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.

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.

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.

In this paper, we propose to employ a modular-based movableextremely large-scale array(XL-array) at Alice for enhancing covert communication performance. Compared with existing work that mostly considered either far-field or near-field covert communications, we consider in this paper a more general and practicalmixed-fieldscenario, where multiple Bobs are located in either the near-field or far-field of Alice, in the presence of multiple near-field Willies. Specifically, we first consider a two-Bob-one-Willie system and show that conventional fixed-position XL-arrays suffer degraded sum-rate performance due to theenergy-spread effectin mixed-field systems, which, however, can be greatly improved by subarray movement. On the other hand, for transmission covertness, it is revealed that sufficient angle difference between far-field Bob and Willie as well as adequate range difference between near-field Bob and Willie are necessary for ensuring covertness in fixed-position XL-array systems, while this requirement can be relaxed in movable XL-array systems thanks to flexible channel correlation control between Bobs and Willie. Next, for general system setups, we formulate an optimization problem to maximize the achievable weighted sum-rate under covertness constraint. To solve this non-convex optimization problem, we first decompose it into two subproblems, corresponding to an inner problem for beamforming optimization given positions of subarrays and an outer problem for subarray movement optimization. Although these two subproblems are still non-convex, we obtain their high-quality solutions by using the successive convex approximation technique and devising a customized differential evolution algorithm, respectively. Last, numerical results demonstrate the effectiveness of proposed movable XL-array in balancing sum-rate and covert communication requirements, as compared to various benchmark schemes.

The over-the-air (OTA) computation which utilizes the waveform-superposition property of wireless signals has been considered as a promising approach to simultaneously accomplish communication and computing tasks in multiple access channels. Equipping federated learning (FL) with the OTA computation allows distributed Artificial Intelligence of Things (AIoT) devices to collaboratively train machine learning models over wireless environment, while preserving data privacy without excessive bandwidth consumption. In fact, the appearance of jamming attacks in OTA-FL systems can severely disrupt the convergence. This paper studies the problem of jamming attacks and its countermeasures in over-the-air federated learning (OTA-FL). To this end, we propose the coordinated dropout strategy (CoDrop), which enables AIoT devices to collaboratively drop out (i.e., to refrain from transmitting) part of their gradients so that the jamming signals aggregated in received signals can be accurately measured and mitigated. Our simulation results reveal that CoDrop is effective in alleviating the negative impacts of jamming signals with significantly low dropout rates, and it is shown to converge well as compared to existing solutions under various parameter settings.

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.

The over-the-air (OTA) computation which utilizes the waveform-superposition property of wireless signals has been considered as a promising approach to simultaneously accomplish communication and computing tasks in multiple access channels. Equipping federated learning (FL) with the OTA computation allows distributed Artificial Intelligence of Things (AIoT) devices to collaboratively train machine learning models over wireless environments, while preserving data privacy without excessive bandwidth consumption. However, the appearance of wardens in OTA-FL can result in model eavesdropping and the risk of information leakage. In this paper, we investigate how OTAFL can be fulfilled among AIoT devices while ensuring the covertness (i.e., the prevention of eavesdropping) against wardens. To this end, we formulate an optimization problem to maximize achievable data rates subject to covertness and transmit power constraints. Then, we first derive optimal detection thresholds to simplify the covertness constraints, and then propose the covertness-aware transmit power control (CAMEO) algorithm, which iteratively explores transmit powers that yield high target signal-to-interference-plus-noise ratios while satisfying both the covertness and transmit power constraints. Simulation results demonstrate that the CAMEO algorithm outperforms comparison schemes in terms of test accuracy, and it is also shown to converge well under various parameter settings.

Orthogonal delay-Doppler division multiplexing (ODDM) has been proposed as a novel modulation scheme for high-mobility environments. Since it is a multi-carrier modulation scheme, ODDM may be subject to concerns regarding high peak-to-average power ratio, which is a practically important issue but remains to be thoroughly studied. In this paper, we propose a method for estimating the instantaneous power distribution of ODDM using its wideband filtered OFDM-based approximate implementation. Specifically, the output of the inverse discrete Fourier transform in this implementation is treated as the signal alphabet of a single-carrier modulation scheme. Analytical results show good agreement with simulations, validating the effectiveness of the proposed approach.

In pursuit of reliable performance for high-mobility integrated sensing and communication (ISAC) scenarios, the orthogonal delay-Doppler division multiplexing (ODDM) modulation can be leveraged to exploit the inherent channel sparsity in the delay-Doppler (DD) domain. In this letter, we propose a joint target localization and channel estimation method for ODDM-ISAC systems that employs a multi-pilot training frame to enhance parameter estimation with accumulated signal energy. By exploiting the block-circulant-like structure of the equivalent sampled DD domain (ESDD) channel matrix, multiple pilots are strategically arranged within a training frame. To facilitate effective energy accumulation from different pilots, a phase compensation strategy is devised, which improves the accuracy of parameter estimation for target localization and channel reconstruction. Moreover, the feasibility of the proposed method is theoretically analyzed when the actual delay and Doppler shift are on or off the quantized DD grid, respectively. Simulation results validate the effectiveness of the proposed method for both target localization and channel estimation.

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.

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.

