity, which provides guidelines for the designs of practical systems. Numerical results show that the promising error performance can be achieved with a simple FTN detection, where the bit error rate of coded SC-FTN signaling outperforms both state-of-art coded FTN systems and the BPSK capacity of Nyquist signaling.

Backscatter communication (BSC) has the potential to realise the ubiquitous proliferation of internet-of-things (IoT) technology. Bistatic BSC offers greater scalability and range over conventional BSC, making it a competent contender for its implementation in the IoT. We propose a robust, novel two-phase channel estimation (CE) protocol for bistatic BSC, which involves finding the required channel estimates at the multiantenna emitter and multiantenna reader and the appropriate pilot signal designs for each phase. We derived closed-form expressions for the emitter-to-tag channel estimate at the emitter and the tag-to-reader channel estimate at the reader without prior knowledge of channel and noise statistics. After that, we explore the utility of the proposed assessments by using them for transceiver (emitter-reader) design at the emitter and reader. Specifically, an optimal design maximising the reader signal-to-noise (SNR) is investigated. Finally, extensive numerical simulations verify the accuracy and utility of the proposed estimates.

Orthogonal time frequency space (OTFS) has recently attracted widespread attention for it leverages frequency dispersion as a source of diversity and mathematically unifies the classical multiple-access schemes. However, as a multi-carrier modulation in nature, OTFS is susceptible to the problem of high peak-to-average power ratio (PAPR), especially when the number of symbols is large in order to obtain a high Doppler resolution at the receiver. In this work, we recast the problem of PAPR reduction as constrained optimization of the precoding matrix. To efficiently solve the underlying nonconvex maximum-norm minimization problem, we propose an iterative algorithm based on block coordinate descent. Simulation results show that the proposed method can significantly mitigate the PAPR without unduly compromising the reliability of data transmission.

Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence ofrandomlink delays of the different users. We reveal that exploiting the link delays may potentially lead to a signal-to-interference-plus-noise ratio (SINR) gain, while transmitting the data symbols at FTN rates has the potential of increasing the degree-of-freedom (DoF). We then unveil the fundamental trade-off between the SINR and DoF. In particular, at a sufficiently high symbol rate, the SINR gain vanishes while the DoF gain achieves its maximum, where the achievable rate is almost$(1+\beta)$times higher than that of the conventional synchronous NOMA transmission in the high signal-to-noise ratio (SNR) regime, with$\beta $being the roll-off factor of the signaling pulse. Our simulation results verify our analysis and demonstrate considerable rate improvements over the conventional power-domain NOMA scheme.

In this paper, we propose a novel integrated sensing and communication (ISAC) transmission framework based on the spatially spread orthogonal time frequency space (SS-OTFS) modulation by considering the fact that communication channel strengths cannot be directly obtained from radar sensing. We first propose the concept of SS-OTFS modulation, where the key novelty is the angular domain discretization enabled by the spatial spreading/de-spreading. This discretization gives rise to simple and insightful effective models for both radar sensing and communication, which results in simplified designs for the related estimation and detection problems. In particular, we design simple beam tracking, angle estimation, and power allocation schemes for radar sensing, by utilizing the special structure of the effective radar sensing matrix. Meanwhile, we provide a detailed analysis on the pair-wise error probability (PEP) for communication, which unveils the key conditions for both precoding and power allocation designs for communication. Based on those conditions, we design a symbol-wise precoding scheme for communication based only on the delay, Doppler, and angle estimates from radar sensing, without thea prioriknowledge of the communication channel fading coefficients, and also propose a suitable power allocation. Furthermore, we notice that radar sensing and communication requires different power allocations. Therefore, we discuss the performances of both the radar sensing and communication with different power allocations and show that the power allocation should be designed leaning towards radar sensing in practical scenarios. The effectiveness of the proposed ISAC transmission framework is verified by our numerical results, which also agree with our analysis and discussions.

Hybrid beamforming for Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems is a promising technology for 6G space-air-ground integrated networks, which can overcome huge propagation loss and offer unprecedented data rates. With ultra-wide bandwidth and ultra-large-scale antennas array in THz band, the beam squint becomes one of the critical problems which could reduce the array gain and degrade the data rate substantially. However, the traditional phase-shifters-based hybrid beamforming architectures cannot tackle this issue due to the frequency-flat property of the phase shifters. In this paper, to combat the beam squint while keeping high energy efficiency, a novel dynamic-subarray with fixed true-time-delay (DS-FTTD) architecture is proposed. Compared to the existing studies which use the complicated adjustable TTDs, the DS-FTTD architecture has lower power consumption and hardware complexity, thanks to the low-cost FTTDs. Furthermore, a low-complexity row-decomposition (RD) algorithm is proposed to design hybrid beamforming matrices for the DS-FTTD architecture. Extensive simulation results show that, by using the RD algorithm, the DS-FTTD architecture achieves near-optimal array gain and significantly higher energy efficiency than the existing architectures. Moreover, the spectral efficiency of DS-FTTD architecture with the RD algorithm is robust to the imperfect channel state information.

A new class of spatially-coupled turbo-like codes (SC-TCs), dubbed generalized spatially coupled parallel concatenated codes (GSC-PCCs), is introduced. These codes are constructed by applying spatial coupling on parallel concatenated codes (PCCs) with a fraction of information bits repeated$q$times. GSC-PCCs can be seen as a generalization of the original spatially-coupled parallel concatenated codes proposed by Moloudiet al., 2017. To characterize the asymptotic performance of GSC-PCCs, we derive the corresponding density evolution equations and compute their decoding thresholds. The threshold saturation effect is observed and proven. Most importantly, we rigorously prove that the rate-$R$GSC-PCC ensemble with 2-state convolutional component codes achieves at least a fraction$1-\frac {R}{R+q}$of the capacity of the binary erasure channel (BEC) for repetition factor$q\geq 2$and this multiplicative gap vanishes as$q$tends to infinity. To the best of our knowledge, this is the first class of SC-TCs that are proven to be capacity-achieving. Further, the connection between the strength of the component codes, the decoding thresholds of GSC-PCCs, and the repetition factor is established. The superiority of the proposed codes with finite blocklength is exemplified by comparing their error performance with that of existing SC-TCs via computer simulations.

We propose a new family of spatially coupled product codes, called sub-block rearranged staircase (SR-staircase) codes. Each code block of SR-staircase codes is obtained by encoding rearranged preceding code blocks and new information blocks, where the rearrangement involves sub-blocks decomposition and transposition. The proposed codes can be constructed to have each code block size of$1/q$to that of the conventional staircase codes while having the same rate and component codes, for any positive integer$q$. In this regard, we can use strong algebraic component codes to construct SR-staircase codes with a similar or the same code block size and rate as staircase codes with weak component codes. We characterize the decoding threshold of the proposed codes under iterative bounded distance decoding (iBDD) by using density evolution. We also derive the conditions under which they achieve a better decoding threshold than that of staircase codes. Further, we investigate the error floor performance by analyzing the contributing error patterns and their multiplicities. Both theoretical and simulation results show that the designed SR-staircase codes outperform staircase codes in terms of waterfall and error floor while the performance can be further improved by using a large coupling width.

Terahertz (THz) communications with multi-GHz bandwidth are envisioned as a key technology for 6G systems. Ultra-massive (UM) MIMO with hybrid beamforming architectures are widely investigated to provide a high array gain to overcome the huge propagation loss at THz band. However, most of the existing hybrid beamforming architectures can only utilize the multiplexing offered by the multipath components, i.e., inter-path multiplexing, which is very limited due to the spatially sparse THz channel. In this paper, a widely-spaced multi-subarray (WSMS) hybrid beamforming architecture is proposed, which improves the multiplexing gain by exploiting a new type of intra-path multiplexing provided by the spherical-wave propagation among$k$widely-spaced subarrays, in addition to the inter-path multiplexing. The resulting multiplexing gain of WSMS architecture is$k$times of the existing architectures. For WSMS architecture, a novel design problem is formulated by optimizing the number of subarrays, subarray spacing, and hybrid beamforming matrices to maximize the spectral efficiency, which is decomposed into two subproblems. An optimal closed-form solution is derived for the first hybrid beamforming subproblem, while a dominant-line-of-sight-relaxation algorithm is proposed for the second array configuration subproblem. Extensive simulation results demonstrate that the WSMS architecture and proposed algorithms enhance the spectral efficiency substantially.

This paper investigates physical layer security for a large-scale WSN with random multiple access, where each fusion center in the network randomly schedules a number of sensors to upload their sensed data subject to the overhearing of randomly distributed eavesdroppers. We propose an uncoordinated random jamming scheme in which those unscheduled sensors send jamming signals with a certain probability to defeat the eavesdroppers. With the aid of stochastic geometry theory and order statistics, we derive analytical expressions for the connection outage probability and secrecy outage probability to characterize transmission reliability and secrecy, respectively. Based on the obtained analytical results, we formulate an optimization problem for maximizing the sum secrecy throughput subject to both reliability and secrecy constraints, considering a joint design of the wiretap code rates for each scheduled sensor and the jamming probability for the unscheduled sensors. We provide both optimal and low-complexity sub-optimal algorithms to tackle the above problem, and further reveal various properties on the optimal parameters which are useful to guide practical designs. In particular, we demonstrate that the proposed random jamming scheme is beneficial for improving the sum secrecy throughput, and the optimal jamming probability is the result of trade-off between secrecy and throughput. We also show that the throughput performance of the sub-optimal scheme approaches that of the optimal one when facing a stringent reliability constraint or a loose secrecy constraint.

Wireless powered mobile edge computing (WP-MEC) has been widely studied as a promising technology to liberate wireless terminals from the computation-intensive and energy-consuming tasks. This article considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a time slotted WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For the DTM algorithm, we characterize a throughput-backlog trade-off of [$\mathcal {O}(1/V)$,$\mathcal {O}(V)$] to indicate that the system throughput goes up as the queue backlog increases, where$V$is a control parameter between the system throughput and the queue backlog. However, we find that, as$V$goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog (i.e.,$\mathcal {O}(V)$). To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of [$\mathcal {O}(1/V)$,$\mathcal {O}((\log (V))^2)$] between the system throughput and the queue backlog. The simulation results demonstrate that IDTM retains close system throughput to DTM with only$\mathcal {O}((\log (V))^2)$queue backlog.

In this paper, an intelligent reflecting surface (IRS) is introduced to assist an unmanned aerial vehicle (UAV) communication system based on non-orthogonal multiple access (NOMA) for serving multiple ground users. We aim to minimize the average total system energy consumption by jointly designing the resource allocation strategy, the three dimensional (3D) trajectory of the UAV, as well as the phase control at the IRS. The design is formulated as a non-convex optimization problem taking into account the maximum tolerable outage probability constraint and the individual minimum data rate requirement. To circumvent the intractability of the design problem due to the altitude-dependent Rician fading in UAV-to-user links, we adopt the deep neural network (DNN) approach to accurately approximate the corresponding effective channel gains, which facilitates the development of a low-complexity suboptimal iterative algorithm via dividing the formulated problem into two subproblems and address them alternatingly. Numerical results demonstrate that the proposed algorithm can converge to an effective solution within a small number of iterations and illustrate some interesting insights: (1) IRS enables a highly flexible UAV’s 3D trajectory design via recycling the dissipated radio signal for improving the achievable system data rate and reducing the flight power consumption of the UAV; (2) IRS provides a rich array gain through passive beamforming in the reflection link, which can substantially reduce the required communication power for guaranteeing the required quality-of-service (QoS); (3) Optimizing the altitude of UAV’s trajectory can effectively exploit the outage-guaranteed effective channel gain to save the total required communication power enabling power-efficient UAV communications; (4) NOMA communications offer higher degrees of freedom (DoF) than that of the conventional orthogonal multiple access (OMA) scheme to minimize the average power consumption via optimizing the UAV’s trajectory.

Recently proposed orthogonal time frequency space (OTFS) modulation has been considered as a promising candidate for accommodating various emerging communication and sensing applications in high-mobility environments. In this paper, we propose a novel cross domain iterative detection algorithm to enhance the error performance of OTFS modulation. Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation. In doing so, the proposed algorithm exploits the time domain channel sparsity and the DD domain symbol constellation constraints. We evaluate the estimation/detection error variance in each domain for each iteration and derive the state evolution to investigate the detection error performance. We show that the performance gain due to iterations comes from the non-Gaussian constellation constraint in the DD domain. More importantly, we prove that the proposed algorithm can indeed converge and, in the convergence, the proposed algorithm can achieve almost the same error performance as the maximum-likelihood sequence detection even in the presence of fractional Doppler shifts. Furthermore, the computational complexity associated with the domain transformation is low, thanks to the structure of the discrete Fourier transform (DFT) kernel. Simulation results are consistent with our analysis and demonstrate a significant performance improvement compared to conventional OTFS detection methods.

Inspired by the orthogonal time frequency space (OTFS) modulation, in this paper, we consider designing a multicarrier (MC) modulation on delay-Doppler (DD) plane, to couple the modulated signal with a doubly-selective channel having DD resolutions. A key challenge for the design of DD plane MC modulation is to investigate whether a realizable pulse orthogonal with respect to the DD plane’s fine resolutions exists or not. To this end, we first indicate that a feasible DD plane MC modulation is essentially a type of staggered multitone modulation. Then, analogous to orthogonal frequency division multiplexing, we propose an orthogonal delay-Doppler division multiplexing (ODDM) modulation, and design the corresponding transmit pulse. Furthermore, we prove that the proposed transmit pulse is orthogonal with respect to the DD plane’s resolutions and therefore a realizable DD plane orthogonal pulse does exist. The orthogonality of this particular pulse significantly eases the derivation of the ODDM’s DD domain channel input-output relation, and yields a channel matrix with an elegant block-circulant-like structure. We demonstrate that the ODDM outperforms the OTFS in terms of out-of-band emission and bit error rate, by achieving perfect coupling between the modulated signal and the DD channel.

Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. OTFS channel estimation is firstly formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. To strike a balance between channel estimation performance and computational complexity, we further propose a two-dimensional (2D) off-grid SSR problem via decoupling the delay and Doppler shift estimations. In our developed 1D and 2D off-grid SBL-based channel estimation algorithms, the hyper-parameters are updated alternatively for computing the conditional posterior distribution of channels, which can be exploited to reconstruct the effective DD domain channel. Compared with the 1D method, the proposed 2D method enjoys a much lower computational complexity while only suffers a slight performance degradation. Simulation results verify the superior performance of the proposed channel estimation schemes over state-of-the-art schemes.

The orthogonal-time-frequency-space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To harvest the time and frequency diversity promised by OTFS, some promising detectors, especially message passing based ones, have been developed by taking advantage of the sparsity of the channel in the delay-Doppler domain. However, when the number of channel paths is relatively large or fractional Doppler shifts have to be considered, the complexity of existing detectors is a concern, and the existing message passing based detectors suffer from performance loss. In this work, we investigate the design of OTFS detectors based on the approximate message passing (AMP). In particular, leveraging the unitary AMP (UAMP), we design new detectors that enjoy the structure of the channel matrix and allow efficient implementation. In addition, the estimation of noise variance is incorporated into the UAMP-based detectors. Thanks to the robustness of UAMP relative to AMP, the UAMP-based detectors deliver superior performance, and outperform state-of-the-art detectors significantly. We also investigate iterative joint detection and decoding in a coded OTFS system, where the OTFS detectors are integrated into a powerful turbo receiver, leading to considerable performance gains.

In this paper, we investigate physical-layer security of the uplink millimeter wave communications for a cellular vehicle-to-everything (C-V2X) network comprised of a large number of base stations (BSs) and different categories of V2X nodes, including vehicles, pedestrians, and road side units. Considering the dynamic change and randomness of the topology of the C-V2X network, we model the roadways, the V2X nodes on each roadway, and the BSs by a Poisson line process, a 1D Poisson point process (PPP), and a 2D PPP, respectively. We propose two uplink association schemes for a typical vehicle, namely, the smallest-distance association (SDA) scheme and the largest-power association (LPA) scheme, and we establish a tractable analytical framework to comprehensively assess the security performance of the uplink transmission, by leveraging the stochastic geometry theory. Specifically, for each association scheme, we first obtain new expressions for the association probability of the typical vehicle, and then derive the overall connection outage probability and secrecy outage probability by calculating the Laplace transform of the aggregate interference power. Numerical results are presented to validate our theoretical analysis, and we also provide interesting insights into how the security performance is influenced by various system parameters, including the densities of V2X nodes and BSs. Moreover, we show that the LPA scheme outperforms the SDA scheme in terms of secrecy throughput.

This paper proposes an anomaly detection algorithm for a factory automation system, which jointly performs data pre-processing and time-delay autoencoder (TDAE) with a hybrid loss function. The source data are pre-processed by digital filters before feeding into a TDAE for anomaly detection. The digital filters extract analog signals from a variety of frequency bands to facilitate identifying anomalies. The pre-processed data then takes time-delay reform to explore temporal relationship of data signals. In addition, two anomaly diagnosis algorithms, a statistical based method and an autoencoder based method, are presented. Numerical results show that time-delay reform can improve the anomaly detection accuracy compared to the conventional autoencoder. Data pre-processing can further improve the anomaly detection accuracy. Moreover, we confirm that our anomaly diagnosis algorithms outperform traditional method that does not perform data pre-processing and time-delay reform.

The realization of practical intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the proper beamforming design exploiting accurate channel state information (CSI). However, channel estimation (CE) in IRS-MUC systems requires a significantly large training overhead due to the numerous reflection elements involved in IRS. In this paper, we adopt a deep learning approach to implicitly learn the historical channel features and directly predict the IRS phase shifts for the next time slot to maximize the average achievable sum-rate of an IRS-MUC system taking into account the user mobility. By doing this, only a low-dimension multiple-input single-output (MISO) CE is needed for transmit beamforming design, thus significantly reducing the CE overhead. To this end, a location-aware convolutional long short-term memory network (LA-CLNet) is first developed to facilitate predictive beamforming at IRS, where the convolutional and recurrent units are jointly adopted to exploit both the spatial and temporal features of channels simultaneously. Given the predictive IRS phase shift beamforming, an instantaneous CSI (ICSI)-aware fully-connected neural network (IA-FNN) is then proposed to optimize the transmit beamforming matrix at the access point. Simulation results demonstrate that the sum-rate performance achieved by the proposed method approaches that of the genie-aided scheme with the full perfect ICSI.

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. The OTFS channel estimation problem is formulated as an off-grid sparse signal recovery problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. Simulation results verify that compared with the on-grid approach, our proposed off-grid OTFS channel estimation scheme enjoys a 1.5 dB lower normalized mean square error.

Hybrid beamforming for Terahertz (THz) ultra-massive MIMO (UM-MIMO) systems is a promising technology for 6G networks, which can overcome huge propagation loss and offer unprecedented data rates. With ultra-wide band-width in THz band, the beam squint becomes one of critical problems which could reduce the array gain and degrade the data rate. However, the traditional phase-shifters-based hybrid beamforming architectures cannot tackle this issue due to the frequency-flat property of the phase shifters. In this paper, to combat this beam squint yet with reduced power consumption, a novel dynamic-subarray with fixed-true-time-delay (DS-FTTD) architecture is proposed. Furthermore, a low-complexity row-decomposition (RD) algorithm is developed for the DS-FTTD architecture. Extensive simulation results show that, by using the RD algorithm, the DS-FTTD architecture achieves signifi-cantly higher array gain and spectral efficiency than the phase-shifters-based architectures. Meanwhile, the energy efficiency is substantially improved thanks to the low-cost FTTDs.

Deep learning (DL) has been envisioned as a plausible solution to spectrum sensing, demonstrating an influential role in dynamic spectrum access. Despite their effectiveness, existing DL based sensing methods are heavily environment-sensitive. In other words, the sensing model trained in one environment usually cannot be applied to another, and a large number of labeled samples from the new environment are required to re-train DL architectures. To address the above challenge, we propose a novel approach leveraging the matching network (MN) for environment-robust spectrum sensing (MN-ERSS). Specifically, to improve the quality of input signals of MN, we propose to use a cross-correlation feature of the cyclic prefix (CP) of orthogonal frequency division multiplexing (OFDM) signals as the input data. Then, we propose to employ an advanced technique of one-shot learning, i.e., MN, to automatically extract inherent features from input signals. Moreover, we propose a tailored training strategy to better utilize the data set from the previous environment. The proposed training strategy can accomplish a successful spectrum sensing with the data set from only one previous environment and one sample from the new/testing environment. To the best of our knowledge, this is the first to investigate the environment-robust spectrum sensing by exploring one-shot learning. Extensive simulation results demonstrate that the proposed MN-ERSS significantly outperforms state-of-the-art sensing approaches, i.e., achieving a higher sensing accuracy with only one sample from the testing environment and the data set from one previous environment.

Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditional communication systems, an IRS-MC system generally involves a cascaded channel with a sophisticated statistical distribution, which hinders the implementations of the Bayesian estimators. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a data-driven approach to realize the channel estimation. Specifically, we propose a convolutional neural network (CNN)-based deep residual network (CDRN) to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. In the proposed CDRN, a CNN denoising block equipped with an element-wise subtraction structure is designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously, which further improves the estimation accuracy. Simulation results demonstrate that the proposed method can almost achieve the same estimation accuracy as that of the optimal minimum mean square error (MMSE) estimator requiring the knowledge of the channel distribution.

In this paper, we investigate the impacts of transmitter and receiver windows on orthogonal time-frequency space (OTFS) modulation and propose a window design to improve the OTFS channel estimation performance. Assuming ideal pulse shaping filters at the transceiver, we first identify the role of window in effective channel and the reduced channel sparsity with conventional rectangular window. Then, we characterize the impacts of windowing on the effective channel estimation performance for OTFS modulation. Based on the revealed insights, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver to effectively enhance the sparsity of the effective channel. As such, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in channel estimation compared with that of the rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation performance over the conventional rectangular or Sine windows.

We introduce generalized spatially coupled parallel concatenated codes (GSC-PCCs), a class of spatially coupled turbo-like codes obtained by coupling parallel concatenated codes (PCCs) with a fraction of information bits repeated before the PCC encoding. GSC-PCCs can be seen as a generalization of the original spatially coupled parallel concatenated convolutional codes (SC-PCCs) proposed by Moloudi et al. [1]. To characterize the asymptotic performance of GSC-PCCs, we derive the corresponding density evolution equations and compute their decoding thresholds. We show that the proposed codes have some nice properties such as threshold saturation and that their decoding thresholds improve with the repetition factor$q$. Most notably, our analysis suggests that the proposed codes asymptotically approach the capacity as$q$tends to infinity with any given constituent convolutional code.

In 5G mMTC system, massive user equipment (UE) access network simultaneously by contending a common RACH resource. It is thus of significant importance for network to estimate the time-of-arrival (ToA) of each UE efficiently based on preamble allocated for Physical Random Access Channel(PRACH) so that UE can achieve uplink synchronization with network reliably. In this paper, we focus on improving the accuracy and performance of ToA estimation. Considering the phase ambiguity problem due to varied propagation delay defined as ToA during detection of preamble signals generated by UE, we propose a ToA estimation technique to compensate phase transition with a set of pre-determined phase corrector vectors(PCV) associated with all possible ToA values. Furthermore, we optimize PCVs by build a reduced and essential PCV candidate as a smaller search space for more efficient phase recover and ToA estimation. Simulation results show that the performance of proposed method and its variants can fulfill the requirements of 3GPP standard with lower computational complexity.

Non-orthogonal multiple access (NOMA) has been widely recognized as a promising multiple access scheme for realizing next generation wireless communications. Unlike existing NOMA schemes assuming perfectly time synchronized user's signals received at the base station (BS), in this paper, we investigate the achievable rates of uplink NOMA with asynchronized transmission. By invoking Szegö's Theorem, we derive both the upper- and lower-bounds of the achievable rates of asynchronized NOMA (aNOMA) systems. In particular, we reveal that the derived lower-bound is essentially the achievable rate for conventional synchronized NOMA systems, which indicates that the asynchronization is not necessarily a foe. More specifically, we show that aNOMA systems are superior to conventional NOMA systems in terms of the achievable rates with non-sinc shaping pulses. Important insights are also unveiled based on the derived bounds. Simulation results confirm the validity of our derived analysis and demonstrate considerable achievable rates gains of aNOMA systems over conventional NOMA systems.

Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems. However, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision from the simultaneous massive access. Despite that this collision problem has been treated in existing RACH schemes, these schemes usually organize IoT devices' transmission and re-transmission along with fixed parameters, thus can hardly adapt to time-varying traffic patterns. Without adaptation, the RACH procedure easily suffers from high access delay, high energy consumption, or even access unavailability. With the goal of improving the RACH procedure, this paper targets to optimize the RACH procedure in real-time by maximizing a long-term hybrid multi-objective function, which consists of the number of access success devices, the average energy consumption, and the average access delay. To do so, we first optimize the long-term objective in the number of access success devices by using Deep Reinforcement Learning (DRL) algorithms for different RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). The converging capability and efficiency of different DRL algorithms including Policy Gradient (PG), Actor-Critic (AC), Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG) are compared. Inspired by the results from this comparison, a decoupled learning strategy is developed to jointly and dynamically adapt the access control factors of those three access schemes. This decoupled strategy integrates predicted traffic into the learning process to improve training efficiency, where a Recurrent Neural Network (RNN) model is first employed to predict the real-time traffic values of the network environment, and then multiple DRL agents are employed to cooperatively configure parameters of each RACH scheme. Our results demonstrate that the decoupled strategy remarkably accelerate the training speedy.

This paper investigates robust and secure multiuser multiple-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can simultaneously reflect and harvest energy from the received signals. We study the joint design of beamformers at an access point (AP) and the phase shifts as well as the energy harvesting schedule at the IRS for maximizing the system sum-rate. The design is formulated as a non-convex optimization problem taking into account the wireless energy harvesting capability of IRS elements, secure communications, and the robustness against the impact of channel state information (CSI) imperfection. Subsequently, we propose a computationally-efficient iterative algorithm to obtain a suboptimal solution to the design problem. In each iteration,$\mathcal {S}$-procedure and the successive convex approximation are adopted to handle the intermediate optimization problem. Our simulation results unveil that: 1) there is a non-trivial trade-off between the system sum-rate and the self-sustainability of the IRS; 2) the performance gain achieved by the proposed scheme is saturated with a large number of energy harvesting IRS elements; 3) an IRS equipped with small bit-resolution discrete phase shifters is sufficient to achieve a considerable system sum-rate of the ideal case with continuous phase shifts.

Online fountain codes (OFC) have attracted much attention for their good intermediate performance, which is important for receivers with low-complexity requirement. However, low-complexity receivers generally have limited buffer size to store coded symbols that have not been fully decoded yet, as well as limited power budget for feedback transmissions. In this paper, we propose improved transmission schemes for online fountain codes to reduce the buffer occupancy and feedback transmissions. Firstly, we analyze the relationship between buffer occupancy and overhead as well as the relationship between recovery rate and overhead for online fountain codes. Motivated by the analysis, we propose the weighted online fountain codes (WOFC) which can adapt to various buffer sizes by adjusting the weight to control the probability that a coded symbol can be fully processed immediately, and analyze its performance. Then we further propose weighted online fountain codes with low feedback (WOFC-LF), which utilize the proposed analysis to estimate the recovery rate, and reduce feedback transmissions. Simulation results verify the effectiveness of the analysis for both OFC and WOFC, and demonstrate the superior performance of WOFC-LF with limited buffer size and feedback transmissions.