Beyond 5G mobile networks are required to support the ultra-reliable data transmission with massive connectivity under high-mobility scenarios, where the delay-Doppler domain multiple access (DDMA) has been regarded as one of the potential candidates to avoid the severe performance degradation brought by double-dispersive channels. However, existing DDMA transceiver designs heavily rely on the shared codebooks or large guard space among user equipments (UEs), which restricts the flexibility, complexity, and spectral efficiency significantly. In this paper, we first propose the Zadoff-Chu training sequences-based frame structure and an element-wise iterative successive interference cancellation (SIC)-maximal ratio combining (MRC) detector for single-user transmission, which serves as the basis of flexible resource allocation in the delay-Doppler (DD) domain. The discussion is then extended to the MU downlink scenario, where rate splitting is adopted to effectively balance the noise and interference at the UE side to improve the bit error rate performance. For MU uplink cases, a non-orthogonal pilots-based iterative SIC-least square channel estimator is developed to promote the spectral efficiency while the iterative SIC-MRC detector is also provided. Simulation results demonstrate the excellent performance of the proposed DDMA scheme under typical DDMA patterns without guard space between UEs.

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.

Federated learning (FL) has emerged as a promising technique to realize distributed machine learning (ML) in practice. FL enables multiple clients to collaboratively train a common ML model without the need to collect raw data from clients, which therefore has merit in the protection of data privacy. On the other hand, it is known that quantum computing excels in solving specific problems that are computationally prohibitive on classical computers due to its ability to harness quantum superposition and entanglement. However, current noisy intermediate-scale quantum (NISQ) computers have difficulties in dealing with many-qubit computation due to the lack of reliable error-correction schemes, which renders hybrid quantum-classical computing with few qubits a promising alternative. In this paper, we investigate how hybrid quantum-classical computing can be applied to FL while considering the data heterogeneity among clients. To this end, we propose the two-qubit quantum circuit-embedded convolutional neural network (2QCNN) for each client, which incorporates parallel two-qubit variational quantum circuits (VQCs) between the fully connected layers of the CNN. Simulation results show that 2QCNN outperforms the comparison schemes in terms of test accuracies. In addition, the impacts of the number and depth of parallel two-qubit VQCs on the performance of 2QCNN under various degrees of data heterogeneity are further evaluated.

In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) based integrated sensing and communications (ISAC) systems. Specifically, we design a beamforming strategy that controls the range of beam squint by adjusting the values of phase shifters and true time delay lines. With this design, beams at different subcarriers can be aligned along different directions in a planned way. Then the received echo signals at different subcarriers will carry targets information in different directions, based on which the targets' angles can be estimated through sophisticatedly designed algorithm. Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different OFDM symbols to estimate the distance and velocity of dynamic targets. Interestingly, the proposed sensing scheme only needs to transmit and receive the signals once, which can be termed as You Only Listen Once (YOLO). Compared with the traditional ISAC method that requires time consuming beam sweeping, the proposed one greatly reduces the sensing overhead. Simulation results confirm the effectiveness of the proposed scheme.

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm in the Artificial Intelligence of Things (AIoT). FL enables AIoT devices to collaboratively train an ML model on the network edge, while protecting data privacy and solving the problem of isolated data islands. Contemporary FL is typically realized through the model aggregation of locally trained models and the model dissemination of a globally averaged model; such a procedure iteratively proceeds until a predefined convergence criterion is met. However, FL necessitates frequent information exchanges between AIoT devices and a parameter server, which inevitably induces tremendous communication costs. Therefore, this article proposes a new design for efficient one-shot FL for AIoT systems, so that the model aggregation and dissemination can be completed within a single communication round. To this end, we leverage optimal transport (OT) theory to design the coupled model averaging (CODE) algorithm to fuse the model weights of the neural networks (NNs) on AIoT devices. The CODE algorithm initially performs OT-based layer-by-layer model averaging (MA) over two NNs to form a fused NN, which will then be averaged with another NN. The CODE algorithm progressively determines a pair of NNs, and continues until all NNs have been examined to achieve one-shot FL. In addition, we provide a detailed convergence analysis for the proposed solution. Our simulation results show that the proposed solution outperforms other one-shot MA mechanisms under various parameter settings.

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.

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.

Using communications signals for dynamic target sensing is an important component of integrated sensing and communications (ISAC). In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) Terahertz band communications systems. Specifically, we construct a wideband channel model of the echo signals, and design a beamforming strategy that controls the range of beam squint by adjusting the values of phase shifters and true time delay lines. With this design, beams at different subcarriers can be aligned along different directions in a planned way. Then the received echo signals at different subcarriers will carry target information in different directions, based on which the targets’ angles can be estimated through sophisticatedly designed algorithm. Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different orthogonal frequency division multiplexing (OFDM) symbols to estimate the distances and velocities of dynamic targets. Interestingly, the proposed sensing scheme only needs to transmit and receive the signals once, which can be termed asYou Only Listen Once(YOLO). Compared with the traditional ISAC methods that require time consuming beam sweeping, the proposed one greatly reduces the sensing overhead. Simulation results are provided to demonstrate the effectiveness of the proposed schemes.

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.

Federated learning (FL) has received explosive research attention in that it enables multiple clients to collaboratively train a global model without sharing raw data in between, thereby facilitating the protection of data privacy. Typically, FL can converge well after a couple of communication rounds, but its convergence is vulnerable to the model poisoning attacks induced by fake clients. Existing works have been devoted to designing various post-processing techniques to alleviate the adverse effects of the model poisoning attacks, they, however, fail to accurately trim off the local models of fake clients while keeping those of benign clients intact during model averaging. In this paper, we investigate the problem of model poisoning attacks in federated learning (FL). To cope with this problem, we design the federated adaptive trimmed mean (FedATM) algorithm, where the clients are sorted in accordance with the distances between local models, and a distance-based threshold is designed to detect the presence of fake clients, thereby preventing the fake local models from destroying the accuracy of model averaging. Simulation results show that the proposed FedATM algorithm is robust to model poisoning attacks as compared to several comparison schemes under various data heterogeneities.

Federated learning (FL) enables edge devices (EDs) to collaboratively train a single machine learning (ML) model maintained by an edge server (ES) without sharing their raw data contents, thereby facilitating distributed ML while protecting data privacy. In fact, FL can be implemented in wireless environments by means of the over-the-air (OTA) computation, which takes advantage of the waveform-superposition property of wireless signals to receive local gradients from EDs without the needs of increased bandwidth. Despite the existing works devoted to power control and client selection in OTA-FL systems, they mostly neglect how to construct cohorts (i.e., a subset of EDs that have similar channel coefficients) to elevate the quality of the aggregated local gradients. In this paper, we investigate the problem of gradient aggregation and recovery in OTA-FL systems. To cope with this problem, we propose the cohort-based power scaling and gradient recovery (CRAIC) algorithm, where we first construct cohorts based on uplink channel coefficients, and then we adjust the transmit powers of EDs and recover the aggregated local gradients in a cohort basis. Simulation results show that our proposed solution outperforms several comparison schemes, and we further evaluate how it performs under various parameter settings.

Federated learning (FL) has been widely regarded as a promising distributed machine learning technology that utilizes on-device computation while protecting clients' data privacy. To adapt FL to wireless networks, the over-the-air (OTA) computation, which employs the superposition nature of wireless waveforms, can prevent excessive consumption of the communication resources. However, energy harvesting technology can overcome the energy limitation of clients to realize durable computation. Despite the existing works devoted to OTA FL from various aspects, they mostly neglect jointly performing client selection and energy management for energy harvesting devices. In this paper, we investigate the combined problem of client selection and energy management for OTA FL and formulate it as a nonlinear integer programming (NIP) problem to minimize the optimality gap. To solve the NIP problem, we propose a client selection scheme that jointly considers channel state information, residual battery capacities, and dataset size. Our simulation results show that the proposed solution outperforms other comparison schemes within various parameter settings.

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 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.

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.

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance.

The performance of an orthogonal frequency division multiplexing (OFDM) system can be largely restricted without taking precautions against phase noise (PN) in receivers. In this paper, we focus on a time-domain PN estimation and compensation problem in OFDM systems. Despite the existing works devoted to the estimation of PN with the aid of pilot signals, they mostly select a set of orthogonal basis to assemble PN, which may lead to inaccurate estimation and compensation of PN. To alleviate the adverse impacts of PN in OFDM systems, we propose a data-driven approach that leverages historical PN samples to extract a set of non-orthogonal frequency-oriented (NOFO) basis for PN mitigation. To show the practicability of the proposed data-driven approach, we employ a 5G new radio (NR) like simulation setting and our numerical results show that the proposed approach can mitigate PN effectively, thereby attaining improved bit error rate (BER) performance.

The proliferation of the Internet of Things (IoT) applications has resulted in the ever-increasing research attention in recent years. In light of the sensitivity to latency in various IoT applications, the age of information (AoI) has been widely regarded as a promising performance metric to quantify the timeliness (i.e., freshness or age) of information updates from IoT devices. In addition, serverless computing (also known as function as a service (FaaS)) evolves as a highly scalable and flexible computing architecture that can facilitate timely IoT analytics. In fact, the execution of serverless functions may rely on the data collected from IoT devices, therefore the freshness of information updates of IoT devices has prompt impacts on the age of service (AoS) of serverless functions. Although existing works have been devoted to various aspects of serverless computing, the issues regarding how information updates affect the AoS of serverless functions are rarely investigated. In this paper, we address the information update delivery and acquisition (IUDA) problem for IoT with serverless computing and formulate it an integer linear program (ILP), the objective of which is to minimize a weighted sum of AoS of serverless functions. To cope with the IUDA problem, we propose an offline and an online algorithm for scheduling information updates with and without the knowledge of the arrivals of serverless functions, respectively. Our simulation results demonstrate that the proposed solutions outperform existing solutions in terms of the AoS performance and effectively provide serverless functions with timely information updates under various parameter settings.

The proliferation of Internet of Things (IoT) applications has prompted the continuous increase of research efforts in recent years. In light of the diversified use cases and service requirements, the information freshness [or Age of Information (AoI)] of IoT data is key for latency-sensitive IoT applications (e.g., industrial automation and intelligent transportation) because stale information may lead to delayed responses and catastrophic outcomes. In addition, various types of IoT applications require the analytics of IoT data collected from their constituent IoT devices. While previous AoI-related works have analyzed or optimized the information freshness of various communication systems, the problem that theinformation cofreshness[or Coage of Information (CoI)] of an IoT application is determined by the maximum AoI of the constituent IoT devices has been rarely investigated. In this article, we address the grant assignment and transmission scheduling (GATS) problem for IoT and formulate it as an integer linear program (ILP) to minimize a weighted sum of CoI. Due to the intractability of the original GATS problem, we transform it to an equivalent problem of the maximization of the number of the eliminated age blocks. Then, we propose the CoI-aware age block elimination (CABEL) algorithm in which information updates are selected progressively according to their coage efficiency (CE) values and prove that the achieved approximation factor depends on the relative service costs and uplink delays. Our simulation results demonstrate that the proposed solution can effectively perform information updates and utilize service budgets, thereby achieving low CoI compared with the existing solutions under various parameter settings.