This paper investigates the design and performance of delayed bit-interleaved coded modulation (DBICM) with low-density parity-check (LDPC) codes. For Gray labeled square M-ary quadrature amplitude modulation (QAM) constellations, we investigate the optimal delay scheme with the largest spectrum efficiency of DBICM for a fixed maximum number of delayed time slots and a given signal-to-noise ratio. When analyzing the capacity of DBICM, we find two important properties: the capacity improvement due to delayed coded bits being mapped to the real and imaginary parts of the transmitted symbols are independent of each other; a pair of delay schemes with delayed coded bits having identical bit-channel capacity lead to equivalent DBICM capacity. Using these two properties, we efficiently optimize the delay scheme for any uniform Gray-QAM systems. Furthermore, these two properties enable efficient LDPC code designs regarding unequal error protection via bit-channel type classifications. Moreover, we use protograph-based extrinsic information transfer charts to jointly optimize degree distributions and channel assignments of LDPC codes and propose a constrained progressive edge growth like algorithm to jointly construct LDPC codes and bit-interleavers for DBICM, taking distinctive bit-channel's capacity into account. Simulation results demonstrate that the designed LDPC coded DBICM systems significantly outperform LDPC coded BICM systems.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for a massive number of low-throughput low-cost devices in delay-tolerant applications with low power consumption. To provide reliable connections with extended coverage, a repetition transmission scheme is introduced to NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, the energy harvesting is considered as a promising solution to support energy sustainability in the NB-IoT network. In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission. We model the temporal dynamics of the energy level as a birth-death process, derive the energy availability of each IoT device, and examine its dependence on the energy storage capacity and the repetition value. We show that in certain scenarios, the energy availability remains unchanged despite randomness in the energy harvesting. We also derive the exact expression for the RACH success probability of a randomly chosen IoT device under the derived energy availability, which is validated under different repetition values via simulations. We show that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario.

A deep learning assisted sum-product detection algorithm (DL-SPDA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to approach the maximum a posterior probabilities (MAP) error performance. In specific, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not considered by the conventional detector with a limited complexity. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a turbo equalization receiver. Furthermore, we propose a compatible training technique to improve the detection performance of the proposed DL-SPDA with turbo equalization. In particular, the neural network is optimized in terms of the mutual information between the transmitted sequence and the extrinsic information. We also investigate the maximum-likelihood bit error rate (BER) performance of a finite length coded FTN system. Simulation results show that the error performance of the proposed algorithm approaches the MAP performance, which is consistent with the analytical BER.

In this paper, we study a class of spatially coupled turbo codes, namely partially information- and partially parity-coupled turbo codes. This class of codes enjoy several advantages such as flexible code rate adjustment by varying the coupling ratio and the encoding and decoding architectures of the underlying component codes can remain unchanged. For this work, we first provide the construction methods for partially coupled turbo codes with coupling memory m and study the corresponding graph models. We then derive the density evolution equations for the corresponding ensembles on the binary erasure channel to precisely compute their iterative decoding thresholds. Rate-compatible designs and their decoding thresholds are also provided, where the coupling and puncturing ratios are jointly optimized to achieve the largest decoding threshold for a given target code rate. Our results show that for a wide range of code rates, the proposed codes attain close-to-capacity performance and the decoding performance improves with increasing the coupling memory. In particular, the proposed partially parity-coupled turbo codes have thresholds within 0.0002 of the BEC capacity for rates ranging from 1/3 to 9/10, yielding an attractive way for constructing rate-compatible capacity-approaching channel codes.

In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

In this paper, we investigate the impacts of transmitter and receiver windows on the performance of orthogonal time-frequency space (OTFS) modulation and propose window designs to improve the OTFS channel estimation and data detection performance. In particular, assuming ideal pulse shaping filters at the transceiver, we derive the impacts of windowing on the effective channel and its estimation performance in the delay-Doppler (DD) domain, the total average transmit power, and the effective noise covariance matrix. When the channel state information (CSI) is available at the transceiver, we analyze the minimum squared error (MSE) of data detection and propose an optimal transmitter window to minimize the detection MSE. The proposed optimal transmitter window can be interpreted as a mercury/water-filling power allocation scheme, where the mercury is firstly filled before pouring water to pre-equalize the time-frequency (TF) domain channels. When the CSI is not available at the transmitter but can be estimated at the receiver, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver, which can effectively enhance the sparsity of the effective channel in the DD domain. Thanks to the enhanced DD domain channel sparsity, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in both channel estimation and data detection compared with that of rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation and data detection performance over the conventional rectangular window design.

In this paper, we propose partially information coupled bit-interleaved polar coded modulation (PIC-BIPCM), which is a class of spatially coupled polar coding schemes designed for$2^{Q}$-ary modulations. Specifically, we consider PIC-BIPCM schemes respectively constructed with three BIPCM schemes: direct BIPCM, punctured BIPCM, and multi-kernel BIPCM. We analyze the error performance of the proposed PIC-BIPCM over the binary erasure channel (BEC) via density evolution. With the analysis as a guideline, we jointly design the positions of coupled bits and the modulation bit-mapper by taking into account the partial polarization of finite length polar codes as well as the unequal error protection of high order modulations. Simulation results demonstrate significant performance improvement of the proposed PIC-BIPCM over the uncoupled BIPCM on both BEC and AWGN channels.

Frequency-hopping (FH) MIMO radar is recently introduced as an underlying system for realizing dual-function radar-communication (DFRC), increasing communication symbol rates to multiples of the radar pulse repetition frequency. As a newly conceived DFRC system, many realistic issues, such as channel estimation and synchronization, are not effectively solved yet. In this paper, we develop a multi-antenna receiver-based downlink communication scheme for the FH-MIMO DFRC, addressing the above issues in multi-path channels. By exploring the unique FH-MIMO radar waveform, we suppress both inter-antenna and inter-hop interference, and introduce minimal constraints on the radar waveform to facilitate DFRC. We then develop accurate estimation methods for timing offset and channel parameters. These methods are further employed to design reliable demodulation methods. We also derive performance bounds for the proposed estimation methods and embedded communications. Simulation results validate the efficacy of our receiving scheme, showing that the performance of estimators and data communications approaches analytical bounds.

To provide reliable transmissions with flexible rates for satellite communications, this paper presents a novel method of constructing multi-rate quasi-cyclic low-density parity-check (LDPC) codes. The basic idea is to generate low-rate codes from a high-rate mother code by combining shortening and extending, which ensures that the generated code family owns the same code length, in order to maintain the same frame structure. The code construction involves the design of base matrices and exponent matrices for the designed codes. A progressive row elimination and addition algorithm is proposed for designing the code base matrices from a high rate to low rates. This algorithm leads to the nested and systematic structure of the parity-check matrices, which are desirable for practical implementations of their encoders and decoders, while ensuring the optimal decoding thresholds. In addition, we construct a circulation coefficient matrix based on finite fields and select the optimal rows in this matrix to construct exponent matrices while considering of cycle structures. We demonstrate that the designed codes achieve better performance for all the code rates than the LDPC codes in DVB-S2X standards. In addition, the proposed codes do not exhibit error floors for their block error rates down to 10−5.

The two-user Gaussian interference channel (G-IC) is revisited, with a particular focus on practically amenable discrete input signalling and treating interference as noise (TIN) receivers. The corresponding deterministic interference channel (D-IC) is first investigated and coding schemes that can achieve the entire capacity region of the D-IC under TIN are proposed. These schemes are thensystematicallytranslated into multi-layer superposition coding schemes based on purely discrete inputs for the real-valued G-IC. Our analysis shows that the proposed scheme is able to achieve theentirecapacity region to within a constant gap for all channel parameters. To the best of our knowledge, this is the first constant-gap result under purely discrete signalling and TIN for the entire capacity region and all the interference regimes. Furthermore, the approach is extended to obtain coding schemes based on discrete inputs for the complex-valued G-IC. For such a scenario, the minimum distance and the achievable rate of the proposed scheme under TIN are analyzed, which takes into account the effects of random phase rotations introduced by the channels. Simulation results show that our scheme is capable of approaching the capacity region of the complex-valued G-IC and significantly outperforms Gaussian signalling with TIN in various interference regimes.

Orthogonal time frequency space (OTFS) modulation is a recently developed multi-carrier multi-slot transmission scheme for wireless communications in high-mobility environments. In this paper, the error performance of coded OTFS modulation over high-mobility channels is investigated. We start from the study of conditional pairwise-error probability (PEP) of the OTFS scheme, based on which its performance upper bound of the coded OTFS system is derived. Then, we show that the coding improvement for OTFS systems depends on the squared Euclidean distance among codeword pairs and the number of independent resolvable paths of the channel. More importantly, we show that there exists a fundamental trade-off between the coding gain and the diversity gain for OTFS systems, i.e., the diversity gain of OTFS systems improves with the number of resolvable paths, while the coding gain declines. Furthermore, based on our analysis, the impact of channel coding parameters on the performance of the coded OTFS systems is unveiled. The error performance of various coded OTFS systems over high-mobility channels is then evaluated. Simulation results demonstrate a significant performance improvement for OTFS modulation over the conventional orthogonal frequency division multiplexing (OFDM) modulation over high-mobility channels. Analytical results and the effectiveness of the proposed code design are also verified by simulations with the application of both classical and modern codes for OTFS systems.

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

Massive multiple-input multiple-output (MIMO) systems with 1-bit analog-to-digital converters (ADCs) are promising to reduce the energy consumption. However, the serious quantization error caused by 1-bit ADCs will potentially limit the feasibility of high order modulations. This paper focuses on the analysis of the high order phase-shift keying (PSK) signal transmission in the 1-bit ADC massive MIMO system. Firstly, assuming ideal channel estimation and a single mobile station (MS), we theoretically prove that with an asymptotically large number of antennas at the base station, PSK signals with arbitrary modulation order can be recovered in the 1-bit ADC massive MIMO system. Secondly, we analyze the impact of pilot based channel estimation on the recovery of the high order PSK signals, which leads to a periodic asymptotic detection phase error (ADPE) at high signal to noise ratio (SNR). Furthermore, we also propose to optimize the pilot sequence for minimizing the cumulative absolute ADPE. Finally, the analysis is extended to the multi-MS case and the performance with different pilot patterns is discussed. Simulation results validate our analysis and show that using our proposed optimized pilot sequence can significantly improve the detection performance for both single-MS and multi-MS 1-bit ADC massive MIMO systems.

In this paper, we consider the application of intelligent reflecting surface (IRS) in unmanned aerial vehicle (UAV)-based orthogonal frequency division multiple access (OFDMA) communication systems, which exploits both the significant beamforming gain brought by the IRS and the high mobility of UAV for improving the system sum-rate. The joint design of UAV's trajectory, IRS scheduling, and communication resource allocation for the proposed system is formulated as a non-convex optimization problem to maximize the system sum-rate while taking into account the heterogeneous quality-of-service (QoS) requirement of each user. The existence of an IRS introduces both frequency-selectivity and spatial-selectivity in the fading of the composite channel from the UAV to ground users. To facilitate the design, we first derive the expression of the composite channels and propose a parametric approximation approach to establish an upper and a lower bound for the formulated problem. An alternating optimization algorithm is devised to handle the lower bound optimization problem and its performance is compared with the benchmark performance achieved by solving the upper bound problem. Simulation results unveil the small gap between the developed bounds and the promising sum-rate gain achieved by the deployment of an IRS in UAV-based communication systems.

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.

In this paper, we study covert communications between a pair of legitimate transmitter-receiver against a watchful warden over slow fading channels. There coexist multiple friendly helper nodes who are willing to protect the covert communication from being detected by the warden. We propose an uncoordinated jammer selection scheme where those helpers whose instantaneous channel gains to the legitimate receiver fall below a pre-established selection threshold will be chosen as jammers radiating jamming signals to defeat the warden. By doing so, the detection accuracy of the warden is expected to be severely degraded while the desired covert communication is rarely affected. We then jointly design the optimal selection threshold and message transmission rate for maximizing covert throughput under the premise that the detection error of the warden exceeds a certain level. Numerical results are presented to validate our theoretical analyses. It is shown that the multi-jammer assisted covert communication outperforms the conventional single-jammer method in terms of covert throughput, and the maximal covert throughput improves significantly as the total number of helpers increases, which demonstrates the validity and superiority of our proposed scheme.

This paper investigates multiuser multi-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can harvest power from the received signals. We study the joint design of the beamformer at an access point (AP) and the phase shifts and the power harvesting schedule at an IRS for maximizing the system sum-rate. The design is formulated as a non-convex optimization problem taking into account the capability of IRS elements to harvest wireless power for realizing self-sustainability. Subsequently, we propose a computationally-efficient alternating algorithm to obtain a suboptimal solution to the design problem. Our simulation results unveil that: 1) there is a non-trivial trade-off between the system sum-rate and self-sustainability in IRS-assisted systems; 2) the performance gain achieved by the proposed scheme is improved with an increasing number of IRS elements; 3) an IRS equipped with small bit-resolution discrete phase shifters is sufficient to achieve a considerable system sumrate of an ideal case with continuous phase shifts.

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

In this paper, we propose the use of intelligent reflecting surface (IRS) in unmanned aerial vehicle (UAV)- based orthogonal frequency division multiple access (OFDMA) communication systems. The proposed scheme exploits both the rich beamforming gain brought by the IRS and the high mobility of UAV for improving the system sum-rate. The joint design of UAV's trajectory, IRS scheduling, and communication resource allocation for the proposed system is formulated as a non-convex optimization problem to maximize the system sum-rate. The existence of an IRS introduces both frequency selectivity and spatial-selectivity in the fading of the composite channel from the UAV to ground users. To facilitate the design, we first derive the expression of the composite channel gain and propose a parametric approximation approach to establish a lower bound for the formulated problem. An alternating optimization algorithm is devised to handle the lower bound optimization problem. Simulation results unveil the promising sum-rate gain achieved by the deployment of an IRS in UAV-based communication systems.

Hybrid beamforming for terahertz (THz) communications is a promising technology for beyond 5G wireless systems, which has great potential to overcome very high propagation loss, mitigate hardware complexity, and achieve unprecedented data rates. In this paper, a dynamic-subarray (DS) architecture is investigated for THz hybrid beamforming systems. Specifically, we analyze both quantized-phase shifters (QPS) with finite phase levels, and fixed-phase shifters (FPS) with unaltered phases in the DS architecture, which significantly reduce hardware complexity and power consumption compared to using the infinite-resolution phase shifters (IPS). Furthermore, a generic low-complexity row-by-row (RBR) algorithm is derived for the proposed DS-structured hybrid beamforming with QPS and FPS. Extensive simulation results demonstrate that the RBR algorithm improves spectral efficiency and substantially reduces computational complexity. Compared to the DS-IPS, the DS-QPS architecture can achieve 98% spectral efficiency and 136% energy efficiency. In addition, we show that while the spectral efficiency of the DS-FPS architecture is 21% lower than the DS-QPS counterpart, the low-cost FPS provides 30% higher energy efficiency than QPS.

Location awareness becomes an essential requirement for numerous applications and services in the future wireless communications. This paper addresses the problem of joint localization and synchronization in a network with cooperative nodes. The focus of this work is on the design of a low-complexity yet near-optimal message-passing implementations. To avoid the high-complexity of applying particle filtering-based approaches, we suitably augment the factor graph by introducing auxiliary variables. Then we propose a hybrid method that combines belief propagation (BP) and mean field (MF) message passing, which are used for message updating in the synchronization and localization parts of the factor graph, respectively. As a result, all messages on factor graph can be represented in parametric forms such that the proposed algorithm features a significantly low complexity while achieving near-optimal positioning performance.

We present a novel monostatic backscatter communication (BSC) protocol for multiple-input-multiple-output (MIMO) reader to detect signals from a single-antenna tag. Understanding tags resource constraints, it involves new preamble designing that neither requires pilot transmission from tag nor channel estimation at reader. So, considering the maximum likelihood detector, we derive closed-form expressions for the optimal detection threshold and bit error rate (BER) by exploiting practically-motivated approximations for short-range BSC channels. Thereafter, analytical insights on the globally-optimal backscattering coefficients (BC) at tag for minimizing BER are provided. Lastly, the analysis is validated via simulations, while shedding key insights on impact of the reader’s array-size, rice factor, and BSC channel gains on optimal BC and achievable BER.

In this paper, a novel code based channel shortening (CCS) algorithm for faster-than-Nyquist (FTN) signaling is proposed, where a special type of convolutional codes is used to absorb the channel memory. In contrast to conventional schemes, the proposed CCS algorithm performs joint detection and decoding (JDD) based only on the code trellis by exploiting the code structure. Therefore, the proposed CCS algorithm provides a new view for channel shortening (CS) techniques, i.e., absorbing the channel memory by using channel codes. According to the code structure, we derive the path metric of the proposed CCS algorithm. Furthermore, we introduce a design of self-concatenated convolutional codes (SECCCs) for FTN signaling based on the CCS algorithm. Simulation results show that with a 16-states BCJR algorithm for JDD, the bit error rate (BER) performance of the designed SECCC incorporated with FTN signaling is only around 0.75 dB away from the Shannon limit of the shaping pulse, and the required signal-to-noise ratio (SNR) is below the BPSK capacity limit of Nyquist signaling.

Both faster than Nyquist (FTN) signaling and non-orthogonal multiple access (NOMA) are promising next generation wireless communications techniques as a benefit of their capability of improving the system's spectral efficiency. This paper considers an uplink system that combines the advantages of FTN and NOMA. Consequently, an improved spectral efficiency is achieved by deliberately introducing both inter-symbol interference (ISI) and inter-user interference (IUI). More specifically, we propose a grant-free transmission scheme to reduce the signaling overhead and transmission latency of the considered NOMA system. To distinguish the active and inactive users, we develop a novel message passing receiver that jointly estimates the channel state, detects the user activity, and performs decoding. We conclude by quantifying the significant spectral efficiency gain achieved by our amalgamated FTN-NOMA scheme compared to the orthogonal transmission system, which is up to 87.5%.

In this paper, we study the achievable rate performance and the design of using purely discrete input signaling and treating interference as noise (TIN) for the two-user complex Gaussian interference channel (G-IC), where the channel introduces random phase rotation for all links. To analyze the achievable rate performance under this scenario, we first look into the corresponding deterministic interference channel model and design schemes to achieve the entire capacity region under TIN. Then, we translate the scheme into a multi-layer superposition coding scheme based on discrete inputs for GIC and analyze the achievable rate under TIN. Our simulation results show that our scheme is capable of approaching the (outer bound of) capacity region of the complex G-IC and performs significantly better than Gaussian signalling with TIN.

Partially information coupled turbo codes (PICTCs) is a class of spatially coupled turbo codes that can approach the BEC capacity while keeping the encoding and decoding architectures of the underlying component codes unchanged. However, PIC-TCs have significant rate loss compared to its component rate-1/3turbo code, and the rate loss increases with the coupling ratio. To absorb the rate loss, in this paper, we propose the partially information coupled duo-binary turbo codes (PIC-dTCs). Given a rate-1/3 turbo code as the benchmark, we construct a duo-binary turbo code by introducing one extra input to the benchmark code. Then, parts of the information sequence from the original input are coupled to the extra input of the succeeding code blocks. By looking into the graph model of PICdTC ensembles, we derive the exact density evolution equations of the PIC-dTC ensembles, and compute their belief propagation decoding thresholds on the binary erasure channel. Simulation results verify the correctness of our theoretical analysis, and also show significant error performance improvement over the uncoupled rate-1/3 turbo codes and existing designs of spatially coupled turbo codes.

Seven decades after the foundation of classical information theory and the invention of the transistor that launched the digital communication and computing revolutions, we are entering a new era of quantum information science and engineering (QISE). Despite holding its impressive sway for nearly 60 years, the celebrated Moore’s law is beginning to hit physical limits, as the ever-shrinking transistor size is making it necessary to account for quantum effects. Concurrently, the growing demand for high-rate processing is imposing unsustainable power and heat dissipation requirements. Thus, there is an urgent need to develop quantum information processing systems that can circumvent the limitations of existing technology.

Terahertz (THz) communications are envisioned as a key technology for 6G wireless systems, owing to an unprecedented promised multi-GHz bandwidth. While THz band suffers from huge propagation losses, large arrays of sub-millimeter wavelength antennas can be realized in ultra-massive multiple-input multiple-output (UM-MIMO) systems to enhance the received power and overcome the distance limitation. In this paper, a dynamic array-of-subarrays (DAoSA) hybrid precoding architecture is proposed to reduce the power consumption while meeting the data rate requirement in THz UM-MIMO systems. The connections between RF chains and subarrays are intelligently adjusted through a network of switches. First, to solve the intractable DAoSA hybrid precoding problem, element-by-element (EBE) and vectorization-based (VEC) algorithms are derived. Moreover, to determine the connections of the switches, near-optimal progressive stage-by-stage (PSBS), low-complexity alternating-selection (AS) and block-diagonal-search (BDS) algorithms are developed. Extensive simulation results show that both the EBE and VEC algorithms have higher spectral efficiency than existing hybrid precoding algorithms. Furthermore, the power consumption of the DAoSA architecture is substantially lessened, with PSBS, AS and BDS algorithms, respectively. The developed DAoSA architecture associated with proposed hybrid precoding and switch network design algorithms demonstrates a superior capability on balancing the spectral efficiency and power consumption.

In this paper, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system.

We propose a two-dimensional modified min-sum algorithm for the LDPC codes in the fifth generation (5G) networks standard to approach the error performance of the sum-product algorithm (SPA). In the proposed decoding algorithm, we adopt a partial self-correction method followed by message amplification to improve the reliability of the variable-to-check (V2C) messages. To further approach the performance of the maximum likelihood decoding for 5G short LDPC codes, we propose an enhanced quasi-maximum likelihood (EQML) decoding method. The proposed decoding method performs multiple rounds of decoding tests once the first decoding attempt fails, where the decoder inputs of the selected unreliable variable nodes are modified in each decoding test. A novel node selection method based on the sign fluctuation of V2C messages is proposed for the EQML decoding method. We also present a partial pruning stopping (PPS) rule to reduce the decoding complexity by deactivating part of the decoding tests once a valid codeword is found. A lower bound on the error performance is also derived by using the semi-analytical method. Simulation results show that the EQML decoding method outperforms the SPA with the same decoding complexity and other QML decoding methods, and it approaches the Polyanskiy-Poor-Verdú bound within 0.4 dB.

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes.

A novel code based channel shortening (CCS) algorithm for faster-than-Nyquist (FTN) signaling is proposed, where special convolutional codes are used to absorb the channel memory. These convolutional codes have a special type of generator matrix that allows previous code symbols to be determined by the current code trellis state and thus been referred to as output-retainable convolutional codes (ORCCs). Different from conventional schemes, the CCS algorithm performs joint detection and decoding (JDD) based only on the code trellis by exploiting the ORCC structure. Therefore, it provides a new view for channel shortening techniques, i.e., absorbing the channel memory by using channel codes. Properties of ORCCs are discussed. Based on these properties, we derive the bit error rate (BER) bound for the CCS algorithm. According to the bound, a code search algorithm is proposed to facilitate the code design. Furthermore, two concatenated codes based on ORCCs are designed. Simulation results show that with a 16-states BCJR algorithm for JDD, the BER performance of the designed self-concatenated convolutional code incorporated with FTN signaling is only 0.75 dB away from the Shannon limit of the shaping pulse, and the required signal-to-noise ratio is below the BPSK limit of Nyquist signaling.

This paper proposes a node-classified redundant decoding (NC-RD) algorithm based on the received sequence's channel reliability for high-density parity-check (HDPC) codes. Two preprocessing steps are proposed prior decoding. The variable nodes of the parity-check matrix are firstly classified by the k -median algorithm based on the number of shortest cycles associated with each variable node before decoding. Then, by searching among the automorphism group of the HDPC codes, we generate a list of permutations for bit positions by computing and sorting the permutation reliability metrics. The redundant decoder conducts the message-passing decoding according to the sorted permutations, which limit the unreliable information propagation for each permutation. Besides proposing a list decoding algorithm on top of the NC-RD algorithm to augment the decoder's performance, we show that the NC-RD algorithm can be transformed into a neural network system. More specifically, multiplicative tuneable weights are attached to the decoding messages to optimize the decoding performance. Simulation results of BCH codes over the AWGN channels show that the NC-RD algorithm provides a performance gain compared to the random redundant decoding algorithm. Additional decoding performance gain can be obtained by both the list decoding method and the neural network “learned” NC-RD algorithm.

Packet-based fronthaul (FH) transport networks are promising for future centralized radio access networks (C-RANs), which support statistical multiplexing via flow scheduling. With stringent requirements on FH delay, the FH network is time sensitive. Targeting to minimize the maximum FH delay of all packets, this paper investigates the optimized flow scheduling in the packet-based FH network. Due to the high complexity of optimal solutions, a heuristic higher rate flow scheduled later (HRSL) scheme is proposed to achieve good performance with a low complexity. The main idea is to schedule the higher-rated flow with a lower priority, because packets in the higher-rated flow have a larger solution space to be scheduled with a smaller delay. It is proved that using HRSL, each packet must have a transmission position and no packet will be discarded during scheduling. Moreover, the asymptotic delay of HRSL is derived when the number of flows is sufficiently large. We show that the delay performance of HRSL approaches to the optimal one and yields a reduction of up to 92.6% compared to existing schemes. The properties of HRSL are also verified via simulations. If the required delay is larger than the asymptotic delay performance, the FH network with HRSL can always deliver packets in time, no matter how many flows are scheduled and what the rates of flows are.

In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by γ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to M-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of M for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, M, and the number of users, K, the ESG of NOMA over OMA increases linearly with both M and K. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

Given the requirements of increased data rate and massive connectivity in the Internet-of-things (IoT) applications of the fifth-generation communication systems (5G), non-orthogonal multiple access (NOMA) was shown to be capable of supporting more users than OMA. As a further potential enhancement, the faster-than-Nyquist (FTN) signaling is also capable of increasing the symbol rate. Since NOMA and FTN signaling impose non-orthogonalities from different perspectives, it is possible to achieve further increased spectral efficiency by exploiting both. Hence we investigate the FTN-NOMA uplink in the context of random access. Although random access schemes reduce the signaling overheads as well as latency, they require the base station to identify active users before performing data detection. As both inter-symbol and inter-user interferences exist, performing optimal detection requires a prohibitively high complexity. Moreover, in typical mobile communication environments, the channel envelope of users fluctuates violently, which imposes challenges on the receiver design. To tackle this problem, we propose a joint user activity tracking and data detection algorithm based on the factor graph framework, which relies on a sophisticated amalgam of expectation maximization (EM) and hybrid message passing algorithms. The complexity of the algorithm advocated only increases linearly with the number of active users. Our simulation results show that the proposed algorithm is effective in tracking user activity and detecting data symbols in dynamic random access systems.

In vehicular networks of the future, sensing and communication functionalities will be intertwined. In this article, we investigate a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique. Aiming for realizing joint sensing and communication functionalities at road side units (RSUs), we present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of each vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. To improve the sensing accuracy while guaranteeing the downlink communication sum-rate, we further propose a power allocation scheme for multiple vehicles. Numerical results have shown that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance. Furthermore, the designed power allocation method is able to achieve a favorable performance trade-off between sensing and communication.