Energy saving in cellular systems has attracted the extensive attention of various studies due to ever-deteriorating global warming. In addition to network greenness, various emerging mobile applications (e.g., mixed reality and automated vehicles) further necessitate timely service provision. Recently, the age of information (AoI) has been regarded as a promising performance metric for quantifying the freshness (i.e., timeliness) of information updates in communication systems. Despite the existing works devoted to energy saving in cellular systems, how to leverage information freshness to ensure timely information updates while achieving network greenness is rarely investigated. In this paper, we investigate the problem of freshness-aware energy saving in cellular systems, where active base stations (BSs) can cooperatively update information to target devices (TDs). To address this problem, we formulate a mixed-integer nonlinear program (MINLP) to minimize average power consumption. Due to its intractability, we propose decomposing the MINLP into two subproblems by means of constraint reinterpretation and spatiotemporal decoupling. Then, we propose a two-stage solution that leverages LP techniques to sequentially determine update scheduling and BS activeness. Our simulation results show that the proposed solution can adequately perform update scheduling and BS activeness control, thereby effectively saving energy for cellular systems under various parameter settings.

In this paper, we design a multi-beam low earth orbit (LEO) satellite internet of things (IoT) for beyond fifth-generation (B5G) wireless networks. Rather than time division multiple access (TDMA), a new non-orthogonal multiple access (NOMA) scheme is adopted to support massive IoT over a very wide range. In order to reduce the power consumption of multi-beam satellite, a spot beam design algorithm is proposed with the goal of minimizing the total power consumption subject to quality-of-service (QoS) requirements. Furthermore, considering high computational complexity of spot beam design in the context of massive IoT, a simple multi-beam design algorithm is provided. Finally, simulation results confirm the effectiveness of the proposed algorithms over conventional ones.

The beyond fifth-generation (B5G) cellular internet of things (IoT) network is required to support low-power and wide-coverage wireless access for a massive number of devices. However, the severe co-channel interference caused by massive access decreases the reliability and coverage. To solve this problem, we design a large intelligent reflecting surface (IRS) aided massive access framework, including channel estimation and information transmission. Furthermore, we analyze the performance of the proposed framework, and reveal the role of the large IRS. It is found that the large IRS is beneficial to improve the performance of edge-devices, and thus enhance the coverage. Moreover, we find that the refection coefficient should be carefully chosen according to channel conditions and system parameters to improve the performance.

In this article, we investigate the issue of massive access in a beyond fifth-generation (B5G) cellular Internet of Things (IoT) network. To reduce the overhead of channel state information acquisition and the complexity of transceiver design, an integrated framework of massive beam-division multiple access (BDMA) is proposed according to the characteristics of beamspace propagation. Then, we analyze the performance of the proposed massive BDMA scheme and derive a closed-form expression for the weighted sum rate in terms of channel conditions and system parameters. To improve the overall performance, we propose a massive access algorithm by jointly optimizing transmit power and receive vector. It is found that the optimal receive vector for each IoT device is the base beam corresponding to the arrival of angle of the IoT device's signal in the beamspace, which significantly simplifies the design of the receiver. Considering the constraint of radio-frequency (RF) chains in practical networks, we propose a clustering-based massive access algorithm, which allocates an RF chain for a cluster. Finally, extensive simulation results confirm that the proposed algorithms can provide small overhead and low complexity massive access schemes for B5G cellular IoT.

This article addresses the issue of symbol detection in ambient backscatter communication systems with the multiantenna reader. Focusing on the ON-OFF keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle. We then analyze the exact closed-form BER expression for the optimal detector. Moreover, simple approximate BER expressions are derived in certain asymptotic regimes. Furthermore, a simple energy detector is analyzed as a benchmark scheme, and the asymptotic BER is devised in a closed form. The findings of this article suggest that implementing multiple antennas at the reader is an effective means to enhance the BER performance and extend the tag-reader communication range. Also, the optimal detector always outperforms the energy detector. In particular, the optimal detector can avoid the error floor phenomenon, which is inevitable for the energy detector in the high SNR regime.

We consider a wireless powered two-way relaying system consisting of two energy constrained single antenna sources and one multi-antenna relay with constant power supply. The time division protocol is adopted, where the relay first acts as the energy source and employs energy beamforming to charge the two sources, and then switches its role as a relay to help forward the information to the sources. To maintain user fairness, we aim to maximize the minimum rate of two sources, by jointly optimizing the energy beamforming vector, time splitting factor and relay transformation matrix. To further reduce the complexity of the optimal algorithm, we propose an alternating optimization method, where closed-form expressions for the energy beamforming and time splitting factor are obtained. To gain more insights, we propose a simple suboptimal design and analyze the outage probability and the average rate when the relay applies simple energy beamforming and transformation matrix. The analysis shows that the system can achieve a diversity order of $\frac {N}{3}$ for a system with a relay of $N$ antennas. Numerical results show that the performance of the proposed low-complexity alternating optimization method approaches the optimal algorithm over the entire SNR range, but has a significantly low complexity, and the proposed suboptimal design can also achieves a decent performance especially in small $N$ regime.

In order to support massive connections over limited radio spectrum for the cellular Internet-of-Things (IoT) in the fifth-generation (5G) wireless network, we propose a new non-orthogonal beamspace multiple access framework. First, we analyze the performance of the proposed non-orthogonal beamspace multiple access scheme, and derive an upper bound on the weighted sum rate in terms of channel conditions and system parameters. Then, we provide a transmit beam construction algorithm for further improving the overall performance. Finally, extensive simulation results show that substantial performance gain can be obtained by the proposed non-orthogonal beamspace multiple access scheme over the baseline ones.

In this paper, we study the optimum multicast scheduling of cache-enabled content-centric wireless networks, where multiple users requesting multiple contents with different quality of service (QoS) constraints. The QoS of a content is measured by using the worst-case delay between a request and transmission. Each content is assigned a unique delay threshold based on its delay tolerance. To ensure the timely delivery of the contents, a large delay penalty is imposed if the worst case delay for a content exceeds its delay threshold. In case two or more contents reach their respective delay thresholds simultaneously, the content with a higher priory is assigned a larger penalty in the cost function to prioritize its transmission. Delaying the transmission of a content can potentially increase the number of requests served by a single transmission, given that more requests for the same content might arrive during the waiting period, thus reducing the average power consumption per request. The objective of this paper is to identify the optimum multicast scheduling policy that can jointly minimize the weighted combination of average power, delay penalty, and the fetching cost associated with fetching uncached contents from a remote server. The problem is formulated as an infinite horizon average cost Markov decision process (MDP), and it is optimally solved by applying the relative value iteration algorithm. Simulation results demonstrate that the proposed multicast scheduling outperforms existing scheduling algorithms, and it can achieve flexible tradeoff between power and delay in a multicast system by adjusting the weight coefficient in the cost function.

In this paper, we consider the frequency synchronization issue in orthogonal frequency division multiplexing distributed antenna systems. There may exist multiple carrier frequency offsets (CFOs) between the transmitter and distributed receive antennas. To solve the multi-CFO estimation problem, we first derive the blind maximum likelihood estimator, which has superior estimation performance, but exhibits high computational complexity. Furthermore, we develop a blind pairing-based efficient estimator (PBEE), which divides the multiple antennas into pairs of two. The two CFOs in each pair are jointly estimated with a carefully designed cost function, which proves to be exactly a cosine function parameterized by the two CFOs even with the effect of noise. Consequently, the joint estimation of the two CFOs can be simplified to the one-dimensional search optimization problem. The PBEE can achieve better CFO estimation performance than the existing competitors. From the theoretical mean square error (MSE) analysis of PBEE, we further derive the optimal antenna pairing scheme by minimizing the signal-to-noise ratio degradation caused by the uncompensated residual CFOs. Finally, the Cramér-Rao bound of CFO estimation is derived and numerical results are provided to corroborate the proposed studies.

This paper investigates the achievable performance of cell-free massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) at both the access points (APs) and users. A closed-form expression for the achievable rate is derived, which enables the study of the effects of AP number, antenna number per AP, user number, and ADC resolution on the achievable rate. In addition, a simple asymptotic approximation for the individual user rate is presented, which shows that the user rate is mainly constrained by the ADC resolution at the user. Moreover, we propose an ADC resolution bits allocation scheme aiming at maximizing the sum rate subject to the total ADC resolution bits constraint, which substantially outperforms the equal ADC resolution bits allocation scheme. Furthermore, a max-min power control scheme is proposed, which not only ensures user fairness, but also improves the achievable rate. Finally, simulation results are provided to corroborate the analytical results.

In this paper, we apply angle-domain Doppler compensation for high-mobility wideband massive multi-input multi-output (MIMO) uplink communications. The time-varying multipath channel is considered between high-speed terminal and static base station (BS), where multiple Doppler frequency offsets (DFOs) are associated with distinct angle of departures (AoDs). With the aid of large-scale uniform linear array (ULA) at the transmitter, we design a beamforming network to generate multiple parallel beamforming branches, each transmitting signal pointing to one particular angle. Then, the transmitted signal in each branch will experience only one dominant DFO when passing over the time-varying channel, which can be easily compensated before transmission starts. We theoretically analyze the Doppler spread of the equivalent uplink channel after angle-domain Doppler compensation, which takes into account both the mainlobe and sidelobes of the transmit beam in each branch. It is seen that the channel time-variation can be effectively suppressed if the number of transmit antennas is sufficiently large. Interestingly, the asymptotic scaling law of channel variation is obtained, which shows that the Doppler spread is proportional to the maximum DFO √ and decreases approximately as 1/√(M) (M is the number of transmit antennas) when M is sufficiently large. The numerical results are provided to corroborate the proposed scheme.

In this paper, we design a compressed sensing (CS) based channel estimation method for millimeter wave (mmWave) massive MIMO systems and investigate the impact of dual-wideband effect (frequency-wideband and spatial-wideband) that appears in large array communications. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the basis mismatch problem. Hence, the proposed method achieves better channel estimation accuracy compared to most state-of-the-art algorithms that rely on on-grid CS approach. Moreover, the proposed method could successfully handle the spatial-wideband effect (sometimes known as beam squint effect) for wideband massive MIMO communications that was previously ignored by many existing literatures. Simulation results are provided to demonstrate the superior performance of the proposed method.

A large-scale antenna array introduces the innegligible propagation delay for a received signal across the array aperture in addition to a phase rotation. If the delay is comparable to a symbol period in wideband millimeter-wave (mmWave) communications, then its impact on channel estimation and signal detection needs to be properly treated. In this case, different frequencies actually “see” distinct angles of arrival (AoAs) for the same physical path, which is also known as the beam squint effect. In this article, we propose a new channel estimation scheme with full consideration of beam squint for the mmWave massive multiple-input multiple-output (MIMO) systems. A super-resolution compressed sensing approach is first developed to jointly extract the initial AoA, the time delay, and the complex gain of each physical path, from which channel covariance matrix can be constructed rather than acquired through the long-term average. Then, the least-square (LS) and the linear minimum mean- squared error (LMMSE) channel estimators are designed with a small amount of training. Numerical results demonstrate the superiority of the proposed scheme over the conventional methods.