In massive multiple-input multiple-output (MIMO) systems with time shifted pilot (TSP) schemes, the inter-group interference caused by the pilot contamination can be eliminated when the number of base station (BS) antennas M approaches infinity. However, M is finite in practice and the effectiveness of the TSP is limited by channel estimation errors. In this paper, it is analytically shown that the mean square channel estimation error (MSCEE) of the TSP is dominated by the inter-group data interference. To reduce the MSCEE in the finite antenna massive MIMO systems, an interference cancellation based channel estimation for the TSP (IC-TSP) is proposed, where the dominant inter-group data interference is canceled based on BS cooperation. To show the advantage of the IC-TSP, the additional overhead of IC-TSP is evaluated by considering different M and the coherence time of BS-BS channels. Furthermore, the impact of sectorization and compressed sensing based BS-BS channel estimation are also discussed. We show that when 128 M 2048, with the inter-group data interference from the nearest two cell layers being canceled, the IC-TSP achieves a spectral efficiency gain of more than 1.2 bps/Hz over the TSP.

This paper studies physical-layer secure transmissions from a transmitter to a legitimate receiver against an eavesdropper over slow fading channels, taking into account the impact of finite blocklength secrecy coding. A comprehensive analysis and optimization framework is established to investigate secrecy throughput for both single- and multi-antenna transmitter scenarios. Both adaptive and non-adaptive design schemes are devised, in which the secrecy throughput is maximized by exploiting the instantaneous and statistical channel state information of the legitimate receiver, respectively. Specifically, optimal transmission policy, blocklength, and code rates are jointly designed to maximize the secrecy throughput. Additionally, null-space artificial noise is employed to improve the secrecy throughput for the multi-antenna setup with the optimal power allocation derived. Various important insights are developed. In particular, 1) increasing blocklength benefits both reliability and secrecy under the proposed transmission policy; 2) secrecy throughput monotonically increases with blocklength; 3) secrecy throughput initially increases but then decreases as secrecy rate increases, and the optimal secrecy rate maximizing the secrecy throughput should be carefully chosen in order to strike a good balance between rate and decoding correctness. Numerical results are eventually presented to verify theoretical findings.

In this paper, we study the communication problem of multiuser multiple-input single-output (MISO) broadcast channels with imperfect channel state information (CSI) at the transmitter. Zero-forcing precoding based on the imperfect CSI is adopted so that the channel can be transformed into a Gaussian interference channel. We consider a practical setting where only discrete input signalings are employed and all the receivers adopt single-user treating-interference-as-noise (TIN) decoding, as opposed to rate-splitting and successive interference cancellation. Under this setting, we first use the deterministic model to approximate the original channel model and develop communication schemes to achieve the entire capacity region. By translating the results of the deterministic model back to the MISO model, we develop a systematic way to design discrete input signalings for the original problem. Our simulation results show that our scheme is capable of approaching the (outer bound of) capacity region of the Gaussian interference channel.

In this paper, we propose a deep learning aided list approximate message passing (AMP) algorithm to further improve the user identification performance in massive machine type communications. A neural network is employed to identify a suspicious device which is most likely to be falsely alarmed during the first round of the AMP algorithm. The neural network returns the false alarm likelihood and it is expected to learn the unknown features of the false alarm event and the implicit correlation structure in the quantized pilot matrix. Then, via employing the idea of list decoding in the field of error control coding, we propose to enforce the suspicious device to be inactive in every iteration of the AMP algorithm in the second round. The proposed scheme can effectively combat the interference caused by the suspicious device and thus improve the user identification performance. Simulations demonstrate that the proposed algorithm improves the mean squared error performance of recovering the sparse unknown signals in comparison to the conventional AMP algorithm with the minimum mean squared error denoiser.

We present a success prioritized binary exponential backoff algorithm of non-persistent carrier-sense multiple access (NP-CSMA) for vast-area large-scale data gathering with power-limited wireless sensors in order to simultaneously gain the transmission and energy efficiencies of the wireless communication devices in this paper. The proposed medium access control (MAC) mechanism does not require any additional physical-layer functions such as multiple antennas, interference canceller, etc. It has high flexibility and scalability without any complex configuration in an autonomous and distributed manner. In this paper, we provide a performance analysis model to estimate the throughput and consumed energy for the proposed backoff algorithm. We show the validity of the performance analysis model by comparing the analysis results with the simulation results for the number of active nodes more than 500 of interest to us.

Quantum signal processing invokes the injection of abstract quantum mechanical frameworks into classical signal processing problems. In this work we apply this idea to the notion of optimal likelihood ratio tests within the context of the location verification problem. We first draw parallels with quantum mechanical measurements and the notion of generalized likelihood ratio measurements. As we show, these quite different measurement frameworks are mathematically similar since both can be described in the language of projections into subspaces - the projections removing the nuisance parameters of the underlying system in the latter case. We then show how the imposition of an `artificial' mathematical constraint, borrowed from a similar constraint imposed on quantum mechanics by the uncertainty principle, is likely to assist in machine-learning solutions of the location verification problem - such solutions being more useful in real-world deployments.

To provide reliable connections with extended coverage in NarrowBand-Internet of Things (NB-IoT), a repetition transmission scheme is introduced during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, energy harvesting from natural resources is considered to be a promising solution to support energy sustainability of NB-IoT network. In this work, we analyze RACH in the self-powered NB-IoT network taking into account the repeated preamble transmission and collision using stochastic geometry. We model the temporal dynamics of the energy level as a birth-death process, and we derive the energy availability of each IoT device and examine its dependence on the energy storage capacity, the cutoff value, and the repetition value. We also derive the exact expression for the RACH success probability of NB-IoT network under time correlated interference and the energy availability, which is validated under different repetition values via practical packet evolution simulations.

As an important component of resource management, admission control is vital to prevent the wireless network from congestion and ensure the quality of service (QoS). Mobile edge computing (MEC), which provides computing resources at the edge of radio access networks (RAN), is able to better support new mobile services. Therefore, enhanced admission control policies should be designed for mobile cellular networks based on MEC. In this paper, a novel MEC-assisted admission control mechanism is proposed from the perspective of convergence of communication and computation. In this mechanism, MEC computing resources are leveraged to pre-process the transmission content. The purpose is to reduce the consumption of wireless bandwidth and increase the number of accepted services. Next, the admission control process is modeled as a Markov decision process (MDP) with objective to maximize the long-term expected average effective throughput. In consideration of the large state space, a simulation-based optimization algorithm of MDP is adopted to obtain the optimal policy. Simulation results show that our proposed admission control mechanism achieves higher effective throughput than that without MEC computing resources. And the probability of accepted services can also be improved significantly. Furthermore, the optimal amount of MEC computing resources can be acquired according to the system traffic statistics.

The problem of downlink non-orthogonal multiple access (NOMA) scheme over fast fading channels is studied. A new class of downlink NOMA scheme is proposed, where each user's signals are encoded to a constellation corresponding to the same algebraic lattices from number fields and the transmitter sends the superposition of users' signals. The minimum product distance achieved by the proposed scheme with an arbitrary power allocation factor is investigated and its upper bounds are derived. Within this class, a family of NOMA schemes based on lattice partitions of the underlying ideal lattice is identified, whose minimum product distances can be easily controlled. Numerical results show that the scheme based on lattice partitions always results in the largest possible minimum product distance among the proposed class. Simulation results further indicate that the proposed scheme significantly outperforms the conventional NOMA scheme and the current state-of-the-art.

In centralized radio access network (C-RAN), the statistical multiplexing gain (SMG) of computing resource can be achieved. This paper focuses on analyzing the SMG considering the quantization granularity of computing resource which indicates the degree of resource sharing in practical C-RAN. Firstly, based on a spatial-temporal traffic model for multiple cells, the quantized model of computing resource is set up. Then, giving a system service threshold and defining the SMG as the ratio of the amount of computing resources deployed in distributed radio access network (D-RAN) and CRAN, the asymptotic SMG with quantized computing resource is derived. The impacts of the service threshold, the granularity of quantized computing resource and the average traffic load on the asymptotic SMG are analyzed. In the special case that the granularity of quantized computing resource is infinitely small, i.e., the computing resources are not quantized, the asymptotic SMG only depends on the service threshold and the fluctuation of spatial traffic load. Simulations are carried out to verify the correctness of the derivation of the asymptotic SMG with quantized computing resource. Simulation results show that when the service threshold gets higher, the asymptotic SMG increases. Moreover, the asymptotic SMG decreases obviously when the ratio of the granularity of quantized computing resource to the average traffic load increases over 1%. In addition, compared to a C-RAN with dramatic traffic load fluctuation in spatial domain, a moderate fluctuation can bring a higher asymptotic SMG.

In this paper, we propose a beamwidth control-based non-orthogonal multiple access (NOMA) scheme for hybrid millimeter wave (mmWave) communication systems. In particular, the proposed scheme allows multiple users in one NOMA group to share the same radio frequency chain and analog beam for superposition transmission. To overcome the physical limit of the narrow analog beam, a beamwidth control approach is proposed to widen the analog beamwidth to facilitate the formation of NOMA groups. Then, we characterize the main lobe power loss associated with the proposed beamwidth control and derive the asymptotically optimal analog beamformer to maximize the system sum-rate in the large number of antennas regime. The system sum-rate gain of the proposed beamwidth control-based NOMA scheme compared to a baseline scheme adopting time division multiple access (TDMA) is analyzed. Simulation results verify the accuracy of our performance analysis and unveil the importance of beamwidth control for practical mmWave NOMA systems.

This paper proposes a distributed hybrid millimeter wave (mmWave) scheme to exploit the structure of a Densely Deployed Distributed (DDD) small-cell-base-stations (SBSs) system for serving multiple users in a geographic area. Both the SBSs and the users are equipped with full access hybrid architectures with multi-antenna arrays and multiple radio frequency chains. Unlike the conventional cellular networks where users receive data streams from their nearest BSs, the users in our proposed scheme simultaneously receive data streams from different SBSs. With appropriate design of analog beamformers, co-channel multi-data-stream interference can be mitigated and the extra spatial degrees of freedom induced by the geographic distributed SBSs are exploited for data multiplexing. Analytical and simulation results show that the proposed scheme can improve the system sum-rate considerably, especially when the number of scattering components in millimeter wave channels is limited.

In this paper, we propose an enhanced quasi-maximum likelihood (EQML) decoder for short LDPC codes. The proposed EQML decoder selects unreliable variable nodes (VNs) and performs the reprocessing if the first belief propagation (BP) decoding attempt fails. To improve the decoding error rate performance, we propose a novel node selection method based on the sign fluctuation of VNs' extrinsic messages. We also present a partial pruning stopping (PPS) rule to reduce the decoding complexity by deactivating part of the decoding tests once a valid codeword is found. Simulation results show that the proposed PPS rule achieves 20% lower decoding complexity compared to the full list decoding without sacrificing the error rate performance. In addition, the proposed EQML decoder outperforms the augmented BP decoder for short LDPC codes and approaches the performance of the ML decoder within 0.3 dB in terms of the frame error rate.

This paper proposes a method to design low-density parity-check (LDPC) codes for delayed bit-interleaved coded modulation (DBICM). In the method, the code variable node (VN) degree distributions and the assignments of VNs with different degrees to DBICM subchannels are optimized via two cascaded differential evolution (DE) steps. In each step, to optimize VN degree distribution or channel assignment, a parity-check matrix is constructed, and the associated decoding threshold is calculated for each element in a generation. In constructing a parity-check matrix for each channel assignment, we propose a constraint PEGlike code construction method. Protograph-EXIT is employed to calculate the decoding threshold for each parity-check matrix. We apply the proposed method to construct irregular binary LDPC codes for both 16-QAM DBICM and BICM schemes. Simulation results demonstrate that the optimized LDPC codes are within 1 dB from the associated capacity limit at a bit error rate (BER) of 10-6. Besides, the LDPC coded DBICM achieves an SNR gain of 0.5 dB to 0.1 dB over BICM counterparts at a code rate ranges from 0.25 to 0.5.

A deep learning assisted sum-product detection algorithm (DL-SPA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm concatenates a neural network to the variable nodes of the conventional factor graph of the FTN system to help the detector converge to the a postenor probabilities based on the received sequence. More specifically, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not modeled by the conventional detector with a limited number of ISI taps. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a Turbo equalization. Furthermore, a simplified convolutional neural network is employed as the neural network function node to enhance the detector's performance and the neural network needs a small number of batches to be trained. Simulation results have shown that the proposed DL-SPA achieves a performance gain up to 2.5 dB with the same bit error rate compared to the conventional sum-product detection algorithm under the same ISI responses.

In this paper, we investigate the performance of a class of spatially coupled codes, namely partially information coupled turbo codes (PIC-TCs) over the binary erasure channel (BEC). This class of codes enjoy flexible code rate adjustment by varying the coupling ratio. Moreover, the coupling method can be directly applied to any component codes without changing the encoding and decoding architectures of the underlying component codes. However, the theoretical performance of PIC-TCs has not been fully investigated. For this work, we consider the codes that have coupling memory m and study the corresponding graph model. We then derive the exact density evolution equations for these code ensembles with any given coupling ratio and coupling memory m to precisely compute their belief propagation decoding thresholds for the BEC. Our simulation results verify the correctness of our theoretical analysis and also show better error performance over uncoupled turbo codes with a variety of code rates on the BEC.

We investigate the spatial coupling technique for coded modulation schemes. In particular, we design partially information coupled bit-interleaved polar coded modulation (PIC-BIPCM) schemes for Gray-labelled 16-ary quadrature-amplitude modulation (16-QAM) over AWGN channels. In order to improve the error performance of the BIPCM, we propose a method to appropriately choose the coupled information bits between consecutive polar code blocks (CBs). We also derive a closed form expression for the CB error rate of the designed PIC-BIPCM. Simulation results show that the proposed PIC-BIPCM can achieve a considerable gain over the uncoupled counterparts for variable code rates.

In ultra-dense cellular networks (UDNs) with user/control plane (U/C) splitting, frequent handovers in user planes are unavoidable. This seriously degrades MS's transmission control protocol (TCP) throughput. This paper proposes a prediction-based user plane handover scheme to improve the TCP throughput in UDNs. Firstly, based on algorithms used in recommender systems, a mobility prediction algorithm called content-based collaborative hybrid filters (CCHF) is proposed to predict the target small base station (SBS). When the mobile station (MS) moves into the cell-edge of the source SBS, it can set up connections to the predicted target SBS and the source SBS simultaneously. An accurate prediction and a simultaneous connection can enhance the signal to interference and noise ratio (SINR) at cell-edge and reduce the handover interruption ratio (HIR). Thus packet loss can be reduced and the MS's TCP throughput will be improved. Simulations are carried out to verify the effectiveness of the proposed CCHF-handover. It is shown that using CCHF, the prediction accuracy of random trajectory can be improved by more than 100% compared with existing prediction algorithm. Moreover, the CCHF-handover improves the average TCP throughput significantly by more than 3 times compared with that of existing handover schemes.

In this paper, we study the resource allocation and trajectory design for energy-efficient secure unmanned aerial vehicle (UAV) communication systems where a UAV base station serves multiple legitimate ground users in the existence of a potential eavesdropper. We aim to maximize the energy efficiency of the UAV by jointly optimizing its transmit power, user scheduling, trajectory, and velocity. The design is formulated as a non-convex optimization problem taking into account the maximum tolerable signal-to-noise ratio (SNR) leakage, the minimum data rate requirement of each user, and the location uncertainty of the eavesdropper. An iterative algorithm is proposed to obtain an efficient suboptimal solution. Simulation results demonstrate that the proposed algorithm can achieve a significant improvement of the system energy efficiency while satisfying communication security constraint, compared to some simple scheme adopting straight flight trajectory with a constant speed.

In cache-enabled heterogeneous cloud radio access networks (HC-RANs), mobile station (MS) association for multimedia content delivery should consider both the content caching location and the wireless channel quality. This paper studies economically optimal MS association to tradeoff the cache-hit ratio and the ratio of MSs with satisfied quality of service (QoS). When the associated enhanced remote radio unit (eRRU) stores the requesting content, the content can be fetched directly from the local cache. Otherwise, fronthaul has to be used to fetch the content. The use of fronthaul resource and cache is treated as costs, and payments of QoS-satisfied MSs are treated as incomes. Thus, the economic MS association is formulated as an optimization problem to maximize the system utility, i.e., total profit of the network operator, which is defined as the difference between incomes and costs. A belief propagation-based method is employed to solve the problem on a developed factor graph. Simulation results show that the proposed economically optimal MS association achieves much higher profit than the existing schemes and works well in the network with various loads. Moreover, the profit of the proposed scheme can be improved with inter-cell interference coordination. For the case with extremely skewed content popularity, the proposed scheme can avoid MS overloading at eRRUs storing most popular multimedia contents. Furthermore, it can support more MSs with satisfied QoS, which leads to a higher profit.

In this paper, the problem of downlink non-orthogonal multiple access (NOMA) over slow fading channels is studied. Full-channel state information (CSI) is assumed at the receivers, while only the statistical CSI is assumed to be available at the transmitter. A novel lattice-partition-based scheme is proposed which, according to statistical CSI, employs discrete inputs from appropriately designed constellations carved from a lattice, rather than continuous Gaussian inputs as used in most existing works. Theoretical analysis shows that for any outage probability smaller than 63.21%, which covers almost all the cases of practical interest, the proposed scheme with single-user decoding, i.e., without successive interference cancellation (SIC) is able to approach the NOMA outage capacity region within a constant gap, independent of the signal-to-noise ratio, and the number of users. Simulation results fortify the effectiveness of the proposed scheme by showing that the approach without SIC can achieve outage rates that are very close to the outage capacity region and the gap becomes even smaller when SIC is employed.

In this paper, we propose a transmission control scheme for the approximate message passing (AMP)-based joint user identification and channel estimation in massive connectivity networks. In the proposed transmission control scheme, a transmission control function is designed to determine a user's transmission probability, when it has a transmission demand. By employing a step transmission control function for the proposed scheme, we derive the channel distribution experienced by the receiver to describe the effect of transmission control on the design of AMP algorithm. Based on that, we modify the AMP algorithm by designing a minimum mean squared error (MMSE) denoiser, to jointly identify the user activity and estimate their channels. We further derive the false alarm and missed detection probabilities to characterize the user identification performance of the proposed scheme. Closed-form expressions of the average packet delay and the network throughput are obtained. Furthermore, we optimize the transmission control function to maximize the network throughput. We demonstrate that the proposed scheme can significantly improve the user identification and channel estimation performance, reduce the average delay, and boost the throughput, compared to the conventional scheme without transmission control.

In this paper, we propose a multi-beam non-orthogonal multiple access (NOMA) scheme for hybrid millimeter wave (mmWave) systems and study its resource allocation. A beam splitting technique is designed to generate multiple analog beams to serve multiple NOMA users on each radio frequency chain. In contrast to the recently proposed single-beam mmWave-NOMA scheme which can only serve multiple NOMA users within the same analog beam, the proposed scheme can perform NOMA transmission for the users with an arbitrary angle-of-departure distribution. This provides a higher flexibility for applying NOMA in mmWave communications and thus can efficiently exploit the potential multi-user diversity. Then, we design a suboptimal two-stage resource allocation for maximizing the system sum-rate. In the first stage, assuming that only analog beamforming is available, a user grouping and antenna allocation algorithm is proposed to maximize the conditional system sum-rate based on the coalition formation game theory. In the second stage, with the zero-forcing digital precoder, a suboptimal solution is devised to solve a non-convex power allocation optimization problem for the maximization of the system sum-rate which takes into account the quality of service constraints. Simulation results show that our designed resource allocation can achieve a close-to-optimal performance in each stage. In addition, we demonstrate that the proposed multi-beam mmWave-NOMA scheme offers a substantial spectral efficiency improvement compared to that of the single-beam mmWave-NOMA and the mmWave orthogonal multiple access schemes.

In this paper, we propose a novel beamforming design to enhance physical layer security of a non-orthogonal multiple access (NOMA) system with the aid of artificial noise (AN). The proposed design uses two factors to balance the useful signal strength and interference at the strong and weak users, which is a generalized version of the existing beamforming designs in the context of physical layer security for NOMA. We determine the optimal power allocation among useful signals and AN together with the two optimal factors in order to maximize the secrecy sum rate (SSR). Our asymptotic analysis in the high signal-to-noise ratio regime provides an efficient and near-optimal solution to optimize the beamforming scalars and power allocation coefficients. Our analysis indicates that it is not optimal to form a beam toward either the strong user or the weak user in NOMA systems for security enhancement. In addition, the asymptotically optimal power allocation informs that, as the transmit power increases, more power should be allocated to the weak user or AN signals, while the power allocated to the strong user keeps constant. Our examination shows that the proposed novel beamforming design can significantly outperform two benchmark schemes.

We consider a heterogeneous ultra dense network (HUDN) with both the hexagon and Poisson point process (PPP) layouts, which is more relevant for practical scenarios. A user-centric and adaptive interference-aware non-coherent coordinated multi-point transmission (IA-CoMP) scheme is used as a system setup to reduce both the cross-tier and the co-tier inter-cell interference (ICI) for the HUDN with range expansion (RE) in small cells. Due to the involvement of hexagonal macrocells, it is intractable to analyze the coverage performance of HUDNs with IA-CoMP. To this end, we present a mobile station GrouPing (MSGP)-based coverage analysis method, which partitions all MSs into four groups according to their main interference, and the whole coverage is obtained as the sum of the coverage of each MS group. We demonstrate that the proposed MSGP-based coverage analysis method can provide a tight upper bound when compared with Monte Carlo simulations. As the small cell density increases, the system coverage of HUDNs with hexagonal macrocells reduces exponentially, while the system coverage of HUDNs with PPP-based macrocells remains unchanged. Moreover, the system coverage increases with the larger one of the main ICI judging coefficient and the RE bias.

In this paper, we investigate the problem of downlink non-orthogonal multiple access (NOMA) over block fading channels. For the single antenna case, we propose a class of NOMA schemes where all the users' signals are mapped into n-dimensional constellations corresponding to the same algebraic lattices from a number field, allowing every user attains full diversity gain with single-user decoding, i.e., no successive interference cancellation (SIC). The minimum product distances of the proposed scheme with arbitrary power allocation factor are analyzed and their upper bounds are derived. Within the proposed class of schemes, we also identify a special family of NOMA schemes based on lattice partitions of the underlying ideal lattices, whose minimum product distances can be easily controlled. Our analysis shows that among the proposed schemes, the lattice-partition-based schemes achieve the largest minimum product distances of the superimposed constellations, which are closely related to the symbol error rates for receivers with single-user decoding. The simulation results are presented to verify our analysis and to show the effectiveness of the proposed schemes as compared to benchmark NOMA schemes. Extensions of our design to the multi-antenna case are also considered where similar analysis and results are presented.

This paper studies multi-antenna-aided covert communications coexisting with randomly located wardens and interferers, considering both centralized and distributed antenna systems (CAS/DAS). The throughput performance of the covert communication is analyzed and optimized under a stochastic geometry framework, where the joint impact of the small-scale channel fading and the large-scale path loss is examined. To be specific, two probabilistic metrics, namely, the covert outage probability and the connectivity probability, are adopted to characterize the covertness and reliability of the transmission, respectively, and analytically tractable expressions for the two metrics are derived. The worst-case covert communication scenario is then investigated, where the wardens invariably can maximize the covert outage probability by adjusting the detection thresholds for their detectors. Afterward, the optimal transmit power and transmission rate are jointly designed to maximize the covert throughput subject to a covertness constraint. Interestingly, it is found that the maximal covert throughput for both the CAS and DAS is invariant to the density of interferers and the interfering power, regardless of the number of transmit antennas. The numerical results demonstrate that the CAS outperforms the DAS in terms of the covert throughput for the random network of interest, and the throughput gap between the two systems increases dramatically when the number of transmit antennas becomes higher.

The performance of cloud radio access networks (Cloud-RAN) is constrained by the limited fronthaul capacity. Physical layer caching is an effective technique to reduce the requirement on fronthaul capacity in peak time, which depends on the cache-hit ratio and the wireless transmission performance. Considering a dynamic points selection (DPS) based coordinated multi-point (CoMP) scheme to enhance the wireless transmission performance, this paper proposes an optimal probabilistic caching scheme to minimize the fronthaul capacity requirement in cloud-RAN. First of all, given the content caching probabilities, the average outage probability of cache-based services is derived. Based on the derivation, the relationship between the wireless transmission performance and the fronthaul capacity requirement is established. Then, an optimization problem is formulated to find an optimal probabilistic caching scheme to minimize the fronthaul capacity requirement, which can be solved using the interior point method. Simulation results show that using CoMP transmissions and optimizing the caching probabilities, the proposed scheme can reduce the fronthaul capacity requirement efficiently. Compared to the existing scheme which does not consider the effect of the wireless transmission performance on the fronthaul capacity requirement, the fronthaul capacity requirement can be reduced by 36\%. Moreover, the proposed scheme can also improve the cache service probability.

In this paper, we develop a lattice-partition-based downlink non-orthogonal multiple access (NOMA) scheme for slow fading channels without successive interference cancellation (SIC) at the receivers. With the knowledge of statistical channel state information at the transmitter, our scheme uses a finite constellation drawn from an n-dimensional lattice and employs channel coding on top of it. The outage rates achieved by our scheme without SIC are analyzed and their gaps to the multiuser outage capacity are derived. We show, both theoretically and numerically, that our scheme without SIC is capable of approaching any point in the multiuser outage capacity region within a constant gap when the required outage probability is smaller than 63.21%, which covers almost all cases of practical interest. Simulation results based on various lattices are provided and demonstrate that the near-capacity performance can be attained by our NOMA scheme without SIC.

In this paper, we investigate the performance gain of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink single-cell systems. In both single-antenna and multi-antenna scenarios, the performance gain of NOMA over OMA in terms of asymptotic ergodic sumrate is analyzed for a sufficiently large number of users. In particular, in single-antenna systems, we identify two types of near-far gains brought by NOMA: 1) the large-scale near-far gain via exploiting the large-scale fading increases with the cell size; 2) the small-scale near-far gain via exploiting the small-scale fading is a constant given by γ = 0.57721 nat/s/Hz in Rayleigh fading channels. Furthermore, we have analyzed that the performance gain achieved by single-antenna NOMA can be amplified via increasing the number of antennas equipped at the base station due to the extra spatial degrees of freedom. The numerical results confirm the accuracy of the derived analyses and unveil the performance gains of NOMA over OMA in different scenarios.scenarios.

This paper explores physical-layer security in a small cell network with cooperative cache-enabled small base stations (SBSs) in the presence of randomly distributed eavesdroppers. We put forward a hybrid caching placement strategy where a proportion of the cache space in each SBS is assigned to store the most popular files (MPFs), while the remaining is used to cache the disjoint subfiles (DSFs) of less popular files in different SBSs as a means to improve secrecy and content diversity. We then propose two coordinated multi-point techniques, namely, joint transmission and orthogonal transmission, to deliver the MPFs and DSFs, respectively. We jointly design the optimal transmission rate and caching assignment proportion to maximize the secure content delivery probability, and provide various insights into the optimal results. Numerical results are also presented to verify the theoretical findings and to demonstrate the superiority of our caching and transmission strategies.

We propose a new design of success prioritized distributed coordination function (SP-DCF) based on the backoff mechanism of IEEE 802.11 DCF to prioritize the station (STA) after successful transmission, which is called success STA. A design parameter of contention-free threshold offers priority to the success STA. The proposed SP-DCF is a fully distributed control scheme and achieves a higher throughput and a lower frame discard rate as compared with the conventional DCF especially when STAs are densely deployed because the success STA has an opportunity to transmit the next frame without contention. We also develop a performance analysis model which enables to estimate the throughput, frame discard rate, and the number of failures per success under saturated traffic. The simulation results reveal the validity of the developed performance analysis model for the SP-DCF with contention-free threshold and the characteristics in the coexistence of the conventional DCF and the proposed SP-DCF.

In this paper, we propose a multi-beam non- orthogonal multiple access (NOMA) framework for hybrid millimeter wave (mmWave) systems. The proposed framework enables the use of a limited number of radio frequency (RF) chains in hybrid mmWave systems to accommodate multiple users with various angles of departures (AODs). A beam splitting technique is introduced to generate multiple analog beams to facilitate NOMA transmission. We analyze the performance of a system when there are sufficient numbers of antennas driven by a single RF chain at each transceiver. Furthermore, we derive the sufficient and necessary conditions of antenna allocation, which guarantees that the proposed multi-beam NOMA scheme outperforms the conventional time division multiple access (TDMA) scheme in terms of system sum-rate. The numerical results confirm the accuracy of the developed analysis and unveil the performance gain achieved by the proposed multi- beam NOMA scheme over the single-beam NOMA scheme.

In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave (mmWave) multiple-input multiple- output (MIMO) network adopting the channel estimation algorithm proposed in [1] for channel estimation. Due to the reuse of orthogonal pilot symbols among different cells, the channel estimation is expected to be affected by pilot contamination, which is considered as a fundamental performance bottleneck of conventional multicell MU massive MIMO networks. To analyze the impact of pilot contamination on the system performance, we derive the closed-form approximation expression of the normalized mean squared error (MSE) of the channel estimation performance. Our analytical and simulation results show that the channel estimation error incurred by the impact of pilot contamination and noise vanishes asymptotically with an increasing number of antennas equipped at each radio frequency (RF) chain deployed at the desired BS. Thus, pilot contamination is no longer the fundamental problem for multi-cell hybrid mmWave systems.

We propose a new class of spatially coupled polar codes, namely information coupled (IC) polar codes, to improve the error performance of finite length polar codes. In the proposed IC-polar codes, every two consecutive polar code blocks (CBs) in a frame are coupled by sharing a few information bits. We optimize the indices of coupling information so that the less reliable information bits in each CB can obtain more reliable messages from the consecutive CBs during decoding. A decoding scheme is proposed for the IC-polar codes. Simulation results show that the proposed IC-polar codes achieve a considerable gain over the uncoupled counterparts for variable code rates with a slightly increased decoding complexity.

This paper proposes a new iterative soft-decision decoding algorithm which combines list decoding and adaptive belief propagation (ABP) algorithm for short Reed-Solomon (RS) codes. The proposed algorithm generates a list of codewords by restarting the decoder with log-likelihood ratio saturations to the dynamically selected suspicious bits based on an up-to-date best decoded codeword. The suspicious bits are selected according to a joint evaluation of the decoded codeword and the initial channel information. The damping coefficient used in the ABP decoder is set to be proportional to the channel noise variance to achieve a proper convergence speed for the decoder at different SNRs. The performance of the proposed algorithm for short RS codes is investigated. It shows that the proposed algorithm brings a considerable coding gain for short RS codes over additive white Gaussian noise channels.

We propose a new class of information-coupled Turbo codes to improve the transport block (TB) error rate performance for LTE systems. Meanwhile, we keep the LTE hybrid automatic repeat request protocol and the Turbo decoder for each code block (CB) unchanged. In the proposed codes, every two consecutive CBs in a TB are coupled together by sharing a few common information bits. We propose a feed-forward and feed-back decoding scheme to decode the whole TB by exploiting the coupled information between CBs. Numerical results show that the proposed codes achieve a signal-to-noise-ratio (SNR) gain of 0.28 dB to 0.72 dB over LTE Turbo codes for the simulated code parameters at a TB error rate level of 10-2.

The integration of local device-to-device (D2D) communications and cellular connections has been intensively studied to satisfy co-existing D2D and cellular communication demand. In future cellular networks, there will be numerous standby users possessing D2D communication capabilities in close proximity to each other. Considering that these standby users do not necessarily request D2D communications all the time, in this paper we propose a hybrid D2D-cellular scheme to make use of these standby users and to improve the rate performance for cellular users. More specifically, through D2D links, a virtual antenna array can be formed by sharing antennas across different terminals to realize the diversity gain of MIMO channels. This paper considers the use of millimeter wave links to enable high data rate D2D communications. We then design an orthogonal D2D multiple access protocol and formulate the optimization problem of joint cellular and D2D resource allocation for downlink transmissions using the proposed scheme. We obtain a closed-form solution for D2D resource allocation, which reveals useful insights for practical system design. Numerical results from extensive system-level simulations demonstrate that the rate performance of cellular users is significantly improved.

In this paper, a novel lattice-partition-based downlink non-orthogonal multiple access framework is proposed. This framework is motivated by recognizing the algebraic structure behind the previous scheme recently proposed by Shieh and Huang as a lattice partition in Z and is in fact a generalization of the scheme to any base lattice. The schemes in the proposed framework enjoy many desirable properties such as explicit and systematic design and discrete input distributions. Moreover, the proposed method only requires a limited knowledge of channel parameters. The rates achieved by the proposed scheme with any base lattice and with single-user decoding (i.e., without successive interference cancellation) are analyzed, and a universal upper bound on the gap to the multiuser capacity is obtained as a function of the normalized second moment of the base lattice. Since the proposed framework has a substantially larger design space than that of the previous scheme of Shieh and Huang whose base lattice is a 1-D lattice, one can easily find instances in larger dimensions that can provide superior performance. Design examples with the base lattices A2, D4, E8, and Construction A lattices, respectively, are provided, and both theoretical and simulation results exhibit smaller gaps to the multiuser capacity as dimensions increase.

Most multi-dimensional (more than two dimensions) lattice partitions only form additive quotient groups and lack multiplication operations. This prevents us from constructing lattice codes based on multi-dimensional lattice partitions directly from non-binary linear codes over finite fields. In this paper, we design lattice codes from Construction A lattices where the underlying linear codes are non-binary irregular repeat-accumulate (IRA) codes. Most importantly, our codes are based on multi-dimensional lattice partitions with finite constellations. We propose a novel encoding structure that adds randomly generated lattice sequences to the encoder's messages, instead of multiplying lattice sequences to the encoder's messages. We prove that our approach can ensure that the decoder's messages exhibit permutation-invariance and symmetry properties. With these two properties, the densities of the messages in the iterative decoder can be modeled by Gaussian distributions described by a single parameter. With Gaussian approximation, extrinsic information transfer charts for our multi-dimensional IRA lattice codes are developed and used for analyzing the convergence behavior and optimizing the decoding thresholds. Simulation results show that our codes can approach the unrestricted Shannon limit within 0.46 dB and outperform the previously designed lattice codes with 2-D lattice partitions and existing lattice coding schemes for large codeword length.

In this paper, we propose novel terminated staircase codes for NAND flash memories. Specifically, we design a rate 0.89 staircase code whose component code is a Bose-Chaudhuri-Hocquenghem (BCH) code, for flash memories with page size of 16K bytes. Different from most conventional unterminated staircase codes, we propose a novel coding structure by performing cyclic redundancy check (CRC) encoding and decoding on each component codeword including information bits and parity bits. The CRC bits are protected by both row and column codewords. Furthermore, a novel iterative bit flipping algorithm is developed to solve stall patterns and lower the error floor. Based on our design, we perform an improved analysis on the error floor. We prove and show that our proposed decoding algorithm can solve more stall patterns which leads to a lower error floor compared with conventional staircase codes. Numerical results show that our terminated staircase codes outperform the stand-alone BCH codes and the conventional staircase codes.

We design classes of quantum low-density paritycheck (LDPC) codes, called quasi-cyclic stabilizer (QCS) codes, from conventional QC-LDPC codes. The proposed QCS codes belong to the family of non-Calderbank-Shor-Steane stabilizer codes. The QC-LDPC codes are self-orthogonal with respect to the symplectic inner product (SIP) and are constructed from submatrices of nonorthogonal Latin squares via array dispersion. The Latin squares are constructed using quadratic (non)-residue sets of prime modulus p, where p = 4n ± 1. For p = 4n - 1, two constructions, namely, Type-I-A and Type-I-B QCS codes, are proposed based on matrix superposition and matrix concatenation, respectively. For p = 4n +1, Type-II QCS codes are proposed based on permutations of a base matrix. We show that the parity-check matrix for Type-I-B and Type-II QCS codes is self-orthogonal with respect to the SIP for all orders of circulant permutation matrix. This resulting in ensembles of QCS codes characterized by a single base matrix. We show that the minimum distance of Type-II QCS codes can be lower bounded by the minimum distance of the QC-LDPC codes. Simulation results show that the proposed QCS codes outperform some codes in the literature with a noteworthy low-error floor, below 10-7, over quantum depolarizing channels.

We propose a new class of partially information-coupled (PIC) turbo codes to improve the transport block (TB) error rate performance for long-term evolution (LTE) systems, while keeping the hybrid automatic repeat request protocol and the turbo decoder for each code block (CB) unchanged. In the proposed codes, every two consecutive CBs in a TB are coupled together by sharing partial information bits. This coding structure introduces irregular variable node degree distributions in the Tanner graph of the PIC-turbo codes. We propose a feedforward and feedback decoding scheme and a windowed decoding scheme to decode the whole TB by exploiting the coupled information between CBs. We calculate the extrinsic information transfer (EXIT) functions for the PIC-turbo codes by assuming that the coupled information are perfectly decoded. An SNR gain upper bound of the PIC-turbo codes over the LTE turbo codes for various coupling ratios is derived by the calculated EXIT charts. Numerical results show that the proposed codes achieve an SNR gain of 0.26-0.73 dB for various code parameters at a TB error rate level of 10-2, which complies with the derived SNR gain upper bound.

In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave communications in a multiple-input multiple-output (MIMO) network. Due to the reuse of pilot symbols among different cells, the performance of channel estimation is expected to be degraded by pilot contamination, which is considered as a fundamental performance bottleneck of conventional multi-cell MU massive MIMO networks. To analyze the impact of pilot contamination to the system performance, we first derive the closed-form approximation of the normalized mean-squared error of the channel estimation algorithm proposed by Zhao et al. over Rician fading channels. Our analytical and simulation results show that the channel estimation error incurred by the impact of pilot contamination and noise vanishes asymptotically with an increasing number of antennas equipped at each radio frequency chain at the desired BS. Furthermore, by adopting zero-forcing precoding in each cell for downlink transmission, we derive a tight closed-form approximation of the average achievable rate per user. Our results unveil that the intra-cell interference and inter-cell interference caused by pilot contamination over Rician fading channels can be mitigated effectively by simply increasing the number of antennas equipped at the desired BS.

This paper studies physical-layer security for a cache-enabled heterogeneous cellular network comprised of a macro base station and multiple small base stations (SBSs). We investigate a joint design on caching placement and file delivery for realizing secure and energy-efficient transmissions against randomly distributed eavesdroppers. We propose a novel hybrid “most popular content” and “largest content diversity” caching placement policy to distribute the files of different popularities. Depending on the availability and placement of the requested file, we employ three cooperative transmission schemes, namely, distributed beamforming, frequency-domain orthogonal transmission, and best SBS relaying. We derive analytical expressions for the connection outage probability and secrecy outage probability for each transmission scheme. Afterward, we design the optimal transmission rates and caching allocation successively to achieve a maximal overall secrecy throughput and secrecy energy efficiency, respectively. Numerical results verify the theoretical analyses and demonstrate the superiority of the proposed hybrid caching policy.

In this paper, we propose a new caching scheme for a random wireless device-to-device (D2D) network of$n$nodes with local caches, where each node intends to download files from a prefixed library via D2D links. Our proposed caching delivery includes two stages, employing distributed MIMO and hierarchical cooperations, respectively. The distributed MIMO is applied to the first stage between source nodes and neighbors of the destination node. The induced multiplexing gain and diversity gain increase the number of simultaneous transmissions, improving the throughput of the network. The hierarchical cooperations are applied to the second stage to facilitate the transmissions between the destination node and its neighbors. The two stages together exploit spatial degrees of freedom as well as spatial reuse. We develop an uncoded random caching placement strategy to serve this cooperative caching delivery. Analytical results show that the average aggregate throughput of the network scales almost linearly with$n$, with a vanishing outage probability. Furthermore, we derive an explicit expression of the optimal throughput as a function of system parameters, such as pathloss factor under a target outage probability. Analytical and numerical results demonstrate that our proposed scheme outperforms existing ones when the local cache size is limited.

This paper explores the physical-layer security in a small cell network with cooperative cache-enabled small base stations (SBSs) in the presence of randomly distributed eavesdroppers. We propose a joint design on the caching placement and the physical-layer transmission to improve the secure content delivery probability (SCDP). We first put forward a hybrid caching placement strategy in which a proportion of the cache unit in each SBS is assigned to store the most popular files (MPFs), while the remaining is used to cache the disjoint subfiles (DSFs) of the less popular files in different SBSs as a means to enhance transmission secrecy and content diversity. We then introduce two coordinated multi-point techniques, namely, joint transmission and orthogonal transmission, to deliver the MPFs and DSFs, respectively. We derive analytical expressions for the SCDP in each transmission scheme, considering both non-colluding and colluding eavesdropping scenarios. Based on the obtained analytical results, we jointly design the optimal transmission rates and the optimal caching assignment for maximizing the overall SCDP. Various insights into the optimal transmission and caching designs are further provided. Numerical results are also presented to verify our theoretical findings and to demonstrate the superiority of the proposed caching and transmission strategies.

Error control codes are widely applied in modern communication systems to improve the bandwidth-power efficiency and the reliability of data transmissions.Modern error control codes have attracted the interest of scholars and industry partners since Turbo codes were invented.For example, Turbo codes have been used in the 4G cellular mobile systems.Nowadays, LDPC codes and the polar codes are adopted in the 5G standard.The recent development on the theoretic framework of new channel coding theorem for finite code length will provide guidelines for future practical error control codes designs.In the age of IoT, everything will be connected via communication links.It is expected that the next-generation communication systems need to support many scenarios such as wireless communications, optical communications, distributed storage systems, V2X networks, and sensor networks.These scenarios will impose new requirements to the communication systems ranging from lower complexity encoder/decoder, lower delay or latencies, ultrareliable transmission at rates close to the Shannon capacity, low energy consumptions, etc.In addition to the communication systems, error control codes also find emerging applications in security, flash memories, and deep-space probing.This special issue is a collection of 11 papers which explore the performance of error control codes for the next generation communication systems and discuss the opportunities and challenges that they will face.

We consider low-density parity-check (LDPC) code design by considering the unequal error protection property in high order modulated bit-interleaved coded modulation (BICM) schemes. The existing work mainly considered the effect of variable node edge assignments on the decoding performance. In this paper, we consider both variable node and check node edge assignments to further optimize the LDPC codes for BICM schemes. To achieve this, we derive new extrinsic information transfer (EXIT) functions for both regular and irregular LDPC code ensembles. Then we employ differential evolution to optimize the code ensembles in terms of the lowest decoding threshold. Finally, we propose a modified progressive edge growth algorithm to design regular and irregular LDPC codes based on the optimized code ensembles. The numerical results show that our designed LDPC codes have better bit error rate performance compared to the codes designed in the existing work.

In this paper, we propose a new caching scheme for a random wireless device-to-device (D2D) network of n nodes with local caches, where each node intends to download files from a prefixed library via D2D links. Our proposed caching delivery includes two stages, employing distributed MIMO and hierarchical cooperations respectively. The distributed MIMO is applied to the first stage between source nodes and neighbours of the destination node. The induced multiplexing gain and diversity gain increase the number of simultaneous transmissions, improving the throughput of the network. The hierarchical cooperations are applied to the second stage to facilitate the transmissions between the destination node and its neighbours. The two stages together exploit spatial degrees of freedom as well as spatial reuse. We develop an uncoded random caching placement strategy to serve this cooperative caching delivery. Analytical results show that the average aggregate throughput of the network scales almost linearly with n, with a vanishing outage probability.

In this paper, downlink non-orthogonal multiple access (NOMA) with receivers performing single- user decoding i.e., without successive interference cancellation (SIC) is studied. Using lattice partitions, we generalize the scheme recently proposed by Shieh and Huang [1] to general n-dimensional constellations carved from lattices. The achievable rates of the proposed scheme without SIC and the gap to the capacity region are investigated. Design examples based on lattice partition chains in Z2, A2, and D4 are studied. Numerical and simulation results are provided, which demonstrate advantages of the proposed scheme over the one in [1] and any orthogonal multiple access scheme.

In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed channel estimation algorithm aims to estimate the strongest angle-of-arrivals (AoAs) at both the base station (BS) and the users. Then all the users transmit orthogonal pilot symbols to the BS via these estimated strongest AoAs to facilitate the channel estimation. The algorithm does not require any explicit channel state information (CSI) feedback from the users and the associated signalling overhead of the algorithm is only proportional to the number of users, which is significantly less compared to various existing schemes. Besides, the proposed algorithm is applicable to both non-sparse and sparse mmWave channel environments. Based on the estimated CSI, zero-forcing (ZF) precoding is adopted for multiuser downlink transmission. In addition, we derive a tight achievable rate upper bound of the system. Our analytical and simulation results show that the proposed scheme offer a considerable achievable rate gain compared to fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Furthermore, the achievable rate performance gap between the considered hybrid mmWave systems and the fully digital system is characterized, which provides useful system design insights.

We propose and design the lattice codes with finite lattice constellations based on multi-dimensional (more than two dimensions) lattice partitions. The codes are constructed from non-binary irregular repeat-accumulate (IRA) codes. Most notably, we propose a novel encoding structure to ensure that the decoder's messages exhibit permutation-invariance and symmetry properties. With these two properties, the densities of the messages in our iterative decoder can be well modeled by Gaussian distributions described by a single parameter. Under the Gaussian approximation, extrinsic information transfer charts for our multi-dimensional IRA lattice codes are developed and used for analysing the convergence behaviour and optimising the decoding threshold. Simulation results show that our proposed lattice codes outperform the previously designed lattice codes with two-dimensional lattice partitions.

This paper proposes a novel hybrid downlinkuplink cooperative NOMA (HDU-CNOMA) scheme to achieve a better tradeoff between spectral efficiency and signal reception reliability than the conventional cooperative NOMA schemes. In particular, the proposed scheme enables the strong user to perform a cooperative transmission and an interference-free uplink transmission simultaneously during the cooperative phase, at the expense of a slightly decrease in signal reception reliability at the weak user. We analyze the outage probability, diversity order, and outage throughput of the proposed scheme. Simulation results not only confirm the accuracy of the developed analytical results, but also unveil the spectral efficiency gains achieved by the proposed scheme over a baseline cooperative NOMA scheme and a non-cooperative NOMA scheme.

In this paper, we compare the resource allocation fairness of uplink communications between non-orthogonal multiple access (NOMA) schemes and orthogonal multiple access (OMA) schemes. Through characterizing the contribution of the individual user data rate to the system sum rate, we analyze the fundamental reasons that NOMA offers a more fair resource allocation than that of OMA in asymmetric channels. Furthermore, a fairness indicator metric based on Jain's index is proposed to measure the asymmetry of multiuser channels. More importantly, the proposed metric provides a selection criterion for choosing between NOMA and OMA for fair resource allocation. Based on this discussion, we propose a hybrid NOMA-OMA scheme to further enhance the users fairness. Simulation results confirm the accuracy of the proposed metric and demonstrate the fairness enhancement of the proposed hybrid NOMA-OMA scheme compared to the conventional OMA and NOMA schemes.

Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth-generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the context of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.

This paper focuses on wireless powered 5G dense cellular networks, where base station (BS) delivers energy to user equipment (UE) via the microwave radiation in sub-6 GHz or millimeter wave (mmWave) frequency, and UE uses the harvested energy for uplink information transmission. By addressing the impacts of employing different numbers of antennas and bandwidths at lower and higher frequencies, we evaluate the amount of harvested energy and throughput in such networks. Based on the derived results, we obtain the required small cell density to achieve an expected level of harvested energy or throughput. Also, we obtain that when the ratio of the number of sub-6-GHz BSs to that of the mmWave BSs is lower than a given threshold, UE harvests more energy from an mmWave BS than a sub-6-GHz BS. We find how many mmWave small cells are needed to perform better than the sub-6-GHz small cells from the perspectives of harvested energy and throughput. Our results reveal that the amount of harvested energy from the mmWave tier can be comparable to the sub-6-GHz counterpart in the dense scenarios. For the same tier scale, mmWave tier can achieve higher throughput. Furthermore, the throughput gap between different mmWave frequencies increases with the mmWave BS density.

In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed mmWave channel estimation algorithm first exploits multiple frequency tones to estimate the strongest angle-of-arrivals (AoAs) at both base station (BS) and user sides for the design of analog beamforming matrices. Then, all the users transmit orthogonal pilot symbols to the BS along the directions of the estimated strongest AoAs in order to estimate the channel. The estimated channel will be adopted to design the digital zero-forcing (ZF) precoder at the BS for the multi-user downlink transmission. The proposed channel estimation algorithm is applicable to both the non-sparse and sparse mmWave channel environments. Furthermore, we derive a tight achievable rate upper bound of the digital ZF precoding with the proposed channel estimation algorithm scheme. Our analytical and simulation results show that the proposed scheme obtains a considerable achievable rate of fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Besides, considering the effect of various types of errors, i.e., random phase errors, transceiver analog beamforming errors, and equivalent channel estimation errors, we derive a closed-form approximation for the achievable rate of the considered scheme. We illustrate the robustness of the proposed channel estimation and multi-user downlink precoding scheme against the system imperfection.

In this paper, we investigate the design and analysis of coded slotted ALOHA (CSA) schemes in the presence of channel erasure. We design the code probability distributions for CSA schemes with repetition codes and maximum distance separable codes to maximize the expected traffic load, under both packet erasure channels and slot erasure channels. We derive the extrinsic information transfer (EXIT) functions of CSA schemes over erasure channels. By optimizing the convergence behavior of the derived EXIT functions, the code probability distributions to achieve the maximum expected traffic load are obtained. Then, we derive the asymptotic throughput of CSA schemes over erasure channels. In addition, we validate that the asymptotic throughput can give a good approximation to the throughput of CSA schemes over erasure channels.

In this paper, we study power-efficient resource allocation for multicarrier non-orthogonal multiple access systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which jointly designs the power allocation, rate allocation, user scheduling, and successive interference cancellation (SIC) decoding policy for minimizing the total transmit power. The proposed framework takes into account the imperfection of channel state information at transmitter and quality of service requirements of users. To facilitate the design of optimal SIC decoding policy on each subcarrier, we define a channel-to-noise ratio outage threshold. Subsequently, the considered non-convex optimization problem is recast as a generalized linear multiplicative programming problem, for which a globally optimal solution is obtained via employing the branch-and-bound approach. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. To strike a balance between system performance and computational complexity, we propose a suboptimal iterative resource allocation algorithm based on difference of convex programming. Simulation results demonstrate that the suboptimal scheme achieves a close-to-optimal performance. Also, both proposed schemes provide significant transmit power savings than that of conventional orthogonal multiple access schemes.

In this paper, we investigate channel estimation and multiuser downlink transmission of a time division duplex massive multiple-input multiple-output (MIMO) system in millimeter wave (mmWave) channels. We propose a tone-based linear search algorithm to facilitate the estimation of angle-of-arrivals (AoAs) of the strongest line-of-sight (SLOS) channel component as well as the scattering components of the users at the base station. Based on the estimated AoAs, we reconstruct the SLOS component and scattering components of the users for downlink transmission. We then derive the achievable rates of maximum-ratio transmission (MRT) and zero-forcing (ZF) precoding based on the SLOS component and the SLOS-plus-scattering components (SLPS), respectively. Taking into account the impact of pilot contamination, our analysis and simulation results show that the SLOS-based MRT can achieve higher data rate than that of the traditional pilot-aided-CSI-based (PAC-based) MRT, under the same mean square errors of channel estimation. As for ZF precoding, the achievable rates of the SLPS-based and the PAC-based are identical. Furthermore, we quantify the achievable rate degradation of the SLOS-based MRT precoding caused by phase quantization errors in the large number of antennas regime. We show that the impact of phase quantization errors on the considered systems cannot be mitigated by increasing the number of antennas and therefore the resolutions of radio frequency phase shifters is critical for the design of efficient mmWave massive MIMO systems.

This paper studies non-orthogonal transmission over a K-user fading multiple access channel. We propose a new reliable physical-layer network coding and cascade-computation decoding scheme. In the proposed scheme, K single-antenna users encode their messages by the same practical channel code and QAM modulation, and transmit simultaneously. The receiver chooses K linear coefficient vectors and computes the associated K layers of finite-field linear message combinations in a cascade manner. Finally, the K users' messages are recovered by solving the K linear equations. The proposed can be regarded as a generalized onion peeling. We study the optimal network coding coefficient vectors used in the cascade computation. Numerical results show the performance of the proposed approaches that of the iterative maximum a posteriori probability detection and decoding scheme, but without using receiver iteration. This results in considerable complexity reduction, processing delay, and easier implementation. Our proposed scheme significantly outperforms the iterative detection and decoding scheme with a single iteration, for example, by 1.7 dB for the two user case. The proposed scheme provides a competitive solution for non-orthogonal multiple access.

This paper studies physical layer security in a wireless ad hoc network with numerous legitimate transmitter-receiver pairs and eavesdroppers. A hybrid full-duplex (FD)/half-duplex receiver deployment strategy is proposed to secure legitimate transmissions, by letting a fraction of legitimate receivers work in the FD mode sending jamming signals to confuse eavesdroppers upon their information receptions, and letting the other receivers work in the half-duplex mode just receiving their desired signals. The objective of this paper is to choose properly the fraction of FD receivers for achieving the optimal network security performance. Both accurate expressions and tractable approximations for the connection outage probability and the secrecy outage probability of an arbitrary legitimate link are derived, based on which the area secure link number, network-wide secrecy throughput, and network-wide secrecy energy efficiency are optimized, respectively. Various insights into the optimal fraction are further developed, and its closed-form expressions are also derived under perfect self-interference cancellation or in a dense network. It is concluded that the fraction of FD receivers triggers a non-trivial tradeoff between reliability and secrecy, and the proposed strategy can significantly enhance the network security performance.

We propose a new low-density parity-check (LDPC) coded binary physical-layer network coding (PNC) scheme for Gaussian two-way relay channels. In this scheme, we introduce a bit mapper between the LDPC encoder and the modulator, which considers the unequal error protections brought by the high order PSK modulations. We add a new bipartite sub-channel graph consisting of sub-channels and variable nodes (VNs) to the Tanner graph and propose a progressive edge growth (PEG) algorithm to design the bit mapper. The design paradigm is to search for the bit mapping distribution with the lowest decoding threshold by using the extrinsic information transfer (EXIT) chart, and then establish the edges progressively between VNs and sub-channels according to the distribution. The proposed PEG algorithm is employed to design the bit mappers of the schemes with 8-PSK, 16-PSK, 64-PSK and 256-PSK. Simulation results show that the proposed schemes can considerably improve the bit error performance of the PNC XOR messages, compared to the schemes without bit mappers.

We propose and design a lattice coded physical- layer network coding (PNC) over a finite complex number field Z[ω]ξZ[ω] in a two-way relay channel (TWRC). In our design, we construct the lattice codes from an irregular repeat- accumulate (IRA) code over GF(q). A randomly generated coset is employed to our scheme to ensure that the codes exhibit permutation invariance and symmetric properties. In a TWRC, two users employ the same lattice codebook and use the same transmit power. The relay attempts to decode the lattice coded network codes of the two users' messages by using an iterative belief propagation decoder and then broadcasts the lattice network coded messages back to both users. We use extrinsic information transfer (EXIT) charts to analyse the convergence behaviour and optimise the decoding threshold. Our results show that the optimised IRA lattice network codes can provide significant coding gain over the previous designed lattice coded PNC scheme over one dimensional Z lattice.