In this paper, we design robust beamforming to guarantee the physical layer security for a multiuser beam division multiple access (BDMA) massive multiple-input multiple-output (MIMO) system, when the channel estimation errors are taken into consideration. With the aid of artificial noise, the proposed design are formulated as minimizing the transmit power of the base station, while providing legal users and the eavesdropper with different signal-to-interference-plus-noise ratio. It is strictly proved that, under BDMA massive MIMO scheme, the initial non-convex optimization can be equivalently converted to a convex semi-definite programming problem and the optimal rank-one beamforming solutions can be guaranteed. In stead of directly resorting to the convex tool, we make one step further by deriving the optimal beamforming direction and the optimal beamforming power allocation in closed-form, which greatly reduces the computational complexity and makes the proposed design practical for real world applications. Simulation results are then provided to verify the efficiency of the proposed algorithm.

Wireless power transfer (WPT) is an emerging and promising technique for power supplies to mobile and portable devices. Among all approaches, magnetic resonant coupling (MRC) is an excellent one for midrange WPT, which provides high mobility, flexibility, and convenience due to its simplicity in hardware implementation and longer transmission distances. In this letter, we consider an MRC-WPT system with multiple power transmitters, one intended power receiver and multiple unintended power receivers. The optimal robust beamforming design of the complex transmit currents is investigated to achieve the minimal total source power with the worst-case mutual inductances measurement, whereas the unintended receiving powers are constrained by certain bounds. Numerical results demonstrate that the proposed algorithm can significantly improve the performance and the robustness of the MRC-WPT systems.

Pilot contamination attack is an important activity of active eavesdropping conducted by a malicious user during channel training phase. This attack is potentially harmful to the physical layer security. In this paper, motivated by the fact that frequency asynchronism could introduce divergence of the transmitted pilot signals between intended user and attacker, we propose a new uncoordinated frequency shift (UFS) scheme for detecting pilot contamination attack in multiple antenna system. During the reverse training phase of the UFS scheme, the legitimate user Bob deliberately introduces multiple random frequency shifts in the publicly known pilot sequence. Since eavesdropper Eve has no knowledge of these random frequency shifts, it is almost impossible for her to pretend exactly like Bob. This provides the opportunity to detect the presence of Eve. An attack detection algorithm is then developed based on source enumeration method. Both the asymptotic performance analysis and numerical results are provided to verify the proposed detection scheme. The proposed scheme is also enhanced based on noise power estimation to cope with attacks from a multi-antenna Eve. Furthermore, the proposed UFS scheme is extended by introducing general parameterized phase shifts. It is demonstrated that the proposed UFS scheme can achieve comparable detection performance as the existing superimposed random sequence based scheme, without sacrifice of legitimate channel estimation performance.

In this paper, we consider a point-to-point multi-input multi-output wireless-powered communication system, where the source S is powered by a dedicated power beacon (PB) with multiple antennas. Employing the time splitting protocol, the energy constrained source S first harvests energy through the radio-frequency signals sent by the PB and then uses this energy to transmit information to the destination D. Unlike several prior works, we assume that the energy transfer link is subjected to Rician fading, which is a real fading environment, due to relatively short range power transfer distance and the existence of a strong line of sight path. We present a comprehensive analysis of the achievable ergodic capacity in two scenarios, depending on the availability of channel state information (CSI) at PB, namely, the absence of CSI and partial CSI. For the former case, equal power allocation is used, while for the later one, energy beamforming is used to enhance the energy transfer efficiency. For both the cases, closed-form expressions for the upper and lower bounds of the ergodic capacity are derived. Furthermore, the optimal time split is discussed, and the capacity in the low and high signal-to-noise ratio regimes is studied through simple closed-form expressions. Numerical results and simulations are provided to validate the theoretical analysis. The results show that the Rician factor K has a significant impact on the ergodic capacity performance, and this impact strongly depends on the availability of the CSI at the PB.

In this paper, we design robust downlink beamforming against the imperfect channel state information (CSI) for beam division multiple access (BDMA) massive multiple-input multiple output (MIMO) systems. Following a worst-case deterministic model, the proposed design is formulated as minimizing the power consumption of base station (BS) under different signal-to-interference-plus-noise ratio (SINR) constraints. The S-Procedure and semi-definite relaxation (SDR) are used to convert the initial non-convex optimization to a convex semi-definite programming problem. Then the optimality of SDR is strictly proved by showing the rank-one property of the optimal beamforming thanks to the orthogonal channels under BDMA scheme. More importantly, we make one step further by deriving the optimal beamforming directions and optimal beamforming power allocation of the SDR in closed-form, which greatly reduces the optimization complexity and makes the proposed design practical for a real word massive MIMO system. Simulation results are then provided to verify the efficiency of the proposed robust beamforming algorithm.

Soft iterative detection/decoding algorithms are fundamentally necessary for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) adopted in the Third Generation Long Term Evolution (LTE)-Advanced in order to increase the capacity and achieve high data rates. However, their high performance critically requires log likelihood ratio computations with prohibitive complexity. This challenge will be addressed in this paper. We first use the assumption of Gaussian transmit symbols to show the equivalence among several existing algorithms. We next develop a non-Gaussian approximation for high-order constellations, which paves the way for interference cancellation-based detectors. Based on both Gaussian and non-Gaussian approximations, we thus develop several capacity-achieving iterative MIMO-OFDM demodulation and decoding algorithms. To this end, we adopt K-best algorithms to take advantage of both the types of approximations and the list decoder. Unlike existing algorithms, our proposed K-best algorithms make use of the a priori probabilities to generate the list. Simulations of standard-compliant LTE systems demonstrate that the proposed algorithms outperform the existing ones.