In this paper, we study the power-efficient resource allocation for multicarrier non-orthogonal multiple access (MC-NOMA) systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which takes into account the statistical channel state information at transmitter and quality of service (QoS) constraints. To strike a balance between system performance and computational complexity, we propose a suboptimal power allocation and user scheduling with low computational complexity to minimize the total power consumption. The proposed design exploits the heterogeneity of QoS requirement to determine the successive interference cancellation decoding order. Simulation results demonstrate that the proposed scheme achieves a close-to-optimal performance and significantly outperforms a conventional orthogonal multiple access (OMA) scheme.

We propose a new linear physical-layer network coding and information combining scheme for the K-user fading multiple-access relay network (MARN), which consists of K users, one relay and one destination. The relay and the destination are connected via a rate-constraint backhaul link. In the proposed scheme, the K users transmit signals simultaneously. The relay and the destination receive the superimposed signals distorted by fading and noise. The relay reconstructs L linear combinations of the K users' messages, referred to as L network coded (NC) messages, and forwards them to the destination. The destination then attempts to recover all K users' messages by combining its received signals and the NC messages obtained from the relay. We develop an explicit expression on the selection of the coefficients of the NC messages at the relay that minimizes the end-to-end error probability at a high signal to noise ratio. We demonstrate that our proposed scheme outperforms the benchmark scheme significantly in an MARN.

In this paper, we model and analyze the downlink (DL) wireless power transfer and uplink (UL) information transmission of K-tier heterogeneous cellular networks (HCNs). Due to the densely located BSs and universal frequency reuse between all tiers in HCNs, the typical mobile terminal (MT) is allowed to harvest energy from the serving BS by direct beamforming, as well as from the other interfering BSs. Equipped with large storage battery, the typical MT utilizes the harvested energy to provide constant transmit power for the UL information transmission. Stochastic geometry is used to model and evaluate the intrinsic relationship between the energy harvested from the BSs in the DL and the information transmission performance in the UL. To well evaluate the system performance, we derive exact expressions for the maximum transmit power at MT and the UL average ergodic rate. Our results show that the UL average ergodic rate per random MT is not significantly improved by increasing the energy conversion efficiency.

In this paper we investigate the coded slotted ALOHA (CSA) schemes with repetition codes and maximum distance separable (MDS) codes over erasure channels. We derive the extrinsic information transfer (EXIT) functions of the CSA schemes over erasure channels, which allow an asymptotic analysis of the packet recovering process. Moreover, we define a traffic load threshold provided that the recovered probability is more than a given recovery ratio. The optimal distribution of the codes chosen by users in the CSA schemes is then designed to maximize the peak throughput and traffic load threshold. By performing the asymptotic analysis, we show that our optimal distributions improve the traffic load threshold by 60% for ε = 0.1 and 86% for ε = 0.135 compared to the optimal distribution for collision channels. Using repetition codes as an example, simulation results show that the obtained distributions enhance the peak throughput for erasure channels when both packet erasure channels and slot erasure channels are considered.

In this paper, we investigate uplink channel estimation and multiuser downlink transmission of a massive MIMO time-division duplex system in the presence of pilot contamination, where the base station (BS) with M antennas communicates with N single-antenna users in a cell. We assume that all channels are affected by Rician fading. We also assume that angles of arrival from users to the BS are different. We first analyze the impact of pilot contamination on the channel estimation, based on which we derive a tight sum-rate approximation. We also obtain the asymptotic sum-rate for large Rician K-factor in the large signal to noise ratio regime. Furthermore, we examine the impact of the Rician K-factor on the sum-rate of the system, showing that the sum-rate increases as K-factor increases.

In this paper, we consider the design of the bit mapping in low-density parity-check (LDPC) coded bit-interleaved coded modulation (BICM) schemes. We introduce a two-layer bipartite graph to represent the LDPC coded BICM scheme where a new bit mapping graph linking sub-channels to variable nodes (VNs) is added to the conventional Tanner graph. We propose a progressive edge growth (PEG) algorithm to design the bit mapping for the BICM scheme. The design paradigm is to provide more protections to the VNs that are allocated to the sub-channels with the lowest mutual information. We define a novel concept of unreliable depth profile to classify the reliability of the VNs. By connecting more reliable edges to the least reliable VNs, we can significantly improve the reliable edge distribution for the unreliable VNs, thus improve the extrinsic information in the iterative decoding. The proposed bit mapping algorithm is employed for the design of the LDPC coded BICM with 64-QAM and 256-QAM. Simulation results show that the proposed design can considerably improve the error performance, compared to the conventional consecutive bit mapping strategy.

We investigate the construction of weakly-secure index codes for a sender to send messages to multiple receivers with side information in the presence of an eavesdropper. We derive a sufficient and necessary condition for the existence of index codes that are secure against an eavesdropper with access to any subset of messages of cardinality t, for any fixed t. In contrast to the benefits of using random keys in secure network coding, we prove that random keys do not promote security in three classes of index-coding instances.

In this paper, we propose a new linear physical- layer network coding (NC) scheme for the fading Y- channel, assuming that the channel state information (CSI) is not available at transmitters. In this scheme, each user transmits one message to a relay and intends to obtain both other two users' messages. Based on the receiver- side CSI, the relay determines two NC generator vectors for linear network coding, and reconstructs the associated two linear NC codewords. For the case when there is one time- slot in the uplink phase, we present an explicit solution for the generator vectors that minimizes the error probability at a high SNR, and a lower bound of the error performance of the proposed scheme using our optimized generator vectors. Extending to multiple time-slots in the uplink, two typical scenarios are discussed. Numerical results show that the proposed scheme significantly outperforms existing schemes, and match well with our analytical results.

In this paper, we construct binary spatially coupled (SC) low-density parity-check (LDPC) codes based on Euclidean geometry (EG) LDPC codes for storage applications, where high error correction capability, extremely low uncorrectable bit error rate (UBER), and low decoding complexity are required. We propose a systematic way to construct the families of SC LDPC codes from (m,2s) EG LDPC codes, which are termed EG-SC LDPC codes. In the construction method, we propose a 2-D edge-spreading process to construct the base matrix of EG-SC LDPC codes, which consists of matrix unwrapping and periodically time-varying of a protograph. A lower bound on the rank of the parity-check matrix of an EG-SC LDPC code is derived. We evaluate the error rate performance of the constructed EG-SC LDPC codes by using a weighted bit-flipping decoding algorithm for its low decoding complexity. Numerical results show that the UBER performance of the constructed EG-SC LDPC codes is superior to that of their EG LDPC code counterparts, and show no error floor compared with the constructed protograph SC LDPC codes and regular LDPC codes.

In this paper, we model and analyze the downlink (DL) wireless power transfer and uplink (UL) information transmission of K-tier heterogeneous cellular networks (HCNs) with randomly located base stations (BSs) and mobile terminals (MTs). In the DL and UL, each energy-constrained MT pairs up with its corresponding BS, which provides the maximum received power at the MT. Due to the densely located BSs and universal frequency reuse between all tiers in HCNs, the typical MT is allowed to harvest energy from the serving BS by direct beamforming as well as from the other interfering BSs. Equipped with large storage battery, the typical MT utilizes the harvested energy to provide constant transmit power for the UL information transmission. Stochastic geometry is used to model and evaluate the intrinsic relationship between the energy harvested from the BSs in the DL and the information transmission performance in the UL. To well evaluate the system performance, we first derive exact expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. As the number of BS antennas goes to infinity, we further derive asymptotic expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. Our results show that the UL outage probability per MT first decreases and then increases with increasing the time allocation factor (the fraction of time allocated to the DL), and the UL outage probability, and the UL average ergodic rate per MT, can be largely improved by using the massive antenna arrays at the BSs.

In this paper, we investigate secure communications uplink transmissions, where there are a base station (BS) with M receive antennas, K mobile users each with a single antenna, and an eavesdropper with N receive antennas. The closed-form expressions of the achievable ergodic secrecy sum rates (ESSRs) for a random k users selection scheme in the high and low signal-to-noise ratio regimes are presented. It is shown that the scaling behavior of ESSR with respect to the number served users k can be quite different under different system configurations, determined by the numbers of the BS antennas and that of the eavesdropper antennas. In order to achieve multiuser gain, two low-complexity user selection schemes are proposed under different assumptions on the eavesdropper's channel state information. The closed-form expressions of the achievable ESSRs and the multiuser secrecy gains of the two schemes are also presented in both low and high SNR regimes. observe that, as k increases, the multiuser secrecy gain increases, while the ESSR may decrease. Therefore, when N much larger than M, serving one user with the strongest channel (Time Division Multiple Address-like) is a favorable secrecy scheme, where the ESSR scales with √2 log K.

We investigate beamforming and artificial noise generation at the secondary transmitters to establish secure transmission in large scale spectrum sharing networks, where multiple noncolluding eavesdroppers attempt to intercept the secondary transmission. We develop a comprehensive analytical framework to accurately assess the secrecy performance under the primary users' quality of service constraint. Our aim is to characterize the impact of beamforming and artificial noise generation (BF&AN) on this complex large scale network. We first derive exact expressions for the average secrecy rate and the secrecy outage probability. We then derive an easy-to-evaluate asymptotic average secrecy rate and asymptotic secrecy outage probability when the number of antennas at the secondary transmitter goes to infinity. Our results show that the equal power allocation between the useful signal and artificial noise is not always the best strategy to achieve maximum average secrecy rate in large scale spectrum sharing networks. Another interesting observation is that the advantage of BF&AN over BF on the average secrecy rate is lost when the aggregate interference from the primary and secondary transmitters is strong, such that it overtakes the effect of the generated AN.

A general distributed multi-antenna two-way relaying network with multiple relays is considered in this paper. We first obtain a tight lower bound of pairwise error probability (PEP) of a maximum likelihood detector for the general distributed linear dispersion code for a half-duplex amplify-and-forward two-way relaying network (TWRN) consisting of two sources with each having single antenna and N relays with each having two antennas. Furthermore, by jointly considering signal precoding at the sources and signal processing at the relays, a general distributed concatenated recursive Alamouti-circulant space-time block code is proposed for the considered TWRNs. Our design ensures that the equivalent channel matrices at both source nodes are the so-called the recursive Alamouti-circulant matrices, with each block being a product of the two Alamouti channel matrices. Based on a lower-upper bound strategy and an induction method, asymptotic PEP formula is attained to show that given the optimal angle rotation matrix and the precoding matrix, the code can meet the lower bound of the diversity gain, as well as the maximum coding gain. In addition, the proposed rate one code turns to be effectively decodable.

In this paper, we investigate a rateless code design for a two-time slot two-way relay channel (TWRC), where network coding operation is employed at the relay. Rateless codes are used at both users and the relay nodes as they can cope with various channel conditions. We consider a general TWRC framework, where the two users may use different rateless code generator matrices, thus, the relay cannot directly recover the physical-layer network coded (PNC) message of the two users. We propose a hybrid estimate and forward (EF)/decode and forward (DF) protocol with rateless codes for the TWRC. In the proposed scheme, the relay uses a joint belief propagation decoding process to decode the messages of two users. Depending on whether the relay recovers both users' messages, only one user's message or no users' messages, the relay will generate three different types of signals, namely, NC, soft NC, and soft PNC, respectively. We design the degree distributions of the rateless codes for the three nodes of the TWRC, to make them suitable not only for decoding at two users at high signal-to-noise ratio (SNR), but also for decoding at the relay at low SNR. We show that the proposed hybrid EF/DF protocol with the designed rateless codes outperforms other schemes significantly in terms of bit error rate performance.

The heterogeneous cellular network (HCN) is a promising approach to the deployment of 5G cellular networks. This paper comprehensively studies physical layer security in a multitier HCN where base stations (BSs), authorized users, and eavesdroppers are all randomly located. We first propose an access threshold-based secrecy mobile association policy that associates each user with the BS providing the maximum truncated average received signal power beyond a threshold. Under the proposed policy, we investigate the connection probability and secrecy probability of a randomly located user and provide tractable expressions for the two metrics. Asymptotic analysis reveals that setting a larger access threshold increases the connection probability while decreases the secrecy probability. We further evaluate the network-wide secrecy throughput and the minimum secrecy throughput per user with both connection and secrecy probability constraints. We show that introducing a properly chosen access threshold significantly enhances the secrecy throughput performance of a HCN.

We design a new relay-aided secure transmission scheme, in which a source communicates with a destination through a trusted decode-and-forward relay in the presence of spatially random-distributed non-colluding eavesdroppers. We consider a general antenna configuration, in which the source, relay, destination, and eavesdroppers are equipped with multiple antennas. We assume that both the source and the relay transmit artificial noise signals in addition to information signals. We also assume that the source and the relay adopt different codebooks, and that the transmitted signals from the source and relay are not jointly processed at each eavesdropper. We first derive a closed-form expression for the transmission outage probability and a new expression for the secrecy outage probability. Notably, these expressions are valid for an arbitrary number of antennas at the source, relay, and destination. We then derive simple yet valuable expressions for the asymptotic transmission outage probability and the asymptotic secrecy outage probability, which reveal the secrecy performance when the number of antennas at the source grows sufficiently large. Using our expressions, we quantify a practical performance metric, namely, the secrecy throughput, under a secrecy outage probability constraint. We further determine the system and channel parameters that maximize the secrecy throughput, leading to analytical security solutions suitable for real-world deployment.

We propose a new linear physical-layer network coding (LPNC) and information combining scheme for the K-user fading multiple-access relay network (MARN), which consists of K users, one relay, and one destination. The relay and the destination are connected by a rate-constraint wired or wireless backhaul. In the proposed scheme, the K users transmit signals simultaneously. The relay and the destination receive the superimposed signals distorted by fading and noise. The relay reconstructs L linear combinations of the K users' messages, referred to as L network-coded (NC) messages, and forwards them to the destination. The destination then attempts to recover all K users' messages by combining its received signals and the NC messages obtained from the relay. We develop an explicit expression on the selection of the coefficients of the NC messages at the relay that minimizes the end-to-end error probability at a high signal-to-noise ratio. We develop a channel-coded LPNC scheme by using an irregular repeat-accumulate modulation code over GF(q). An iterative belief-propagation algorithm is employed to compute the NC messages at the relay, while a new algorithm is proposed for the information combining decoding at the destination. We demonstrate that our proposed scheme outperforms benchmark schemes significantly in both un-channel-coded and channel-coded MARNs.

We investigate beamforming and artificial noise generation at the secondary transmitters to establish secure transmission in large scale spectrum sharing networks, where multiple non-colluding eavesdroppers attempt to intercept the secondary transmission. We develop a comprehensive analytical framework to accurately assess the secrecy performance under the primary user's quality of service constraint. Our aim is to characterize the impact of beamforming and artificial noise generation on this complex large scale network. We first derive the exact expressions for the average secrecy rate and the secrecy outage probability. Our results show that there exists an average secrecy rate wall beyond which the primary user's quality of service is violated. Interestingly, we find that different from the conventional network with fixed nodes where equal power allocation achieves near optimal average secrecy rate, the equal power allocation may not be a good option for large scale spectrum sharing networks.

We design a linear precoder based on the principles of the generalized regularized channel inversion (RCI) precoder that achieves confidential broadcasting in a two-cell network. In each cell of the network, an N-antenna base station (BS) communicates with K single-antenna users. We consider coordinated beamforming where the BSs in the two cells do not share messages but the users in the two cells feed back their channel state information to both BSs. In the precoder design, we determine the optimal regularization parameter that maximizes the secrecy sum rate. To this end, we derive new channel-independent expressions for the secrecy sum rate in the large-system regime, where K and N approach infinity with a fixed ratio μ = K/N. Moreover, we propose a power-reduction strategy that significantly improves the secrecy sum rate at high transmit signal-to-noise ratios when μ is higher than 0.5.

In this paper, we propose a new linear physical-layer network coding scheme for spatial-multiplexing MIMO two-way relay channels (TWRCs), where the transmitters lack the channel state information (CSI). In the uplink, each user transmits independent signal streams from its multiple antennas, and the two users transmit simultaneously. The relay selects a finite-field coefficient matrix based on its receiver CSI. It then jointly computes the associated linear combinations of all messages. In the downlink, the resultant message-combinations are forwarded to the users, which recover their desired messages. We derive an asymptotic expression for the coefficient matrix used by the relay that minimizes the error probability. We show by numerical results that for Rayleigh fading channel, the proposed linear PNC scheme outperforms existing schemes by up to 4.5 dB and that the proposed scheme approaches an interference-free lower bound at a sufficiently high SNR.

Location information has been shown to be useful for a wide variety of applications in wireless networks, while its role in physical layer security has so far drawn little attention. In this work, we propose a new location-based secure transmission scheme for wiretap channels, where the accurate locations of the sources, destinations and any other authorized transceivers are known, but only an estimate of the eavesdropper's location is available. We outline how such an estimate of the eavesdropper's location can still allow for quantitative assessment of key security metrics. To provide focus, we describe how optimization of the effective secrecy throughput of a relay wiretap channel is obtained in our scheme, and investigate in detail the impact of the location uncertainty on the system performance. The work reported here provides insights into the design of new location-based physical layer security schemes in which the only information available on an eavesdropper is a noisy estimate of her location.

A distributed multiple-input-multiple-output (MIMO) system consists of M users served by L distributed base stations (BSs), where the BSs are connected to a central unit (CU) via L independent backhaul (BH) links. In this paper, we consider the design of an uplink distributed MIMO system where 1) the channel state information is not available at the transmitters and 2) the BH links are rate constrained. We propose a new linear network coding (LNC)-based protocol: the M users transmit simultaneously. Each BS generates N linear functions of the M users' messages, based on a preassigned LNC coefficient matrix. The CU collects N · L linear functions from the L BSs and recovers all M users' messages by solving these linear functions. The decoding becomes successful if the linear functions has full rank M and fails if the linear functions are rank deficient. We derive the preassigned LNC coefficient matrix that minimizes the probability of rank deficiency. We then analyze the outage probability (OP) of the proposed scheme over a Rayleigh fading channel. We analytically show that as long as the BH rate is greater than the individual data rate of one user, the OP of the proposed scheme decays like 1/SNRLat high SNR. This is in contrast to the existing scheme whose OP decays like 1/SNR. As the BH rate constraint approaches M times the data rate of one user, the performance of the proposed scheme is 10/L log10(L!) dB away from that of the full MIMO scenario at high SNR. We also develop a structured way to efficiently construct the preassigned LNC coefficient matrix that yields the optimized OP performance. Numerical results show that the proposed scheme has significantly improved performance over existing schemes.

Special issue on recent advances in network and information security - security and communication networks journal

In this paper, we propose a new linear vector physical-layer network coding (NC) scheme for spatial multiplexing multiple-input multiple-output (MIMO) two-way relay channel (TWRC) where the channel state information (CSI) is not available at the transmitters. In this scheme, each user transmits M independent quadrature amplitude modulation signal streams respectively from its M antennas to the relay. Based on the receiver-side CSI, the relay determines a NC generator matrix for linear vector network coding, and reconstructs the associated M linear combinations of all messages. We present an explicit solution for the generator matrix that minimizes the error probability at a high SNR, as well as an efficient algorithm to find the optimized solution. We propose a novel typical error event analysis that exploits a new characterization of the deep fade events for the TWRC. We derive a new closed-form expression for the average error probability of the proposed scheme over a Rayleigh fading MIMO TWRC. Our analysis shows that the proposed scheme achieves the optimal error rate performance at a high SNR. Numerical results show that the proposed scheme significantly outperforms existing schemes, and match well with our analytical results.

We analyze and optimize the secrecy performance of artificial noise (AN) in multi-input single-output wiretap channels with multiple antennas at the transmitter and a single antenna at the receiver and the eavesdropper. We consider two transmission schemes: 1) an on-off transmission scheme with a constant secrecy rate for all transmission periods, and 2) an adaptive transmission scheme with a varying secrecy rate during each transmission period. For the on-off transmission scheme, an easy-to-compute expression is derived for the hybrid outage probability, which allows us to evaluate the transmission outage probability and the secrecy outage probability. For the adaptive transmission scheme where transmission outage does not occur, we derive a closed-form expression for the secrecy outage probability. Using these expressions, we determine the optimal power allocation between the information signal and the AN signal and also determine the optimal secrecy rate such that the effective secrecy throughput is maximized for both transmission schemes. We show that the maximum effective secrecy throughput requires more power to be allocated to the AN signal when the quality of the transmitter-receiver channel or the transmitter-eavesdropper channel improves. We also show that both transmission schemes achieve a higher maximum effective secrecy throughput while incurring a lower secrecy outage probability than existing schemes.

In this paper, we study the secure multi-antenna transmission with artificial noise (AN) under slow fading channels coexisting with randomly located eavesdroppers. We provide a comprehensive secrecy performance analysis and system design/optimization under a stochastic geometry framework. Specifically, we first evaluate the secrecy outage performance, and derive a closed-form expression for the optimal power allocation ratio of the information signal power to the total transmit power that minimizes the secrecy outage probability (SOP). Subject to a SOP constraint, we then propose a dynamic parameter transmission scheme (DPTS) and a static parameter transmission scheme (SPTS) to maximize secrecy throughput, and provide explicit solutions on the optimal transmission parameters, including the wiretap code rates, the on-off transmission threshold and the power allocation ratio. Our results give new insight into secure transmission designs. For example, secrecy rate is a concave function of the power allocation ratio in DPTS, and AN plays a significant role under SOP constraints and in dense eavesdropper scenarios. In SPTS, transmission probability is a concave function of the power allocation ratio, and secrecy throughput is a quasi-concave function of the secrecy rate. Numerical results are demonstrated to validate our theoretical analysis.

We design linear precoders that perform confidential broadcasting in multi-cell networks for two different forms of base station (BS) cooperation, namely, multi-cell processing (MCP) and coordinated beamforming (CBf). We consider a two-cell network where each cell consists of an $N$-antenna BS and $K$ single-antenna users. For such a network, we design a linear precoder based on the regularized channel inversion (RCI) for the MCP and a linear precoder based on the generalized RCI for the CBf. For each form of BS cooperation, we derive new channel-independent expressions to approximate the secrecy sum rate achieved by the precoder in the large system regime where $K,N\rightarrow\infty$ with a fixed ratio $\beta=K/N$. Using these results, we determine the optimal regularization parameters of the RCI and the generalized RCI precoders that maximize the secrecy sum rate for the MCP and the CBf, respectively. We further propose power-reduction strategies that significantly increase the secrecy sum rate at high transmit signal-to-noise ratios when the network load is high. Our numerical results substantiate the derived expressions, verify the optimality of the determined optimal regularization parameters, and demonstrate the performance improvement offered by the proposed power-reduction strategies.

We propose a new partial feedback scheme in multiuser relay networks (MRNs) where a source communicates with K destinations via a relay. We focus on a practical network model where orthogonal frequency division multiple access (OFDMA) is adopted in the downlink and only limited feedback overhead is supported in the uplink. For this model, we consider that the OFDMA spectrum consists of MRB resource blocks (RBs). In the proposed scheme, the destinations feed back the channel quality information (CQI) for the best MFB RBs, instead of all MRB RBs, to the source through the relay, which fulfills the requirement of feedback overhead. Considering the highly versatile Nakagami-m fading, we derive new closed-form expressions for the exact sum rate for ideal CQI feedback and quantized CQI feedback. We also derive the asymptotic sum rate expression for ideal CQI feedback. We have some new findings to understand the impact of network and channel parameters on the sum rate. First, a more scattering fading environment with a lower m decreases the sum rate for a small K, but increases the sum rate for a large K. Second, the sum rate increases as MFB approaches MRB. Third, the sum rate gap between ideal CQI feedback and quantized CQI feedback increases when MFB or K increases. Fourth, we demonstrate that the proposed partial feedback scheme achieves almost the same sum rate as the full feedback scheme for a large number of destinations.

We propose a new framework for determining the wiretap code rates of single-input-single-output multiantenna eavesdropper wiretap channels when the capacity of the eavesdropper's channel is not available at the transmitter. In our framework, we introduce the effective secrecy throughput (EST) as a new performance metric that explicitly captures the two key features of wiretap channels, namely, reliability and secrecy. Notably, the EST measures the average rate of the confidential information transmitted from the transmitter to the intended receiver without being eavesdropped on. We provide easy-to-implement methods to determine the wiretap code rates for two transmission schemes: 1) adaptive transmission scheme in which the capacity of the main channel is available at the transmitter and 2) fixed-rate transmission scheme in which the capacity of the main channel is not available at the transmitter. Such determinations are further extended into an absolute-passive eavesdropping scenario where even the average signal-to-noise ratio of the eavesdropper's channel is not available at the transmitter. Notably, our solutions for the wiretap code rates do not require us to set reliability or secrecy constraints for the transmission within wiretap channels.

We propose and design a practical modulation-coded (MC) physical-layer network coding (PNC) scheme to approach the capacity limits of Gaussian and fading two-way relay channels (TWRCs). In the proposed scheme, an irregular repeat-accumulate (IRA) MC over GF(q) with the same random coset is employed at two users, which directly maps the message sequences into coded PAM or QAM symbol sequences. The relay chooses appropriate network coding coefficients and computes the associated finite-field linear combinations of the two users' message sequences using an iterative belief propagation algorithm. For a symmetric Gaussian TWRC, we show that, by introducing the same random coset vector at the two users and a time-varying accumulator in the IRA code, the MC-PNC scheme exhibits symmetry and permutation-invariant properties for the soft information distribution of the network-coded message sequence (NCMS). We explore these properties in analyzing the convergence behavior of the scheme and optimizing the MC to approach the capacity limit of a TWRC. For a block fading TWRC, we present a new MC linear PNC scheme and an algorithm used at the relay for computing the NCMS. We demonstrate that our developed schemes achieve near-capacity performance in both Gaussian and Rayleigh fading TWRCs. For example, our designed codes over GF(7) and GF(3) with a code rate of 3/4 are within 1 and 1.2 dB of the TWRC capacity, respectively. Our method can be regarded as a practical embodiment of the notion of compute-and-forward with a good nested lattice code, and it can be applied to a wide range of network configurations.

In this paper, we investigate the degrees of freedom (DoF) of the half-duplex multiple-input multiple-output (MIMO) multi-way relay channel (MWRC) with full data exchange, where each user wants to learn all the messages from the other users in the channel. We observe that, unlike the case of pairwise data exchange, the uplink and downlink traffic loads are asymmetric when full data exchange is considered. This asymmetry implies that unequal uplink/downlink time allocation, which is allowed in a half-duplex system, can improve the DoF of the MIMO MWRC. Based on that, we derive the DoF capacity of the half-duplex MIMO MWRC with full data exchange. We show that, as compared to the equal uplink/downlink time allocation, the optimized uplink/downlink time allocation achieves a significant DoF gain.

In relay assisted wireless communications, the multi-source, single relay and single destination system (an M-1-1 system) has garnered significant interest, due to the increased demand for higher network throughput and connectivity. Previously, power allocation at the relay in M-1-1 systems have assumed availability of instantaneous channel state information (CSI), which is rather idealistic. In this paper we consider an M-1-1 Decode-and-Forward (DF), Full-Duplex, orthogonal frequency division multiple access (OFDMA) based relay system with statistical-CSI and analyze the achievable rate R of such a system. We show how R can only be maximized by numerical power allocation schemes which have high-complexity of order O(M3). By introducing a rational approximation in the achievable rate analysis, we develop a low-complexity power allocation scheme at the relay that can obtain a system achievable rate very close to the maximum R. Most importantly, we show that the complexity of our power allocation scheme is of the order O(M log M). Our power allocation scheme is suitable for a multiuser relay system, where the priority is to maximize the system throughput. The work we present in this paper will be of value to the design and implementation of real-time multi-user relay systems operating under realistic channel conditions.

In this paper, we study physical layer security for the downlink of cellular networks. In a cellular network, the confidential messages transmitted to each mobile user can be eavesdropped by the other users in the same cell and also by the users in the other cells. We model the locations of base stations and mobile users as two independent two-dimensional Poisson point processes. By combining tools from stochastic geometry and random matrix theory, we analyze the secrecy rates achievable with regularized channel inversion (RCI) precoding under Rayleigh fading. Our analysis shows that unlike isolated cells, the secrecy rate in a cellular network does not grow monotonically with the transmit power. Moreover, we find that the network tends to be in secrecy outage if the transmit power grows unbounded. Furthermore, we show that there exists an optimal value for the base station deployment density that maximizes the secrecy rate.

In this paper, we consider the broadcast channel with confidential messages and eavesdroppers (BCCE), where a multi-antenna base station simultaneously communicates to multiple potentially malicious users, in the presence of external eavesdroppers randomly located according to a Poisson point process (PPP). By using techniques from stochastic geometry and random matrix theory, we obtain explicit expressions for the secrecy outage probability and mean secrecy rate achievable with regularized channel inversion precoding. We show that both these metrics scale as -4fe, where N is the number of transmit antennas and Aeis the density of external eavesdroppers.

We propose two beamforming schemes supporting multi-stream transmission in multi-input multi-output multi-antenna eavesdropper wiretap channels with imperfect channel state information of the eavesdropper. We first propose a generalized eigenvalue decomposition (GEVD)-based beamforming scheme by designing the beamforming matrix and determining the power allocation matrix. In particular, we determine a general power allocation matrix for arbitrary signal-to-noise ratio (SNR) and a simplified power allocation matrix for high SNR. We demonstrate that our GEVD-based beamforming scheme delivers a higher achievable secrecy rate than the existing beamforming schemes in the medium and high SNR regime. We also demonstrate that the simplified power allocation matrix delivers the same achievable secrecy rate as the general power allocation matrix at high SNRs. We then propose an easy-to-construct EVD-based beamforming scheme which reduces signal processing cost and eliminates power allocation. We demonstrate that our EVD-based beamforming scheme delivers a higher secrecy rate than the GEVD-based beamforming scheme and the existing beamforming schemes in the low SNR regime.

This paper advocates physical layer security in wiretap channels with two-wave with diffuse power fading. In such a wiretap channel, we consider that confidential messages transmitted from a single antenna transmitter to an M-antenna receiver is overheard by an N-antenna eavesdropper. The receiver adopts maximal-ratio combining (MRC) to enhance transmission security, whereas the eavesdropper adopts MRC to maximize the probability of successful eavesdropping. We develop a new analytical framework to characterize the average secrecy capacity as the principal security performance metric in active eavesdropping. Specifically, we derive new closed-form expressions for the exact and asymptotic average secrecy capacity. Based on these, we determine the high signal-to-noise ratio power offset to explicitly quantify the impact of the main channel and the eavesdropper's channel on the average secrecy capacity.

We propose a new framework for optimizing the target secrecy rate for SISOME wiretap channels when the instantaneous capacity of the eavesdropper's channel is not available at the transmitter. In our framework we introduce the effective secrecy throughput, a new optimization metric that implicitly captures the two key features of wiretap channels, namely, reliability and secrecy. We derive target secrecy rates which maximize the effective secrecy throughput for two different schemes, an on-off transmission scheme and an adaptive transmission scheme. Our analysis demonstrates that the adaptive transmission scheme outperforms the on-off transmission scheme and that the difference in the effective secrecy throughput between the two schemes increases with the SNR of the main channel. The work reported here solves the important problem of how to optimally set the target secrecy rate of wiretap codes for an important class of channels. Notably, our solution for the target secrecy rate does not require us to set a priori any reliability or secrecy constraint for the channel.

We analyze and optimize the use of artificial noise (AN) for a predefined secrecy rate in wiretap channels with a multi-antenna transmitter, a single-antenna receiver, and a single-antenna eavesdropper. We derive a new closed-form expression for the secrecy outage probability that is independent of the channel realization. Based on this expression, we first optimize the power allocation between the information signal and the AN signal such that the secrecy outage probability is minimized. We then optimize jointly the power allocation and secrecy rate such that the secrecy throughput is maximized. As demonstrated by our analysis, the minimum secrecy outage probability requires more power to be allocated to the AN signal when the quality of the main channel quality or the eavesdropper's channel improves.

Quantum synchronizable codes are quantum error-correcting codes designed to correct the effects of both quantum noise and block synchronization errors. While it is known that quantum synchronizable codes can be constructed from cyclic codes that satisfy special properties, only a few classes of cyclic codes have been proved to give promising quantum synchronizable codes. In this paper, using quadratic residue codes and their supercodes, we give a simple construction for quantum synchronizable codes whose synchronization capabilities attain the upper bound. The method is applicable to cyclic codes of prime length.

The authors propose a low complexity list successive cancellation (LCLSC) decoding algorithm, where the advantages of the successive cancellation (SC) decoding and the list successive cancellation (LSC) decoding are both considered. In the proposed decoding, SC decoding instead of LSC decoding is implemented when all information bits from bad subchannels are received reliably. While the reliability of each information bit is estimated by its likelihood ratio (LR), the bit channel quality is measured via its Bhattacharyya parameter. To achieve this goal, the authors introduce two thresholds: LR threshold and Bhattacharyya parameter threshold. Also, the methods to determine them are both elaborated. The numerical results suggest that the complexity of LCLSC decoding is much lower than LSC decoding and can be close to that of SC decoding, while the error performance is almost equal to that of LSC decoding. Especially, when the code rate is in low region, the advantage of our decoding is more obvious.

In this paper, we propose a novel linear physical-layer network coding scheme over hybrid finite ring (HFR-LPNC) for Rayleigh fading two-way relay channels. The relay maps the superimposed signal of the two users to a linear network coded combination (LNCC) in hybrid finite ring, rather than using the simple bit-wise eXclusive-OR mapping. The optimal linear coefficients are selected to generate the LNCC, aiming to: 1) maximize the sum-rate in the MAC phase; and 2) ensure unambiguous decoding. To avoid the performance degradation caused by high-order irregular mappings, properly designed source coding is used for compressing the LNCC alphabet over the hybrid finite ring into the unifying 4-ary alphabet. We derive the constellation constrained sum-rates for HFR-LPNC in comparison with 5QAM denoise-and-forward (5QAM-DNF), which we use as a reference scheme. Furthermore, we explicitly characterize the rate difference between HFR-LPNC and 5QAM-DNF. Our analysis and simulation show that: 1) HFR-LPNC has a superior ability to mitigate the singular fading compared with 5QAM-DNF; and 2) HFR-LPNC is superior to 5QAM-DNF over a wide range of SNRs.

In this paper, we study physical layer security for the downlink of cellular networks, where the confidential messages transmitted to each mobile user can be eavesdropped by both (i) the other users in the same cell and (ii) the users in the other cells. The locations of base stations and mobile users are modeled as two independent two-dimensional Poisson point processes. Using the proposed model, we analyze the secrecy rates achievable by regularized channel inversion (RCI) precoding by performing a large-system analysis that combines tools from stochastic geometry and random matrix theory. We obtain approximations for the probability of secrecy outage and the mean secrecy rate, and characterize regimes where RCI precoding achieves a nonzero secrecy rate. We find that unlike isolated cells, the secrecy rate in a cellular network does not grow monotonically with the transmit power, and the network tends to be in secrecy outage if the transmit power grows unbounded. Furthermore, we show that there is an optimal value for the base station deployment density that maximizes the secrecy rate, and this value is a decreasing function of the signal-to-noise ratio.

We propose linear precoding with power control to achieve confidential broadcasting in multi-input-multi-output multiuser networks such that the base station (BS) with Ntantennas securely broadcasts messages to K users with Nrantennas each. We focus on the practical non-homogeneous scenario where the distances between the BS and the users are not equal. We first design a linear precoder based on regularized channel inversion, and derive new channel-independent expressions for the achievable secrecy sum-rate in the large system regime. With the aid of these expressions, we examine the impact of user dispersion, Nt, and K on the secrecy sum-rate. We then propose a power reduction strategy and power allocation algorithms to increase the secrecy sum-rate. We demonstrate that our power reduction strategy increases the secrecy sum-rate at high signal-to-noise ratios. We also show the secrecy sum-rate advantage of optimal power allocation over equal power allocation. Furthermore, we consider channel correlation and derive an easy-to-compute expression for the secrecy sum-rate to examine its impact on the secrecy performance.

This paper advocates physical layer security of maximal ratio combining (MRC) in wiretap two-wave with diffuse power fading channels. In such a wiretap channel, we consider that confidential messages transmitted from a single antenna transmitter to an M-antenna receiver are overheard by an N-antenna eavesdropper. The receiver adopts MRC to maximize the probability of secure transmission, whereas the eavesdropper adopts MRC to maximize the probability of successful eavesdropping. We derive the secrecy performance for two practical scenarios: 1) the eavesdropper's channel state information (CSI) is available at the transmitter and 2) the eavesdropper's CSI is not available at the transmitter. For the first scenario, we develop a new analytical framework to characterize the average secrecy capacity as the principal security performance metric. Specifically, we derive new closed-form expressions for the exact and asymptotic average secrecy capacity. Based on these, we determine the high signal-to-noise ratio power offset to explicitly quantify the impacts of the main channel and the eavesdropper's channel on the average secrecy capacity. For the second scenario, the secrecy outage probability is the primary security performance metric. Here, we derive new closed-form expressions for the exact and asymptotic secrecy outage probability. We also derive the probability of nonzero secrecy capacity. The asymptotic secrecy outage probability explicitly indicates that the positive impact of M is reflected in the secrecy diversity order and the negative impact of N is reflected in the secrecy array gain. Motivated by this, we examine the performance gap between N and N+1 antennas based on their respective secrecy array gains.

In this paper, we consider the broadcast channel with confidential messages and external eavesdroppers (BCCE), where a multi-antenna base station simultaneously communicates to multiple potentially malicious users, in the presence of randomly located external eavesdroppers. Using the proposed model, we study the secrecy rates achievable with regularized channel inversion (RCI) precoding by performing a large-system analysis that combines results from stochastic geometry and random matrix theory, where the number of users K and the number of transmit antennas N both grow to infinity in a fixed ratio. We obtain explicit expressions for the probability of secrecy outage and an upper bound on the rate loss due to the presence of external eavesdroppers. We show that both these quantities scale as \fraclambda_esqrt{N} as the density of external eavesdroppers λ_e grows, irrespective of their collusion strategy. Furthermore, we derive a practical rule for the choice of the regularization parameter, which is agnostic of channel state information and location of eavesdroppers, and yet provides close to optimal performance.

We develop a new and efficient algorithm to solve the problem of joint channel and Doppler offset estimation in time-varying cooperative wireless relay networks. We first formulate the problem as a Bayesian dynamic nonlinear state space model, then develop an algorithm, which is based on particle adaptive marginal Markov chain Monte Carlo, method to jointly estimate the time-varying channels and static Doppler offsets. We perform detailed complexity analysis of the proposed algorithm and show that it is very efficient and requires moderate computational complexity. In addition, we develop a new version of the recursive marginal Cramér-Rao lower bound and derive expressions for the achievable mean-square error. Simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms and performs close to the Cramér-Rao lower bound.

Due to the overwhelming complexity of multihop transmission and intermessage interference, only a limited amount of research has been carried out in the implementation of wireless network coding (WNC) for generalized multihop two-way relay channels (MH-TRCs), let alone the generalization of multihop WNC (MH-WNC) schemes. Our recent paper has showed that the MH-WNC scheme with fixed two transmission time intervals (TTIs) was unable to always outperform conventional non-NC schemes in outage performance for the MH-TRC with an arbitrary number of nodes. In view of this fact, a generalized MH-WNC scheme with multiple TTIs is designed for the L-node K-message MH-TRC in this paper. Closed-form expressions for the upper bound of the outage probability for two prominent relaying network coding strategies (i.e., analog network coding and compute-and-forward network coding) are derived. Moreover, by investigating the relationships between the outage probability and the numbers of nodes, messages, and TTIs, we obtain an optimal MH-WNC scheme that can achieve the best outage probability and always outperform non-NC in the MH-TRC with an arbitrary number of nodes.

In this work, we propose a new transmit antenna selection (TAS) scheme which examines the trade-off between feedback overhead and secrecy performance in multiple-input multiple-output wiretap channels. Our new scheme is carried out in two steps. First, the transmitter selects the first two strongest antennas to maximize the instantaneous signal-to-noise ratio (SNR) of the transmitter-receiver channel. Second, Alamouti coding is employed at the selected antennas in order to perform secure data transmission. When equal power is applied to the selected antennas, we refer to our new scheme as TAS-Alamouti. To provide valuable insights into TAS-Alamouti, we derive new closed-form expressions for the secrecy performance metrics. In terms of these metrics, we show how in a Rayleigh fading channel that our TAS-Alamouti scheme outperforms the traditional single TAS scheme conditioned on the SNR of the transmitter-receiver channel being larger than a specific value. We show how in some antenna configurations no additional feedback, relative to single TAS, is required in order to realize such performance enhancements. Furthermore, we show how optimal power allocation (OPA) across the selected antennas at the transmitter leads to a new scheme, which we refer to as TAS-Alamouti-OPA, that outperforms single TAS unconditionally. Relative to TAS-Alamouti, TAS-Alamouti-OPA requires only one additional feedback bit.

ABSTRACT In this paper, we investigate the optimal sensing settings for a cognitive radio (CR) network consisting a number of CR users and a fusion center (FC). Our objective is to maximize the channel utilization under the constraint that the signals from the primary user (PU) are sufficiently protected. We focus on the utilization of the channel in which PUs dynamically enter the network with burst nature. Thus, we apply the average error probability (AEP) as the metric of channel utilization. Moreover, in order to protect the PU signal from being interfered, the missing detection probability is applied as the constraint function. Assuming that counting rules are utilized in the FC, we derive the false alarm probability, the missing detection probability, and the AEP in the maximum a posteriori (MAP) fashion. Then, after proving the monotonic properties of the objective function and the constraint function, we propose an efficient algorithm named Algorithm I that can derive the optimal settings for maximizing the channel utilization. Moreover, a simplified algorithm named Algorithm II is also proposed to minimize the AEP, supposing that perfect synchronization exists between the CR users and the PU. Finally, we show our numerical results and compare our optimal results with those found by exhaustive searches. We conclude that our Algorithm I produces optimal results very close to those found by exhaustive searches. Performance comparison between Algorithms I and II is also provided in terms of the AEP and the probability of missing detection. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we propose a new beamforming scheme for multi-input multi-output (MIMO) Gaussian wiretap channels where the channel state information (CSI) from the eavesdropper is imperfectly known to the transmitter. A stochastic model is constructed to characterize the imperfect CSI of the eavesdropper, in which a factor 0 ≤ τ ≤ 1 is introduced to describe the degree of the available eavesdropper's channel knowledge at the transmitter. When τ varies from 0 to 1, the eavesdropper's channel knowledge available at the transmitter ranges from statistically known to perfectly known. We design the proposed beamforming scheme by maximizing a lower bound on the achievable secrecy rate. We first demonstrate that our scheme achieves higher secrecy rate than the existing eigenvalue decomposition-based beamforming scheme which is optimal for τ = 0. We then demonstrate that the proposed scheme achieves higher secrecy rate than the existing generalized eigenvalue decomposition-based beamforming scheme which is optimal for τ = 1. Furthermore, we derive tight approximations for the proposed beamforming scheme in the high signal-to-noise ratio (SNR) regime and the low SNR regime. The accuracy of these approximations is validated via numerical results. Finally, we demonstrate that our proposed scheme achieves almost the same secrecy performance as the optimal beamforming solution that is obtained through numerical search.

This paper proposes a new transmit antenna selection (TAS) scheme which provides enhanced physical layer security in multiple-input multiple-output (MIMO) wiretap channels. The practical passive eavesdropping scenario we consider is where channel state information (CSI) from the eavesdropper is not available at the transmitter. Our new scheme is carried out in two steps. First, the transmitter selects the two strongest antennas based on the feedback from the receiver, which maximizes the instantaneous signal-to-noise ratio (SNR) of the transmitter-receiver channel. Second, the Alamouti scheme is employed at the selected antennas in order to perform data transmission. At the receiver and the eavesdropper, maximal-ratio combining is applied in order to exploit the multiple antennas. We derive a new closed-form expression for the secrecy outage probability in non-identical Rayleigh fading, and using this result, we then present the probability of non-zero secrecy capacity in closed form and the ε-outage secrecy capacity in numerical form. We demonstrate that our proposed TAS-Alamouti scheme offers lower secrecy outage probability than a single TAS scheme when the SNR of the transmitter-receiver channel is above a specific value.

In this paper, we study the performance of regularized channel inversion precoding in MISO broadcast channels with confidential messages under imperfect channel state information at the transmitter (CSIT). We obtain an approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas and the number of users grow to infinity in a fixed ratio. Simulations prove this anaylsis accurate even for finite-size systems. For FDD systems, we determine how the CSIT error must scale with the SNR, and we derive the number of feedback bits required to ensure a constant high-SNR rate gap to the case with perfect CSIT. For TDD systems, we study the optimum amount of channel training that maximizes the high-SNR secrecy sum-rate.

When designing two-way relay channels, there is a dilemma of how to reduce the transmission power by network coding (XORing symbols of opposite directions) with a low symbol delay. Moreover, if the channels are fading, an extra penalty caused by error probability should be added to each transmission, which makes the decision-making problem more complex. In this paper, we propose a new model that considers instantaneous signal to noise ratio (SNR) in addition to queue occupation status. In this model, the channel state evolution is traced by a finite state Markov chain. We develop an efficient computational solution utilizing value iteration algorithm to find an optimal policy regarding symbol delay, transmission power consumption, symbol loss due to the queue overflow and transmission error probabilities. Simulation results show that there is an improvement in both the symbol loss rate and the overall system cost in practical scenarios, compared to the conventional modeling method where channel states are ignored.

In this paper, we propose two cooperative jamming protocols in two-hop amplify-and-forward (AF) wiretap channels: 1) jamming signal at the source (JSS) and 2) jamming signal at the relay (JSR). We apply optimal power allocation (OPA) between the useful signal and the jamming signal to maximize the secrecy rate for each of the protocols. A fundamental question to address is “Which cooperative jamming protocol is superior under OPA?” To this end, we evaluate the maximum secrecy rate of JSS and JSR for the general scenario of independent but not necessarily identically distributed fading with distinct average signal-to-noise ratios (SNRs) in the first hop, the second hop, and the wiretap link. We demonstrate that the strong first and second hops equally benefit the secrecy rates of JSS and JSR, when the wiretap link is weak. When the wiretap link is strong, the secrecy rate of JSR is superior to JSS.

In this paper, we propose a novel opportunistic pair-wise transmission scheme in a multi-way relay channel (MWRC), in which multiple users exchange information via a common relay. We investigate pair-wise compute-and-forward for MWRCs by exploiting the multi-user fading channels. Conventionally, a pair-wise physical-layer network coding scheme with binary phase shift keying modulation was studied for a MWRC. In this paper, the proposed opportunistic pair-wise compute-and-forward employs high level modulation with nested lattice codes to improve the sum-rate of multi-user transmission. We demonstrate that this novel opportunistic pair-wise transmission has a 2 bits/s/Hz improvement in the sum-rate performance at signal-to-noise ratio of 30 dB for a 4-user MWRC. For the same MWRC, up to 4.5 dB gain or 2.5 dB gain can be achieved for an uncoded or a channel-coded system, respectively, at the frame error probability of 10-2.

In this work we have developed a new method to construct general quantum stabilizer codes of variable block size by adopting the notion of a difference set. The proposed method comprises an efficient way to obtain the difference set, and from that set the construction of a quantum stabilizer code, which we refer to as a DSS (Difference Set Stabilizer) code. Our efficient method to generate the difference set requires no computer search, instead only a single parameter is required to generate the set.

In this paper, we focus on the realization of multiple interpretations (MI) in multi-way relay channels (MWRC) with fading, where multiple sources communicate with each other with the help of a relay. We first propose a novel nested convolutional lattice codes (NCLC) over the finite field, which can achieve the MI for each source in two time slots. Then we derive a theoretical upper bound for the codeword error rate (WER) of the NCLC. We further optimize our NCLC by developing a code design criterion which minimizes the derived WER. In simulations, we construct a specific NCLC based on our code design criterion. Simulation results show that our code can realize MI for each source in two time slots, and validate the derived upper bound in the high normalized signal-to-effective-noise ratio (SENRnorm) region.

We investigate the use of rate-compatible lowdensity parity-check (RC-LDPC) codes as part of a soft decodeand- forward (SDF) protocol over the half-duplex relay channel. We propose a new methodology to design the degree distribution of the RC-LDPC codes with a lower triangular parity-check matrix, enabling the additional parity bits to be linearly and systematically encoded at the relay. Our proposed methodology introduces the concept of a K-layer doping matrix to represent the structure of a lower triangular parity-check matrix. As a result of our methodology, the asymptotic performance of RC-LDPC codes can be analyzed and predicted using the multi-edge-type density evolution. Then, we derive the soft-re-encoding of the additional parity symbols at the relay using our designed RCLDPC codes. Moreover, we propose a novel method, which we refer to as soft fading, to compute the log-likelihood ratio (LLR) of the received signal at the destination for the SDF protocol. We demonstrate that our proposed soft fading method outperforms the best known method in the literature by up to 0.7 dB in terms of BER performance. Finally, we derive a new bound for the power multiplication factor at the relay, which limits the amount of soft-errors forwarded by the relay to the destination. The BER performance of our new RC-LDPC codes improves significantly once the power multiplication factor at the relay satisfies this bound.

In this paper, we study the performance of regularized channel inversion (RCI) precoding in large MISO broadcast channels with confidential messages (BCC). We obtain a deterministic approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas M and the number of users K grow to infinity in a fixed ratio β=K/M. We derive the optimal regularization parameter ξ and the optimal network load β that maximize the per-antenna secrecy sum-rate. We then propose a linear precoder based on RCI and power reduction (RCI-PR) that significantly increases the high-SNR secrecy sum-rate for 1<;β<;2. Our proposed precoder achieves a per-user secrecy rate which has the same high-SNR scaling factor as both the following upper bounds: (i) the rate of the optimum RCI precoder without secrecy requirements, and (ii) the secrecy capacity of a single-user system without interference. Furthermore, we obtain a deterministic approximation for the secrecy sum-rate achievable by RCI precoding in the presence of channel state information (CSI) error. We also analyze the performance of our proposed RCI-PR precoder with CSI error, and we determine how the error must scale with the SNR in order to maintain a given rate gap to the case with perfect CSI.

This paper investigates the throughput-reliability tradeoff (TRT) for dual-hop amplify-and-forward relay systems with one source, one destination, and multiple relays, and its relationship with the diversity-multiplexing tradeoff (DMT). The TRT was proposed in the context of MIMO block fading channels to reveal the interplay between the signal-to-noise ratio (SNR), rate R, and outage probability that are not accessible through the DMT. The contributions of this paper include the calculation of the TRT expressions for two classes of amplify-and-forward protocols: the slotted amplify-and-forward and the non-orthogonal amplify-and-forward. Based on the derived expressions, relationships between the SNR, rate and outage probability are explored. The relationship between the TRT and the DMT is investigated. One of the goals of the TRT is to predict the slope and offset of the outage vs. SNR set of curves parameterized by different rates. We verify the accuracy of the TRT predictions in the context of amplify-and-forward relays.

This paper addresses the design of irregular repeat accumulate (IRA) codes for coded physical-layer network coding (PNC) for the binary-input Gaussian two-way relay channel, assuming perfect synchronization and equal received power at the relay. The design is based on a nontrivial extension of EXIT-chart based design. Specifically, we analyze the components of the IRA-PNC scheme and propose an approach to model the soft information exchanged between these components. Then, we develop upper and lower bounds on the extrinsic information transfer functions to characterize the iterative process of computing the network-coded information. Based on that, we construct optimized IRA codes to minimize the computation error at the relay. The optimized IRA-PNC has considerable performance improvement over the existing regular RA coded PNC. For a rate 3/4 code, as an example, we observed improvements of 2.6 dB, and the optimized IRA-PNC scheme is only about 1.7 dB away from the capacity upper bound of the Gaussian two-way relay channel.

In underlay cognitive radio, a secondary user transmits in the transmission band of a primary user without serious degradation in the performance of the primary user. This paper proposes a method of underlay cognitive radio where the secondary pair listens to the primary ARQ feedback to glean information about the primary channel. The secondary transmitter may also probe the channel by transmitting a packet and listening to the primary ARQ, thus getting additional information about the relative strength of the cross channel and primary channel. The method is entitled Spectrum Harvesting with ARQ Retransmission and Probing (SHARP). The probing is done only infrequently to minimize its impact on the primary throughput. Two varieties of spectrum sharing, named conservative and aggressive SHARP, are introduced. Both methods avoid introducing any outage in the primary; their difference is that conservative SHARP leaves the primary operations altogether unaffected, while aggressive SHARP may occasionally force the primary to use two instead of one transmission cycle for a packet, in order to harvest a better throughput for the secondary. The performance of the proposed system is analyzed and it is shown that the secondary throughput can be significantly improved via the proposed approach, possibly with a small loss of the primary throughput during the transmission as well as probing period.

In this paper, we investigate lattice network codes (LNCs) constructed from Eisenstein integer based lattices. Quantization and encoding algorithms over Eisenstein integers are first introduced. Then, a union bound estimation (UBE) of the decoding error probability is derived when the shaping region of the LNC is a product of regular hexagons. Next, the Gaussian reduction algorithm is generalized to be applicable to complex lattices over Eisenstein integers such that an optimal coefficient vector can be found in the two-transmitter single-relay system. Based on the UBE, design criteria for optimal LNCs with minimum decoding error probability are formulated and applied to construct both Gaussian integer and Eisenstein integer based good LNCs from rate-1/2 feed-forward convolutional codes by Complex Construction A. The constructed codes provide up to 7.65 dB nominal coding gains over Rayleigh fading channels. Furthermore, we introduce the construction of LNCs from linear codes by Complex Construction B. The nominal coding gains and error performance of the LNCs thus constructed are explicitly analyzed. Examples show that the LNCs constructed by Complex Construction B provide a better tradeoff between code rate and nominal coding gain.

We investigate efficient communication over multiple-input multiple-output (MIMO) two-way relay channels (TWRCs), where two multi-antenna users exchange information via a multi-antenna relay. We propose a new MIMO physical-layer network coding (PNC) scheme that includes novel eigen-direction alignment (EDA) precoding. The proposed EDA precoding efficiently aligns the two-user's eigen-modes into the same set of orthogonal directions, and multiple independent PNC streams are implemented over the aligned eigen-modes. We derive an achievable rate-pair of the proposed scheme, for given EDA precoding parameters, over a MIMO TWRC. To maximize the achievable rate-region, we formulate a design criterion for the EDA precoding parameters, and present solutions to the formulation. Closed-form bounds on the sum-rates of the designed EDA-PNC schemes are derived. Numerical results show that there is only a small gap between the achievable rate of the proposed scheme and the capacity upper bound of the MIMO TWRC. It is shown that the proposed scheme can significantly outperforms existing schemes in the literature.