Ambient backscatter communication is a newly emerged paradigm, which utilizes the ambient radio frequency signal as the carrier to reduce the system battery requirement, and is regarded as a promising solution for enabling large-scale deployment of future Internet of Things networks. The key issue of ambient backscatter communication systems is how to perform reliable detection. In this paper, we propose novel encoding methods at the information tag and devise the corresponding symbol detection methods at the reader. In particular, Manchester coding and differential Manchester coding are adopted at the information tag, and the corresponding semi-coherent Manchester (SeCoMC) and non-coherent Manchester (NoCoMC) detectors are developed. In addition, analytical bit-error-rate (BER) expressions are characterized for both detectors assuming either complex Gaussian or unknown deterministic ambient signal. Simulation results show that the BER performance of unknown deterministic ambient signal is better, and the SeCoMC detector outperforms the NoCoMC detector. Finally, compared with the prior detectors for ambient backscatter communications, the proposed detectors have the advantages of achieving superior BER performance with lower communication delay.

In this paper, we propose a frequency synchronization scheme for multiuser orthogonal frequency division multiplexing uplink with a large-scale uniform linear array at base station (BS) by exploiting the angle information of users. Considering that the incident signal at BS from each user can be restricted within a certain angular spread, the proposed scheme could perform carrier frequency offset (CFO) estimation for each user individually through a joint spatial-frequency alignment procedure and can be completed efficiently with the aid of fast Fourier transform. A multi-branch receive beamforming is further designed to yield an equivalent single user transmission model for which the conventional single-user channel estimation and data detection can be carried out. To make the study complete, theoretical performance analysis of the CFO estimation is also conducted. We further develop a user grouping scheme to deal with the unexpected scenarios that some users may not be separated well from the spatial domain. Finally, various numerical results are provided to verify the proposed studies.

In this paper, we design a channel tracking method for massive multiple-input multiple-output systems under both time-varying and spatial-varying circumstances. By exploiting the characteristics of massive antenna array, a spatial-temporal basis expansion model is proposed to reduce the effective dimension of uplink/downlink channel, which decomposes channel state information into time-varying spatial information and gain information. We first model the user's movement as the one-order unknown Markov process, whose parameters are blindly obtained by expectation and maximization learning. Then, the uplink time-varying spatial information can also be blindly tracked by unscented Kalman filter and Taylor series expansion of the steering vector, while the rest of uplink channel gain information can be trained by only a few pilot symbols. Due to physical angle reciprocity, the spatial information of the downlink channel can be immediately computed from the uplink counterpart, which greatly reduces the complexity of downlink channel tracking. Various numerical results are provided to demonstrate the effectiveness of the proposed method.

In this paper, we consider a power beacon (PB) assisted two-way relaying network, where two single antenna energy constrained users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. The key aim of the paper is to design the energy beamforming vector and time split parameter for optimizing the system performance. In particular, two different design objectives are investigated, namely, max-min rate design and sum rate maximization design. Due to the non-convex nature of the optimization problems, the global optimal solutions are difficult to obtain. Instead, we propose an alternating optimization design framework where near optimal performance can be achieved through iterative optimization. In addition, to further reduce the computation complexity, closed-form suboptimal designs are provided. Simulation results are presented to validate the effectiveness of the proposed alternating optimization design and suboptimal design. The outcomes of the paper suggest that adopting the proposed energy beamforming vector at the PB can substantially boost the system performance. Also, the topology of the network has a significant impact on the achievable performance.

For massive multiple-input multiple-output (MIMO) systems, the furthest distance between two antennas may be very large compared with the carrier wavelength. Therefore, the physical propagation delay of electromagnetic wave across the array aperture cannot be ignored, which will cause spatial-wideband effect and make the system design much different from the conventional one that only considers the frequency-wideband effect. Taking mmWave-band communications as an example, we demonstrate the spatial- and frequency-wideband effects, called the dual-wideband effects, in massive MIMO systems. We first discuss a new dual-wideband channel model. By exploiting the channel sparsity in the angle and the delay domains, we then develop a simple yet effective channel estimation algorithm. Thanks to the angular-delay reciprocity, the proposed channel estimation strategy is suitable for both TDD and FDD communication systems. The subsequent numerical results clarify that the proposed transmission design can well address the dual-wideband effects and significantly outperform the existing designs that only consider the frequency-wideband effect.

This paper investigates a power beacon (PB) assisted two-way relaying network, in which two single antenna energy constraint users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. Considering user fairness, we propose the max-min design that maximizes the minimum rate of the two users and investigate the corresponding optimal energy beamformer and time split. Due to the non-convex nature of the optimization problems, an alternating optimization design is proposed in which the optimal beamformer and time split can be solved via a low-complexity algorithm and convex optimization, respectively. Our results show that the proposed alternating optimization design yields near optimal performance, and implementing multiple antennas at the PB can significantly improve the system performance.

mmWave massive multiple-input multiple-output (MIMO) system has gained much attention for its considerable improvement in system throughput. However, the cost of the complex hardware, e.g., the radio frequency (RF) chains, hinders it from the practical deployment. In this paper, we propose an angle space channel tracking method for mmWave massive MIMO systems with limited RF chains (hybrid scheme). Specifically, the users can be scheduled according to their DOA information, i.e. angle division multiple access (ADMA). Besides, the channel information can be divided into direction of arrival (DOA) information and gain information respectively, where DOA can be tracked through unscented Kalman filter (UKF), while the gain information can be obtained from beam training and spatial rotation. Numerical results are provided to corroborate our studies.