In this paper, we investigate the error probability and the diversity order of physical-layer network coding (PNC) in the two-way relay channel (TWRC), where two users communicate with the help of a relay. Each user has one antenna and the relay has two antennas. The system operates in a half-duplex mode with binary phase shift keying (BPSK) modulation. All the channels are Rayleigh faded. In particular, we consider a general asymmetric case, where two users have different average signal-to-noise ratio (SNR) to the relay. The upper and lower bounds on the instantaneous and average error probability of the uplink channel are derived based on the maximum-likelihood (ML) criterion. We also investigate the diversity order of the uplink channel. Numerical result demonstrates that the bounds are much tighter than other existing work, especially at low SNR, and the analytical result shows that the diversity is two for the asymmetric uplink channel.

In this paper, we study the performance of the Regularized Channel Inversion (RCI) precoder in a multi-user MIMO system with malicious users. We consider the general case when the number of users per transmit antenna β can take any value. We derive the optimal regularization parameter that maximizes the achievable secrecy sum-rate via large-system analysis, which we show to be accurate via simulations. We find that the secrecy requirements limit the number of users per transmit antenna that can be served with non-zero sum-rate. We show that for large signal-to-noise ratio, RCI can achieve secrecy without reducing the sum-rate if β; 1, then the secrecy requirements result in a poor sum-rate.

This paper deals with the problem of spectrum sensing in cognitive radio. We consider a stochastic system model where the Primary User (PU) transmits a periodic signal over fading channels. The effect of frequency offsets due to oscillator mismatch, and Doppler offset is studied. We show that for this case the Likelihood Ratio Test (LRT) cannot be evaluated pointwise. We present a novel approach to approximate the marginilisation of the frequency offset using a single point estimate. This is obtained via a low complexity Constrained Adaptive Notch Filter (CANF) to estimate the frequency offset. Performance is evaluated via numerical simulations and it is shown that the proposed spectrum sensing scheme can achieve the same performance as the “near-optimal” scheme, that is based on a bank of matched filters, using only a fraction of the complexity required.

We present a flexible class of stochastic models that are developed for cooperative wireless relay networks systems, in which the relay processing functionality is not known at the destination. The challenge is then to perform system identification in this wireless relay network. We first construct a statistical model based on a representation of the system using Gaussian Processes. We then develop a computationally efficient algorithm which is based on the Iterated Conditioning on the Modes estimation to undertake system identification for each relay in the presence of partial Channel State Information (CSI). We evaluate the identification performance for different non-linear relay functionalities.

In this paper, we investigate lattice network codes (LNCs) constructed from lattices over the ring of Eisenstein integers. Quantization and encoding algorithms over Eisenstein integers are first introduced. Then, a union bound estimation (UBE) of the decoding error probability is derived when the shaping region of the LNC is a product of regular hexagons. We show that the UBE is in the same form as the one for hypercube shaped LNCs, such as in the Gaussian integer case. We also demonstrate that in the Eisenstein integer case, the nominal coding gain and the shaping gain of a baseline LNC are, respectively, 0.625 dB and 0.167 dB, in contrast to the Gaussian integer case, where both gains are 0 dB. This is consistent with the simulation results comparing the performance of decoding error probability of baseline LNCs.

In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter α and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter αLSof the precoder using large-system analysis. We show that the RCI precoder with αLSoutperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed α. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing α and the power allocation vector. The jointly optimized precoder outperforms RCI with αLSand equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas.

This paper proposes a low complexity protocol that preserves full diversity in multiuser amplify-and-forward relay networks with NSantennas at the source, NRantennas at the relay, and NDantennas at each of the K destinations. In the proposed protocol, a two-fold diversity is guaranteed: 1) multi-antenna diversity via transmit antenna selection with maximal-ratio combining (TAS/MRC), and 2) multiuser diversity via opportunistic scheduling. Under perfect feedback with precise channel state information (CSI), we derive new exact and asymptotic symbol error rate (SER) expressions in closed-form for the general case of Nakagami-m fading. We prove that the full diversity order of NSNDKmX+ min{NSNRmY, NRNDKmZ} is guaranteed, where mX, mY, and mZdenote the fading parameters of the source-destination, source-relay, and relay-destination links, respectively. To examine the impact of delayed feedback, we next derive new exact and asymptotic SER expressions in closed-form. We prove that in the presence of delayed feedback, outdated CSI degrades the diversity order to NDmX+ min{NRmY, NDmZ}. In addition, based on our asymptotic expressions, we determine the optimal power allocation between the source and the relay such that the SER is minimized. We show that optimal power allocation offers superior performance over uniform power allocation; highlighting a pivotal design choice for maximizing network performance without investing additional resources.

We investigate a channel-coded physical-layer network coding (CPNC) scheme for binary-input Gaussian two-way relay channels. In this scheme, the codewords of the two users are transmitted simultaneously. The relay computes and forwards a network-coded (NC) codeword without complete decoding of the two users' individual messages. We propose a new punctured codebook method to explicitly find the distance spectrum of the CPNC scheme. Based on that, we derive an asymptotically tight performance bound for the error probability. Our analysis shows that, compared to the single-user scenario, the CPNC scheme exhibits the same minimum Euclidean distance but an increased multiplicity of error events with minimum distance. At a high SNR, this leads to an SNR penalty of at most ln2 (in linear scale), for long channel codes of various rates. Our analytical results match well with the simulated performance.

We propose cascaded transmit antenna selection with maximal-ratio combining (TAS/MRC) for use in multiuser relay networks (MRN) with NS, NR, and NDantennas at the source, the relay, and each of the K destinations, respectively. We consider opportunistic scheduling where the destination with the highest instantaneous end-to-end signal-to-noise ratio (SNR) is scheduled for transmission. In cascaded TAS/MRC, a single transmit antenna that maximizes the instantaneous received SNR in each hop is selected, and all the receive antennas are MRC combined. We derive new exact closed-form statistics of the end-to-end SNR, from which we derive the exact and the approximate symbol error rate (SER) for M-ary quadrature amplitude modulation (M-QAM) and M-ary phase-shift keying (M-PSK). New concise expressions are derived to characterize the diversity order and the array gain. We highlight that our proposed scheme attains the maximum diversity order of NR× min{NS, NDK}. Furthermore, we determine the optimal power assignment at the source and the relay that minimizes the SER.

In relay networks, we may not possess the ability to tune all system parameters in order to achieve the maximum achievable rate promised by theoretical analysis. This paper investigates the pragmatic issue of sub-optimal relay networks, where we can only optimize either on the time allocation, or on the power allocation. Our study concludes that optimizing on the power allocation, or on the time allocation, can achieve more than 95% of the relaying gain relative to the optimization of both time and power simultaneously. To produce the result, we derive the closed-form expression of the optimum power allocation for the source and relay that obtains the achievable rate of the half-duplex relay channel with fixed (equal) time allocation. We also derive the closed-form expression of the optimum time allocation between the source and relay transmission that obtains the achievable rate of the half-duplex relay channel with fixed power allocation. We demonstrate that for small SNR, where relaying is most advantageous, almost all relaying gain can be achieved by only optimizing the power allocation. Conversely, optimizing the time allocation alone is sufficient to achieve most of the relaying gain when the system's SNR is large. This result is important for pragmatic designs of emerging relay communication systems.

In this paper, we analyze the error performance of the mutual information based forwarding (MIF) scheme for a memoryless parallel relay network in additive white Gaussian noise (AWGN) channels. The analytical expression for soft noise variance is first derived. Note that in the literature, the exact soft noise variance could only be evaluated by Monte Carlo simulation due to the lack of its analytical form. The derived soft noise variance expression only relies on the transmit signal-to-noise ratio (SNR), without the need to have the knowledge of actual or estimated information bits. With the expression of the soft noise variance, we derive an approximate bit error rate (BER) expression for a parallel relay network employing MIF scheme. The derived soft noise variance and system BER expressions are shown to be in tight match with Monte Carlo simulation results.

We propose transmit antenna selection with maximal-ratio combining (TAS/MRC) for use in multiple-input-multiple-output (MIMO) multiuser relay networks (MRN). The network under consideration is equipped with NS, NR, and NDantennas at the source, the relay, and each of the K destinations, respectively. For this network, the destination with the highest instantaneous end-to-end signal-to-noise ratio (SNR) is scheduled for transmission. In each hop, a single transmit antenna that maximizes the post-processing SNR is selected, while all the receive antennas are MRC combined. We first derive new closed-form expressions for the outage probability and the symbol error rate (SER) for amplify-and-forward (AaF) relaying. Next, we present compact and easy-to-compute expressions for the diversity order and the array gain to provide practical insights into the network behavior. We highlight the fact that our proposed scheme attains the maximum diversity order of NR× min{NS,NDK}.

This paper studies the problem of determining the optimum degree distribution for distributed LDPC codes in two-way relay channels. Based on the framework of multi-edge type (MET) LDPC codes, we propose a methodology to asymptotically optimize the code's ensemble when different segments within the distributed codeword have been transmitted through different channels and experience different SNRs. An average noise threshold is formulated to compute the convergence threshold of the distributed LDPC codes under density evolution and acts as the performance gap between the optimized distributed codes and the theoretical limit. We demonstrate that the optimized distributed LDPC code using our proposed method performs asymptotically within a fraction of a dB away from the theoretical limit.

We study the design of network codes for M-source, N-relay wireless networks over slow fading channels. Specifically, vector-wise binary field network coding (BFNC) schemes are proposed. In the construction of our BFNC schemes, we utilize a diversity achieving criterion which can be expressed in terms of the linear independence of quasi-cyclic matrices. Our codes can be implemented with low-complexity encoders at the relays as only binary operations are used. Meanwhile at the destination, for small code lengths, ML decoder can be applied. For large code lengths, we propose a modified BP decoder with low decoding complexity. From analysis and simulations, we show that our proposed BFNC schemes can achieve full diversity for the ML decoder, as well as full diversity for the modified BP decoder we propose for large block lengths. Our simulations also show that our proposed BFNC schemes achieve a higher coding gain relative to previous network coding schemes.

We investigate a slotted incremental decode-and-forward (SIDF) protocol for cooperative communication in wireless networks. In the proposed scheme, each user allocates only a portion of its time-resource to incrementally relay its partner's information, thus to improve the bandwidth efficiency of the cooperative transmission. To analyze the performance, we derive the diversity-multiplexing tradeoff (DMT) of the SIDF protocol and show the optimal time-allocation. It is shown that the SIDF achieves the DMT upper bound for low and high multiplexing gain regions whereas it is slightly away from the upper bound at a medium multiplexing gain region. The superiority of the DMT of SIDF protocol is translated into significant improved outage probability, as compared with other well-known user-cooperation protocols.

This paper proposes a new soft decode-and-forward (SDF) protocol using LDPC codes in the half-duplex relay channels. In order for the encoding of the additional parity-check symbols at the relay to be linear and systematic, we introduce a structured rate-compatible (RC) LDPC code. We then develop the soft-decoding and soft-re-encoding algorithms for the proposed RC-LDPC code, which allows the relay to forward soft messages to the destination when the relay fails to decode the source's message. Furthermore, we propose a new method, which we refer to as soft fading, to compute the log-likelihood ratio (LLR) of the received signal at the destination for the SDF protocol. We show that our proposed method performs better when compared to a previous reported method in literature.

We investigate joint channel and physical layer network coding (CPNC) for Gaussian two-way relay channels. The two users' messages are encoded using the same binary linear code and are transmitted simultaneously with equal power. At the relay node, the network-coded message is recovered directly from the received signal sequence, and is then broadcast to the users. We propose a new methodology to explicitly find the distance spectrum of the coding scheme. Based on that, we analyze the error probability at the relay and derive an asymptotically tight performance bound (for high SNRs). We show that, with a general binary linear code, the CPNC scheme is subject to an asymptotic SNR loss of approximately ln 2 relative to the single-user case, regardless of the coding rate. Numerical results show that our analysis matches very well with the performance of the CPNC scheme.

We propose a new joint channel coding and physical layer network coding (CPNC) scheme for multiple-input multiple-output (MIMO) two-way relay channels (TWRCs). At the heart of the scheme lies a key technique referred to as eigen-direction alignment (EDA) precoding. This technique efficiently creates multiple aligned parallel channels which facilitates the deployment of multi-stream CPNC. Our analysis shows that the achievable rate of the scheme can approach the capacity upper bound in the median to high signal-to-noise (SNR) region when nT> nR, where nTand nRdenote the number of antennas of each user and that of relay, respectively. The gap to the capacity upper bound diminishes as nT/nRincreases. Numerical results demonstrate that the proposed scheme significantly outperform other well-known schemes in the literature.

We present a new approach for joint channel tracking and parameter estimation in cooperative wireless relay networks, based on a particle Markov chain Monte Carlo (PMCMC) method. We consider a system with multiple relay nodes operating under an amplify and forward relay function. In particular, it first involves developing a non-liner Bayesian state space model, then estimating the associated high dimensional posterior using an adaptive Markov chain Monte Carlo (MCMC) sampler relying on a proposal built using a Rao-Blackwellised Sequential Monte Carlo (SMC) filter. Simulation results demonstrate the effectiveness of the proposed algorithm, requiring only a fraction of the computational complexity of standard MCMC approaches.

We propose opportunistic scheduling with cooperative selection diversity (CSD) in wireless multiuser relay networks (MRN). In this policy, the destination with the highest instantaneous end-to-end signal-to-noise ratio (SNR), either directly from the source or indirectly through the relay, is scheduled for transmission. For the practical case of unbalanced Nakagami-m fading channels, we derive new exact closed-form expressions for the outage probability and the symbol error rate (SER) for amplify-and-forward (AaF) relaying. Next, we quantify the asymptotic network behavior in the low SER regime by presenting concise expressions for the diversity order and the array gain. We demonstrate that our two-step policy achieves the maximum diversity order. We further prove that both the diversity order and the array gain are jointly influenced by the direct link and the weaker hop of the relay link.

Distributed transmit antenna selection with maximal-ratio combining (TAS/MRC) is proposed for use in multiple-input multiple-output (MIMO) multiuser relay networks (MRN), where NS, NR, and NDantennas are equipped at the source, the relay, and each of the K destinations, respectively. For such networks, the destination with the highest instantaneous end-to-end signal-to-noise ratio (SNR) is scheduled for transmission. In each hop, a single transmit antenna that maximizes the post-processing SNR is selected, while all the receive antennas are MRC combined. New exact closed-form expressions are derived for the cumulative distribution function (CDF), the probability density function (PDF), and the moment generating function (MGF) of the highest instantaneous end-to-end SNR. Based on these, we determine the symbol error rate with M-ary phase-shift keying. Our derived results apply to general operating scenarios with arbitrary number of antennas, arbitrary number of destinations, and distinct average SNRs.

We investigate the performance of analog network coding (ANC) for multi-hop networks in this paper. With the amplify-and-forward (AF) protocol, relays broadcast the sum of two colliding signals to neighboring nodes, while the source node can subtract its own signal from the colliding signal to obtain the received information. We first give the transmission scheme expressions for the n-node m-frame two-way multi-hop network. For this scheme, we derive the end-to-end signal-to-noise ratio (SNR) expression. Without loss of generality, the closed-form expression of the outage probability for the generalized multi-hop network is evaluated. Numerical results demonstrate that the outage performance with ANC is better than the traditional scheme without ANC.

We consider the design of bilayer-expurgated low density parity-check (BE-LDPC) codes as part of a decode and-forward protocol for use over the full-duplex relay channel. A new ensemble of codes, termed multi-edge-type bilayer expurgated LDPC (MET-BE-LDPC) codes, is introduced where the BE-LDPC code design problem is transformed into the problem of optimizing the multinomials of a multi-edge-type LDPC code. We propose two design strategies for optimizing MET-BE-LDPC codes; the bilayer approach is preferred when the difference in SNR between the source-to-relay and the source to-destination channels is small, while the bilayer approach with intermediate rates is preferred when this difference is large. In both proposed design strategies multi-edge-type density evolution is used for code optimization. The resulting MET-BE-LDPC codes exhibit improved threshold and bit-error-rate performance as compared to previously reported bilayer LDPC codes.

An opportunistic spectrum sharing scheme for a pair of secondary users co-existing with a pair of primary users is proposed in this letter. The primary user is considered to exploit the limited feedback of channel quality information (CQI) to allocate its transmission power and rate. By overhearing the limited feedback of the primary CQI, the secondary user accesses the channel with proper transmission power and rate while causing interference to the primary user. Under the primary rate loss constraint, the optimal transmit power and transmission rate for the secondary is obtained to maximize the secondary user throughput. Numerical results show that, with only 3 or 4 bits of feedback, the effective throughput of the secondary user is comparable to the case when both primary and secondary transmitters have the perfect CQI of the primary link.

This letter advocates the performance of a multiuser relay network (MRN) equipped with a single amplify-and-forward (AaF) relay over Rayleigh fading environments. We derive new expressions for the cumulative distribution function (CDF) of the highest instantaneous end-to-end signal-to-noise ratio (SNR) taking into consideration the two cases of fixed gain relays and variable gain relays. Relying on these statistical results, we derive new expressions for the outage probability and symbol error rate (SER), both of which are obtained in exact closed form. Furthermore, we derive simple asymptotic outage probability and SER. Our asymptotic results confirm that opportunistic scheduling has no impact on the diversity order. We further prove that the array gain is what determines the SNR advantage of opportunistic scheduling over the single user scenario.

We investigate LDPC code design for a multi-source single-relay system, with uniform phase-fading Gaussian channels. We specifically consider the asymmetric channels for multiple sources, where the channel condition for each source in the system is different. We focus on LDPC code design when network coding (NC) at the relay is utilized. For the asymmetric sources, we firstly introduce a binary field rate splitting theorem which is used to discover an appropriate NC scheme at the relay. This NC scheme is then used to determine the achievable rates of each source and the whole system. These steps assist us in the development of the main contribution of our work, namely, network coded multi-edge type LDPC (NCMET-LDPC) code design. Extrinsic mutual information transfer (EXIT) chart analysis is utilized to optimize the code profiles. Our results demonstrate two key points. (1) From the whole system point of view, our NCMET-LDPC codes achieve better error performance than that of LDPC codes designed for the system without NC. (2) As a consequence of the binary field rate-splitting theorem, our NCMET-LDPC codes also guarantee better error performance of each asymmetric source. The improvement in error performance is typically about 0.3 dB relative to a system without NC.

In this paper, we consider a pair of cognitive radio (CR) users co-existing with a pair of ARQ- based primary users (PU). The secondary user (SU) overhears the ACK/NACK feedback sent from the receiver of the primary system, and then decides to access the spectrum or not. An opportunistic sharing scheme, referred to as Spectrum sHaring with ARQ Retransmission and Probing timeslots (SHARP), is proposed to exploit spectrum opportunities based on the ACK/NACK of PU only. Numerical results show that the analytical outcomes perfectly match those from the Monte Carlo simulation. Moreover, the goodput of SU increases dramatically while the outage probability of the primary remains small.

In this contribution, we propose an antenna selection scheme in dual-hop amplify-and-forward (AaF) multiple-input multiple-output (MIMO) relaying in Nakagami-m fading channels. In each hop, the transmit and receive antenna pair that maximizes the instantaneous signal-to-noise ratio (SNR) at the receiver is selected for transmission. We derive new closed-form expressions for the exact outage probability and the exact symbol error rate (SER), relying on the cumulative distribution function (CDF) of the instantaneous end-to-end SNR. Furthermore, we derive simple closed-form expressions for the asymptotic outage probability and the asymptotic SER, revealing the diversity order of the proposed scheme. Specifically, the diversity order is equal to the minimum of two parameters: first, the product of the number of source and relay antennas and the first hop fading parameter, and second, the product of the number of relay and destination antennas and the second hop fading parameter. Our derived results apply to general operating scenarios with distinct Nakagami-m fading parameters and average SNRs in each hop.

This paper analyzes the performance of wireless multiuser relay networks (MRN) in unbalanced Nakagami-m fading channels. For such networks, we consider a single channel state information (CSI)-based amplify-and-forward (AaF) relay. We derive a new exact expression for the symbol error rate (SER), which is in closed-form and applies to a wide variety of modulations. Subsequently we present a simplified asymptotic expression for the SER in the high signal-to-noise ratio (SNR) regime to identify key performance metrics such as the diversity order and array gain. Our asymptotic result explicitly reveals the direct relationship between the diversity order and both the number of destinations and the per-hop fading parameters. Moreover, we highlight the effect of the number of destinations on the optimal relay location aiming at minimizing the SER. The validity of our analysis is substantiated by numerical results.

Distributed turbo coding (DTC) has been shown to be able to approach the information-theoretic capacity of wireless relay networks. However, decoding errors at the relay can lead to severe error propagation and hinder the achievement of such capacity. In order to increase the decoding success rate at the relay, thereby enhancing the DTC system performance, we propose a novel DTC scheme, referred to as nested DTC, for a two-hop triangular relay network. In this scheme, we use a turbo code instead of a single convolutional code as does in the conventional DTC. At the source node, the four output streams of the two component convolutional encoders are converted into two streams by adding in modulo-2 addition. An extended 2-D log MAP decoding is used at both relay and destination to retrieve the information symbols. Numerical results show that the error performance of the proposed nested DTC scheme significantly outperforms the existing DTC schemes and performs within only 1 dB of the DTC technique with automatic repeat request (ARQ). The performance superiority of the nested DTC scheme is attributed to the retention of the full information due to the nested encoding structure.

This paper advocates the performance of wireless multiuser relay networks (MRNs) equipped with a single amplify-and-forward (AaF) relay. For such networks, we focus on opportunistic scheduling, in which the destination with the highest instantaneous end-to-end signal-to-noise ratio (SNR) is scheduled for transmission. The scheduled destination can receive either directly from the source or indirectly through the relay, based on cooperative selection diversity (CSD). Assuming Rayleigh fading channels, we first derive a new expression for the cumulative distribution function (CDF) of the received instantaneous end-to-end SNR. Then, relying on this statistical result, we present new exact expressions for the outage probability and symbol error rate (SER), both of which are obtained in closed-form. Furthermore, we derive simple yet valuable closed-form asymptotic expressions for the outage probability and SER in the high SNR regime. Our results explicitly reveal the impacts of CSD and opportunistic scheduling on the diversity order and array gain. Numerical results are presented to validate the analysis.

This paper considers the performance of the multiuser multi-antenna downlink where NTbase station antennas transmit to K non-collocated, single antenna users over correlated channels. A practical and near-capacity multi-user technique called vector perturbation precoding (VPP) is considered. For these scenarios, we derive a lower bound to the average energy of the precoded vector. We use this to approximate the effective noise gain at the receivers due to the presence of transmit side correlation. Furthermore, we obtain simple approximations for the noise gain for constant and exponential transmit correlation models. Simulation results show that our approximations predict the performance loss due to correlation remarkably well regardless of the correlation parameter.

This paper considers the problem of Gaussian symbols detection in MIMO systems in the presence of channel estimation errors. Under this framework we develop a computationally efficient approximations of the MAP detector. The new detectors are based on a relaxation of the discrete nature of the digital constellation and on the channel estimation error statistics. This leads to a non-convex program that is solved efficiently via a hidden convexity minimization approach. Additionally, we show that using a Bayesian EM approach, comparable BER performance to that of the MAP detector can be achieved. Next we extend the detection scheme to the case where the noise variance is unknown. We present a modified Bayesian EM approach with annealed Gibbs sampling to perform joint noise variance estimation and symbols detection. Simulation results in a random MIMO system show that the proposed algorithm outperforms the linear MMSE receiver in terms of BER.

We consider an uplink communication in a wireless network where each source-user is assisted by M half-duplex relays. The source users and relays are equipped with single-antenna whereas the destination has N antennas. We show that the degree of freedom (d.o.f.) of this network is limited by the links from the source to the collection of relays and destination. This phenomenon is referred to as a "d.o.f. bottleneck problem" which compromise the cooperative multiplexing gain (CMG) and the achievable rate of the system. To improve the CMG, we propose an auxiliary frequency bands (AFBs)-based approach to tackle the d.o.f. bottleneck problem. Information theoretic analysis and numerical results show that the proposed scheme can achieve a CMG of min(M+1,N) when the number of AFBs is sufficiently large. As compared with conventional cooperative communications, the improved multiplexing gain of the proposed scheme is translated into tremendously increased achievable rate at a relatively high SNR.

This paper considers the multiuser multiple-input multiple-output (MIMO) broadcast channel. We consider the case where the multiple transmit antennas are used to deliver independent data streams to multiple users via vector perturbation. We derive expressions for the sum rate in terms of the average energy of the precoded vector, and use this to derive a high signal-to-noise ratio (SNR) closed-form upper bound, which we show to be tight via simulation. We also propose a modification to vector perturbation where different rates can be allocated to different users. We conclude that for vector perturbation precoding most of the sum rate gains can be achieved by reducing the rate allocation problem to the user selection problem. We then propose a low-complexity user selection algorithm that attempts to maximize the high-SNR sum rate upper bound. Simulations show that the algorithm outperforms other user selection algorithms of similar complexity.

In this paper, we propose a new superposition modulation-based cooperative scheme and investigate receivers with iterative detection and decoding (IDD), where we focus on an interference cancellation (IC) detector. For quasi-static fading environment, we analyze the bit-error-probability (BEP) of the proposed scheme and analytically show the optimal power allocation in the superposition modulation. It is demonstrated that the proposed scheme performs about 2-3.5 dB better than the system previously proposed in the literature and simulation results are shown to be very close to the analytical results. In addition, the receiver with IC detectors achieves the MAP detection performance in the scheme.

In this paper, we develop a rate allocation scheme for interleave-division multiple-access (IDMA) systems with iterative decoding. We use a fully-analytical approach to predict the performance of the scheme and propose a modified linear programming method to design the rate profile. Numerical results show that with just repetition coding and rate allocation, the performance of the scheme is only about 5 dB away from the capacity for a wide range of SNR, provided that the number of users is sufficiently large. Compared with power allocation schemes for IDMA, the proposed rate allocation scheme achieves a similar performance at a moderate spectral efficiency and the requirement of sophisticated power amplifiers can be relaxed.

Orthogonal space-time block codes (OSTBC) are attractive in that they can achieve full diversity and linear complexity of maximum likelihood (ML) decoding. However, the OSTBC have a low symbol rate due to the limitation of the orthogonality of the code structure. Most of the high-rate STBC achieve full diversity based on ML decoding at the receiver that is computationally expensive. In order to achieve full diversity with linear receivers, recently Liu-Zhang-Wong and Shang-Xia introduced new STBC. In this paper, we propose a simple design of STBC which have a high rate and achieve full diversity with linear receivers. The proposed STBC are constructed by embedding Alamouti codes into a Toeplitz matrix. Simulation results show that in comparison with some existing codes for a given codeword length the proposed STBC can give a better bit error rate (BER) performance while having a high rate.

This paper considers the multiuser multiple-input multiple-output (MIMO) Rayleigh fading broadcast channel. We consider the case where the multiple transmit antennas are used to deliver independent data streams to multiple users via a multi-user technique known as vector perturbation. We derive expressions for the capacity in terms of the average energy of the preceded vector, and use this to derive a closed-form high-SNR upper bound, which we show to be tight via simulation. We then turn to the practical issue of user selection. We propose a low-complexity user selection algorithm that attempts to maximize the high-SNR sum rate upper bound. Simulations show that the algorithm outperforms other user selection algorithms of similar complexity.

Recently several STBC designs were proposed for MIMO systems with linear receivers. In this paper, we propose a new design of high-rate STBC with linear receivers. Compared to the overlapped Alamouti code (OAC) recently proposed by Shang and Xia, the new STBC has a higher symbol rate without guarantee of achieving full diversity. To achieve the same rate, the new STBC has only a half of the block length (code delay) of the OAC. Simulation results show that in comparison with some existing codes for a given rate the proposed STBC can give a better outage probability performance.

This paper presents a new bilayer-expurgated LDPC (BE-LDPC) code design for decode-and-forward relay system. The code design is based on the multi-edge type LDPC (MET-LDPC) code structure. The lower and upper graphs' edges of the BE-LDPC codes are represented via different edge types. We call the codes as multi-edge type bilayer-expurgated LDPC (MET-BE-LDPC) codes. We derive the relationship between the lower and the overall graphs of the MET-BE-LDPC codes using the multi-edge type multinomials. This relationship acts as a constraint in optimizing the MET-BE-LDPC codes under the density evolution. We apply the differential evolution algorithm to search for the best MET-BE-LDPC codes. Applying the proposed methodology, two MET-BE-LDPC codes are designed. The codes perform asymptotically within 0.08474-0.5087 dB from the theoretical limits. Finally, we demonstrate that the proposed methodology can be used to design the BE-LDPC codes at a larger source-to-relay and source-to-destination SNR differences.

We propose a new superposition modulation-based cooperative diversity scheme and investigate receivers with iterative detection and decoding for the scheme, where we focus on a interference cancellation (IC) detector. We analyze the bit-error-probability (BEP) of the scheme and find the optimal power allocation. Simulation results are shown to be very close to the analytical results for a wide range of signal-to-noise ratios (SNRs). It is also demonstrated that the proposed scheme is about 2-3.5 dB better than the system previously proposed in the literature.

In this paper, we consider a general two-hop relay network and propose a distributed turbo coding with selective relaying (DTC-SR) scheme to improve the performance of relayed transmission. In the proposed scheme, each relay adaptively selects an amplify and forward (AAF) or a decode and forward (DAF) protocol based on whether it can decode correctly or not. Among all the relays, a single relay, which has the maximum destination SNR, is selected for transmission. If the selected relay uses the DAF protocol, it decodes the received signals, interleaves, re-encodes and forwards them to the destination. At the destination, the signals directly transmitted from the source and that from the selected relay form a distributed turbo code (DTC). If the selected relay uses the AAF protocol, it just simply amplifies the received signal. The destination then combines the signals transmitted from the source and the relay. Simulation results show that the DTC-SR can take advantages of both distributed turbo coding and relay selection, providing not only a considerable SNR gain contributed from the relay selection, but also a coding gain contributed from the distributed turbo coding. And these gains increase as the number of relay increases.

In this paper, we consider the performance of downlink multiuser relay networks (MRN) equipped with a single amplify-and-forward (AaF) relay. We present exact analysis in closed-form for the outage probability of MRN with channel state information (CSI)-based gain relaying in Nakagami-m fading channels. In doing so, we derived a new expression for the cumulative distribution function (CDF) of the highest end-to-end signal-to-noise ratio (SNR) associated with the strongest destination terminal. We demonstrate the impact of the fading severity m and the number of destination terminals on the system performance in unbalanced fading conditions. Numerical results substantiate the validity of our analysis.

The end-to-end performance of multi-branch dual-hop wireless communication systems with non-regenerative relays and equal gain combiner (EGC) at the destination over independent Nakagami-m fading channels is studied. We present new closed form expressions for probability distribution function (PDF) and cumulative distribution function (CDF) of end-to-end signal to noise ratio (SNR) per branch in terms of Meijer's G function. From these results, analytical formulae for the moments of the output SNR, the average overall SNR, the amount of fading and the spectral efficiency are also obtained in closed form. Instead of using moments based approach to analyze the asymptotic error performance of the system, we employ the characteristic function (CHF) method to calculate the average bit error probability (ABEP) and the outage probability for several coherent and non-coherent modulation schemes. The accuracy of the analytical formulae is verified by various numerical results and simulations.

In this paper, two improved distributed turbo coded (DTC) schemes, namely, superposition DTC scheme and punctured DTC scheme, are proposed for a two-hop relay system using decode-and-forward protocol. The performance of a communication system using relay mainly depends on the successful decoding of source information at the relay. The proposed techniques increase the decoding success rate at the relay thanks to the unique encoding procedures used at the source to relay link. Here, the input information symbols are first encoded using two rate 1/2 parallel concatenated recursive systematic convolutional (RSC) encoders, and the two parity streams of the two RSC encoders are converted into one stream by superposition and puncturing in the proposed superposition DTC scheme and punctured DTC scheme, respectively. Simulation results show that the error performance of the proposed superposition DTC scheme and the punctured DTC scheme are superior to the conventional DTC scheme by around 1.2 and 0.8 dB respectively.

This paper considers the problem of channel estimation for OFDM systems, where the number of channel taps and their power delay profile (PDP) are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the PDP. In order to sample from the resulting posterior distribution we develop a novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithm. This is done using a Stochastic Approximation (SA) approach to develop an adaptively learning algorithm to improve mixing rates of the basic Birth-Death (B-D) Markov chain for the between model subspaces. Using simulations we assess its performance in terms of channel order estimation and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP are known.

In this letter, we develop a rate allocation scheme for interleave-division multiple-access (IDMA) systems with iterative decoding. We use a fully-analytical approach to predict the performance of the scheme. Then, we propose a modified linear programming method to find the best rate profile for the scheme. Numerical results show that with just repetition coding and optimal rate allocation, the performance of the scheme is only about 5 dB away from the capacity for a wide range of SNR, provided that the number of users is sufficiently large. Compared with power allocation schemes for IDMA, the proposed rate allocation scheme achieves a similar performance at a moderate spectral efficiency and the requirement of sophisticated power amplifiers can be relaxed.

In this paper, we propose a novel distributed spacetime trellis code (DSTTC) structure, and analyze its error performance in both slow and quasi-slow Rayleigh fading channels. The protocol adopted is decode-and-forward (DAF) with a single relay between the source and destination. Both scenarios with perfect and imperfect decoding at the relay are investigated. For imperfect decoding at the relay node, we consider an equivalent one-hop link model for the source-relay-destination path, and use it to modify the maximum likelihood detection metric by taking into account the equivalent signal-to-noise ratio (SNR) of the link model. The upper bounds of pairwise error probability (PEP) are derived for slow and quasi-slow Rayleigh fading channels, and the DSTTC design criteria are formulated accordingly. Based on the proposed design criteria, new DSTTCs are constructed by computer search. Simulation results demonstrate the superiority of the designed codes.

This paper considers the problem of Bayesian estimation of a Gaussian vector in a linear model with random Gaussian uncertainty in the mixing matrix. The maximum a-posteriori estimator is derived for this model using the Bayesian Expectation-Maximization. It is demonstrated that the solution forms an elegant and simple iteration which can be easily implemented. Finally, the estimator developed is considered in the context of near-Gaussian-digitally modulated signals under channel uncertainty, where it is shown that the MAP estimator outperforms the standard linear MMSE estimator in terms of mean square error (MSE) and bit error rate (BER).

In this paper, we consider a multiple-relay network. We propose differential modulation at each communication node and selective combining method at the receiver. As both differential modulation and the selective combining do not require the full channel state information, the system design is greatly simplified without compromising much of the system performance. The average BER for this multiple-relay system with estimation-and- forward signaling protocol at the relay nodes is derived. In the analysis, we take into account the effect of imperfect estimation at the relay nodes, by replacing each of the source-relay-destination links with an equivalent relay-destination link. From the performance analysis, we conclude that this multiple-relay system with selective combiner can achieve full diversity. The analysis is also verified by simulation results.

This paper investigates the sum-rate performance of a multiuser multi-antenna downlink system. Assuming limited channel state information at the base station and a large number of single antenna users in a cell, we investigate a semi-orthogonal user selection scheme with a total feedback rate constraint. The sum-rate performance of the proposed scheme is analyzed under zero-forcing linear precoder at base station, and a lower bound to the sum-rate of broadcast channels with quantized channel direction information and channel quality information is derived. This lower bound is used to evaluate the performance of broadcast channels with finite-rate feedback of channel state information and semi-orthogonal user selection. Numerical examples show that our analytical results agree with Monte Carlo simulations.

In this paper, we propose a new superposition- modulation based cooperative multiple-access scheme. In this scheme, each user only superimpose the information from the previous user, where the full diversity is achieved by cooperative spatial-interleaving. Focusing on interference cancellation with minimum mean square error filtering at the destination, we derive a lower bound on the frame error probability of the scheme. We show that the bound can be used to predict the frame error rate of the scheme. Moreover, we show that full diversity gain is achieved and the detection complexity is reduced as compared to the previous scheme.

This paper presents an improved LDPC code design for decode-and-forward relay system. Our design is based on the recent work of bilayer LDPC code structures.We introduce an irregular check node degree distribution for the lower and hyper (overall) bilayer LDPC code Tanner graphs. We derive the exact relationship between the lower and hyper graphs in terms of degree distributions (node perspective). This relationship acts as constraint to perform the density evolution. We apply the differential evolution algorithm to search for the best degree distribution for both bilayer-lengthened and bilayer-expurgated LDPC codes. We show that the performance gap of the codes is within 0.07595dB and 0.28407dB from the theoretical limits.

Distributed turbo coding (DTC) has been shown to be an effective coding scheme to approach the capacity of a wireless relay network. However, most of existing DTC schemes only consider a relay network with single relay node and assume that relay can perform an error free decoding, which we refer to as a perfect DTC scheme. In this paper, we consider a general 2-hop relay network with an arbitrary number of relays and design the DTC for such a network when taking into account imperfect decoding at each relay. We propose a generalized distributed turbo coding (GDTC) scheme with hybrid relaying protocol for such relay networks. In each transmission, based on whether relays can decode correctly or not, each relay is included into one of two relay groups, referred to as a decode and forward (DAF) relay group and an amplify and forward (AAF) relay group. Each relay in the DAF relay group decodes the received signals from the source, interleaves, re-encodes and forwards it to the destination, while each relay in the AAF relay group amplifies the received signals and forwards it to the destination. At the destination, all signals transmitted from the relays in the DAF relay group are combined into one signal and that in the AAF relay group are combined into another signal. These two signals form a generalized DTC codeword. Theoretical analysis and simulation results show that the proposed GDTC scheme benefits from a significant coding gain contributed from the DTC relay group compared to the distributed coding with pure AAF relaying and simultaneously overcome the detrimental effects of error propagation due to the imperfect decoding at relays in the conventional DTC schemes. It also approaches the perfect DTC as the signal to noise ratio (SNR) increases.

We consider the Bayesian inference of a random Gaussian vector in a linear model with a random Gaussian matrix. We review two approaches to finding the MAP estimator for this model. We propose improved versions of these approaches with reduced complexity. Next we analyze their complexity and convergence properties. Then we derive the MAP estimator in the setting in which the variance of the noise is unknown. Simulation results presented compare the performance in terms of estimation error of the approaches.

In this paper schemes for feedback reduction in MIMO broadcast channels are considered for a multi-user system. We consider a case where the number of single antenna users K >> M where M is the size of antenna array at the base station (BS). We consider zero-forcing beamforming system with semi-orthogonal user selection algorithm in which channel state information is splitted into channel quality information (CQI) and channel direction information (CDI). In such a user-dense multi-user system feedback channel is inundated if all the users are feedbacking even in the case where only a limited feedback is employed. In order to reduce the feedback load we propose threshold based schemes in which threshold is applied on CQI or CDI. We show that by employing proposed feedback reduction schemes feedback load can be significantly reduced without sacrificing much of the sum-capacity of the system.

This work presents new CIR estimators for OFDM systems over frequency selective channels, where the length of the CIR is unknown a-priori. We derive the MMSE estimator for this model using two different criteria, namely, Bayesian model averaging and Bayesian model order selection. Estimation of the CIR length enables adapting the CP length to the changing propagation environment, resulting in increased throughput due to shorter CP. Simulation results under different channel conditions demonstrate the robustness of the estimators.

In this letter, we propose a new multi-stage LLR combining (MLC) algorithm in an iterative receiver for MIMOBICM systems. This algorithm combines the soft information from different receive antennas in a multi-stage fashion, where the combining factors are derived based on the joint likelihood function of bivariate Gaussian random variables. The variance transfer (VT) function of the proposed scheme is derived for performance analysis. For slow fading channels, we show that the proposed MLC algorithm can achieve almost the same performance as the linear minimum mean square error (LMMSE) filtering approach, whereas the computation-demanding matrix inverse for LMMSE can be avoided.

In this paper, we propose an improved relay selection scheme based on a hybrid relaying protocol (RS-HRP). In the proposed scheme, all the relays are included into two groups, referred to as an amplify and forward (AAF) and a decode and forward (DAF) relay groups. The relays which decode successfully are included in the DAF group and the rest of relays, which fail to decode correctly, are included in the AAF group. The best relay, which maximizes the destination SNR, will be selected from all relays in both AAF and DAF relay groups. If it is selected from the AAF group, it will amplify the received signal while if it is selected from the DAF group, it will decode the received signals and re-encode. Results show that the proposed relay selection scheme significantly outperforms the conventional AAF selection scheme and this performance gain considerably grows as the number of relays increases. It also approaches the perfect DAF relay selection as the SNR increases.

In this paper, we propose a new linear detector for the iterative receiver for MIMO-BICM systems. Instead of performing vector matched filtering after the interference cancellation, the proposed detector successively combines the soft information from different receive antennas, where the combining factors are computed based on the joint likelihood function of multivariate Gaussian random variables. Numerical results show that significant performance improvement can be achieved and the additional complexity is negligible.

In this paper we investigate the problem of channel tracking and detection for MIMO-OFDM systems over fast varying channels obeying a Gauss-Markov model. We consider time domain tracking of the channel matrix taps with Kalman filter, whereas symbols detection is carried out by a zero-forcing (ZF) soft detector. A key assumption of the theory of Kalman filter is that the state-space model is perfectly known, while communication systems make use of the detected symbols as an input to the Kaiman filter in order to form a suitable state-space model. This gives rise to error propagation due to misdetected symbols (model mismatch) and is usually solved by using frequently inserted pilot symbols, resulting in a reduced spectral efficiency. To overcome this problem, we suggest a novel approach to mitigate the error propagation due to misdetections without using frequent pilot symbols. In particular, we consider the reliability of the detections based on the soft detector and use only those outputs that have robust reliability to track the channel matrix taps, minimizing the effect of Kalman filter mismodeling. This method can significantly reduce the error propagation effect, leading to an improved bit error probability.

In this paper, we present a modified version of the maximum shortening signal to noise ratio (MSSNR) algorithm for channel shortening in a time hopping (TH) pulse position modulated (PPM) ultra wideband (UWB) communication system. The proposed algorithm introduces two additional UWB channel related parameters in the optimization problem along with the conventional energy criterion. This modification significantly improve the performance of the conventional MSSNR algorithm and enables it to handle the extreme nature of channel shortening needed in UWB systems. We also derive a lower bound for bit error rate (BER) as a comparison bench mark. The proposed algorithm does not need any training or channel estimation and outperforms the conventional MSSNR algorithm in terms of different comparative parameters.

In this paper we analyze the performance of a general user cooperative space-time coded system in wireless network. The upper bound of the pairwise error probability (PEP) is derived, on slow, quasi-slow and fast fading channels. From the upper bounds, we show that cooperative space-time coded system in wireless network can achieve full cooperation diversity gain and coding gain. Based on the performance analysis, we formulate a generalized space-time code design criteria to construct better codes for user cooperation systems.

Most of existing distributed coding schemes assume a perfect detection at relays. In this paper, we propose a distributed space time trellis coding (DSTTC) scheme by taking into account the imperfect detections at relays. We propose a detection metric to address the effect of decoding errors. In particular, we consider an equivalent one-hop link model for the source-relay-destination path and use it to modify the maximum likelihood detection metric by taking into account the equivalent signal to noise ratio (SNR) of the link model. The upper bound of the pairwise error probability (PEP) is then derived for both slow and quasi-slow Rayleigh fading channels, and the design criteria for the distributed space-time trellis codes are formulated accordingly. Based on the proposed design criteria, the optimal QPSK 4-state, 8-state, 16 -state and 32- state DSTTCs are constructed. The results are validated by computer simulations.

In this paper, we present a novel channel shortening algorithm developed in particular for ultra wideband (UWB) communication systems. This algorithm makes use of exponentially decaying characteristics of the UWB channel models. It optimizes the channel decaying factor within a desired temporal window in the effective channel while keeping the channel decaying factor constrained elsewhere. The presence of the proposed channel shortening equalizer before rake reception greatly simplifies the rake receiver architecture by significantly reducing the number of channel taps. We compare its performance with all, selective and partial rakes in terms of captured channel energy, rake combining delay and bit error rate (BER). Simulation results show that the proposed algorithm improves the performance of the Rake receiver in dense multipath UWB channels and considerably reduces its structure complexity.

In this paper, a coded beamforming scheme is considered for frequency-flat multiple-input-multiple-output (MIMO) fading channels. With channel state information (CSI) available at the transmitter, this scheme combines coded modulation (CM) with downlink transmission beamforming to exploit both diversity and coding advantages. In order to identify the appropriate code design criteria, the exact pairwise error probability and bit error rate upper bounds are derived for both frequency-flat slow and fast MIMO Rayleigh fading channels. It is shown that the minimum squared Euclidean distance of the code should be maximised in slow-fading channels, whereas the minimum effective code length and the product distances should be maximised in fast-fading scenarios. This conclusion indicates that the conventional Ungerboeck's trellis-coded modulation codes originally designed for additive white Gaussian noise channels and single-antenna Rayleigh fading channels could be directly utilised in the proposed scheme for slow and fast MIMO fading channels, respectively. This eliminates the need for complicated code design. Simulation results are provided to substantiate the theoretical analysis. For an example, it is demonstrated that a trellis-coded beamforming scheme can outperform the published space-time trellis coded beamforming schemes by up to 4 dB, although it has a simpler encoder structure and requires no specific code design. Interference free is assumed in the analysis, while the impact of the imperfect CSI and antenna correlation on the system error performance is evaluated.

Linear precoding (LP) techniques for a multiuser multiple-input-multiple-output broadcast channel is investigated and analytical results of the achievable sum capacity and error performance for zero forcing (ZF) linear precoders is presented. It is shown that the detection signal-to-noise ratio of ZF-LP can be accurately approximated by a shrinking Chi-square distribution. The symbol error rate and its achievable diversity gain of ZF-LP are given. Then, an improved linear precoder based on the minimum mean-square error (MMSE) criterion is derived. Its error performance and sum capacity are analysed and compared with that of the ZF-LP. It is shown that the MMSE-LP can achieve much better error performance and a high sum capacity than the existing ZF-LP.

We are interested in studying different capacity formulations in linear vector Gaussian channels where the transmitter is only informed with the magnitude of the complex channel matrix coefficients and the receiver has perfect channel knowledge. Initially, we give the general expressions for the ergodic, compound, and outage capacities for our particular model of channel state information. Next, focusing on the compound formulation, we find that the optimal transmitter strategy consists in independent signaling through the transmit dimensions. Finally, we present a new result on the power allocation for the maximization of the outage mutual information.

Since the introduction of the block turbo code (BTC) except, several soft-input/soft-output (SISO) algorithms have been used in order to softly decode product codes. The classical Chase-Pyndiah algorithm seems to be one with the best trade-off between complexity and performance, especially for low error correction capability t (typically 1 or 2) where it is nearly optimal. However, as an algebraic decoding-based algorithm, the lack of codeword diversity is one of its weakness for BTCs with higher error correction capability and/or non binary BTCs. In this paper, we propose an improved iterative decoding algorithm for BTCs. We present a simple sliding encoding-window (SEW) based decoding algorithm which exploits the cyclic and systematic properties of RS and BCH codes. By adding the SEW algorithm to a classical algebraic decoding method, the proposed decoder can easily generate a list of codewords that are close to the decoded codeword. With the codeword diversity, we can compute more reliable soft output necessary in the turbo decoding process, Monte-Carlo simulations of binary and non-binary BTCs are carried out on Gaussian channels. The results show that the algorithm can improve the error performance up to 1.5 dB relative to the conventional Chase-Pyndiah decoder, while the increase in complexity due to the encoding is minor since it is a low-cost process compared to that of algebraic decoding. Compared to the other encoder-based decoding algorithms in the literature, the proposed algorithm has the advantage that there is no requirement to recompute the generator of parity-check matrix by using Gaussian elimination operations, thus a lower computational complexity

In this paper, we propose a set of design criteria of constructing shortened turbo product codes. Based on the criteria, we design two families of rate-compatible turbo product codes. The performance of rate-compatible turbo product codes is evaluated on the Gaussian channels using Monte Carlo simulations. We also propose a modified Gallager bounding technique to the rate-compatible turbo product codes. We show that the performance bound has a good match with the simulation results.

In this paper, the bit error rate (BER) and packet error rate (PER) performance of multiple-input multiple-output orthogonal frequency division multiplexing with bit interleaved coded modulation (MIMO-OFDM-BICM) systems using convolutional, Turbo, or low density parity check (LDPC) coding schemes are evaluated on MIMO-OFDM channels measured in various indoor environments. For a given average signal to noise ratio (SNR) per receiver, the performance of MIMO-OFDM-BICM in a line-of-sight (LoS) environment can vary significantly depending on the existence of scattering, while it is more uniformly distributed in a non-line-of-sight (NLoS) environment. We show that, due to bursty coded bit error characteristics of Turbo and LDPC coding, only marginal coding gain over convolutional coding is observed in terms of BER. However, when PER is compared, a 3 dB coding gain is achieved by the Turbo or LDPC code over the convolutional code.

Recently, various quasi-orthogonal space-time block codes (QO-STBCs) have been proposed in multi-input and multi-output (MIMO) channels. These codes achieve a full code rate (R=1) at the expense of a loss in the diversity gain. In this paper, we propose a transmit antenna shuffling scheme for QO-STBCs. We show that by adaptively mapping the space-time sequences of the QO-STBC to the appropriate transmit antennas depending on the channel condition, the proposed scheme can improve its transmit diversity with limited feedback information. The performance of the scheme with various numbers of shuffling patterns is analyzed. The bit error probability of the schemes is evaluated by simulations. It is demonstrated that with the linear zero-forcing (ZF) and the minimum mean squared error (MMSE) receivers, the closed-loop QO-STBC of four transmit antenna shuffling patterns can achieve almost the same performance as the ideal 4-path diversity by using two feedback bits and it is about 4-5 dB better than the open loop schemes.

Complex orthogonal space-time block codes (STBCs) with linear processing for more than two transmit antennas can not achieve a full rate in multiple-input and multiple-output (MIMO) channels. However, at the expense of loosing some degree of diversity advantage, it is possible to achieve a full rate or even rates higher than one for any number of transmit antennas. In this paper, we propose new closed-loop extended orthogonal STBCs (EO-STBCs) for three and four transmit antennas. We show that the schemes can achieve a full transmit diversity with linear processing and they outperform the previous closed-loop STBCs. In addition, we investigate the closed-loop EO-STBCs with receive antenna selection. It is shown that for four transmit antennas and up to three receive antennas, performance gains of up to 5.8 dB are obtained over the closed-loop EO-STBCs without antenna selection. The performance improvement is also investigated when the proposed method is concatenated with bit-interleaved turbo coded modulation (BITCM)

Complex orthogonal space-time block codes (STBCs) with linear processing for more than two transmit antennas cannot achieve a full rate in multiple-input and multiple-output (MIMO) channels. However, at the expense of losing some degree of diversity advantage, it is possible to achieve a full rate or even rates higher than one for any number of transmit antennas. In this letter, we propose new closed-loop extended orthogonal STBCs (EO-STBCs) for three and four transmit antennas. We show that the schemes can achieve a full transmit diversity with linear processing, and they outperform the previous closed-loop STBCs. In addition, we investigate the closed-loop EO-STBCs with receive antenna selection. It is shown that for four transmit antennas and up to three receive antennas, performance gains of up to 5.8 dB are obtained over the closed-loop EO-STBCs without antenna selection.

For coded multiple-input multiple-output spatial multiplexing (MIMO-SM) systems, the iterative receiver consisting of the MIMO detector and decoding can provide near optimal performance. While the sphere detection (SD) technique can be employed to implement the MIMO maximum likelihood (ML) detection with a lower complexity, some modifications of the SD have been proposed to provide a soft-decision for iterative receivers. In the paper, we propose an alternative approach that is based on a quadratic cost function to find the maximum a-posteriori (MAP) solution for the MIMO detection. Using the proposed approach, the MAP detection with the soft-decision can be straightforwardly implemented by the SD technique. Compared to the existing approach, in the new scheme, the soft-decision is well defined and it avoids a numerical instability in computing a soft-decision (which is an approximation of the log likelihood ratio (LLR)). Through simulation results, it is shown that the performance of the proposed scheme is comparable to that of the existing approach.

We consider the problem of selecting a subset of transmit antennas in MIMO systems to minimize error probability when only partial channel information is available at the transmitter. An upper bound for error probability of space-time coded transmit antenna selection scheme conditioned on the channel state information is presented. Based on the performance analysis, a criterion of selecting a subset of available transmit antennas to minimize the upper bound on the PEP is proposed. In contrast to other transmit antenna selection schemes for uncoded transmission or with a fixed number of antennas within the selection subset in the literature, the proposed scheme can adaptively select both a variable number of transmit antennas and their corresponding space-time codes for transmission. Furthermore, we present pragmatic space-time trellis coding schemes for slow Rayleigh fading channels. The principal advantage of the schemes is that a single encoder and decoder can be used for systems with a variable number of transmit antennas. The performance of the pragmatic space-time codes with adaptive antenna selection and the effect of the imperfect channel estimation on performance are evaluated by simulations. It is shown that the adaptive selection offers considerable antenna selection gain relative to the antenna selection system with a fixed number of antennas within the selection subset

We analyze the performance of a trellis-coded beamforming scheme for a MIMO system over Rayleigh fading channels. It is assumed that the channel state information can be obtained at both transmitter and receiver. Before transmission, the coded symbols are weighted to maximize the received signal-to-noise ratio. We derive an upper bound for the pair-wise error probability and a closed-form expression for the exact average pair-wise error probability. It is shown that on slow Rayleigh fading channel, the minimum squared Euclidean distance between the pair-wise sequences should be maximized, while on fast Rayleigh fading channel, the code effective length and the product distances should be maximized to achieve higher performance gain. Furthermore, we show that this trellis coded beamforming scheme outperforms the space-time trellis coded beamforming scheme. Simulation has been conducted to verify the performance analysis. For the sake of practicality, simulation over imperfect channels is done to compare with simulation over perfect channels.

This paper considers precoder designs in downlink MIMO broadcast channels (BC). A preceding scheme that is analog to the MMSE decision feedback equalizer in the uplink MIMO multiple access channels is first introduced. The proposed scheme can asymptotically achieve the sum-capacity of MIMO-BC at high SNRs. At medium SNRs, its achievable sum capacity is very close to that of MIMO-BC and larger than that of ZF-DR. The new scheme can also offer a significant gain over linear pre-equalization techniques in terms of average error probability. Furthermore, by linking the proposed precoder with the sphere encoder, a modified sphere encoder is devised. The modified sphere encoder is shown to be able to achieve the same diversity order as the maximum likelihood (ML) receiver in dual uplink. Its performance is also approaching the corresponding ML receiver.

In this paper, the error performance of the Alamouti scheme with transmit antenna selection is investigated in the context of imperfect subset selection. The asymptotic bit error performance is derived for binary phase-shift keying (BPSK) modulation in flat Rayleigh fading channels. It is shown that the transmit diversity order is equal to the larger ordinal number of the antenna within the selected antenna subset, while the smaller ordinal number only determines horizontal location of the error performance curve without an impact on asymptotic diversity order. Simulation results are provided to substantiate the theoretical analysis.