In this paper, we design robust downlink beamforming against the imperfect channel state information (CSI) for beam division multiple access (BDMA) massive multiple-input multiple output (MIMO) systems. Different from conventional approach, the optimality of semi-definite relaxation (SDR) is strictly proved by showing the rank-one property of the optimal beamforming with the orthogonal BDMA massive channels, where globally optimal robust beamforming solutions are derived. More importantly, we make one step further by deriving the optimal beamforming directions and optimal beamforming power allocation of the SDR in closed-form, which greatly reduces the optimization complexity and makes the proposed design practical for a real word massive MIMO system. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.

This paper considers a multipair amplify-and-forward massive MIMO relaying system with low-resolution ADCs at both the relay and destinations. The channel state information (CSI) at the relay is obtained via pilot training, which is then utilized to perform simple maximum-ratio combining/maximum-ratio transmission processing by the relay. Also, it is assumed that the destinations use statistical CSI to decode the transmitted signals. A closed-form approximation of the achievable sum rate is presented, which enables the efficient evaluation of the impact of key system parameters on the system performance. It is found that increasing the number of relay antennas is an effective method to compensate for the rate loss caused by coarse quantization. Moreover, it is desirable to deploy the low-resolution ADCs at the relay and high-resolution ADCs at the destination.

We propose a time-domain phase noise (PN) compensation scheme for orthogonal frequency division multiplexing with offset quadrature amplitude modulation (OFDM/OQAM) systems. The proposed scheme fully explores the unique overlapped signal structure of OFDM/OQAM to perform symbol-wise phase noise approximation using discrete cosine transform basis. Numerical simulation results verify better performance of the proposed schemes.

This paper presents a new view of multi-user (MU) hybrid massive multiple-input and multiple-output (MIMO) systems from array signal processing perspective. We first show that the instantaneous channel vectors corresponding to different users are asymptotically orthogonal if the angles of arrival of users are different. We then decompose the channel matrix into an angle domain basis matrix and a gain matrix. The former can be formulated by steering vectors and the latter has the same size as the number of RF chains, which perfectly matches the structure of hybrid precoding. A novel hybrid channel estimation is proposed by separately estimating the angle information and the gain matrix, which could significantly save the training overhead and substantially improve the channel estimation accuracy compared with the conventional beamspace approach. Moreover, with the aid of the angle domain matrix, the MU massive MIMO system can be viewed as a type of non-orthogonal angle division multiple access to simultaneously serve multiple users at the same frequency band. Finally, the performance of the proposed scheme is validated by computer simulation results.

Non-orthogonal multiple access (NOMA) has been considered as a highly efficient communication technology in the fifth generation (5G) networks by serving multiple users concurrently through non-orthogonal sharing communication resources. NOMA can be combined with both massive multiple input multiple output (MIMO) and relaying technologies to further improve 5G system efficiency at the cost of increased complexity. These combinations rely on the efficient utilization of 3-D communication resources. In the first part of this paper, we investigate highly efficient 3-D resource allocation for massive MIMO-NOMA systems. Due to hardware complexity constraints and channel variation in the massive MIMO-NOMA system, efficient antenna selection and user scheduling algorithms are proposed for sum rate maximization. In the second part of this paper, a collaborative NOMA-assisted relaying (CNAR) system is proposed to serve multiple cell-edge users by 3-D resource utilization. To reduce the relaying complexity in CNAR system, a simplified-CNAR (S-CNAR) system is proposed as an alternative NOMA-enabled relaying strategy. Numerical results show that our antenna selection and user scheduling algorithms achieve similar performance to existing methods with reduced complexity. Under high target rate, CNAR obtains better performance over other transmission strategies and S-CNAR reaches similar performance by simplified relaying scheme.

This letter investigates the performance of a legitimate surveillance system, where a legitimate monitor aims to eavesdrop on a dubious decode-and-forward relaying communication link. In order to maximize the effective eavesdropping rate, two strategies are proposed, where the legitimate monitor adaptively acts as an eavesdropper, a jammer, or a helper. In addition, the corresponding optimal jamming beamformer and jamming power are presented. Numerical results demonstrate that the proposed strategies attain better performance compared with intuitive benchmark schemes. Moreover, it is revealed that the position of the legitimate monitor plays an important role on the eavesdropping performance for the two strategies.

Magnetic resonance coupling (MRC) has been utilized in wireless power transfer (WPT) to achieve mid-range contactless power supply. However, unintended users might also draw power from the transmission devices. In this letter, an MRC-WPT system with multiple power transmitters, one intended power receiver, and one unintended power receiver is investigated. We formulate a power security problem by limiting the unintended receiving power and, at the same time, maximizing the power of the intended user. Such an optimization problem is in general nonconvex. Nevertheless, a global optimal solution can be efficiently achieved with the aid of semidefinite relaxation approach. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Considering a heterogeneous network where both macro base station (BS) and small cell (SC) nodes are equipped with massive number of antennas, this paper studies the performance for multiple-input multiple-output downlinks when the macro and small cells share the same spectrum, and hence interfere with each other. Suppose that the large-scale antenna arrays at both macro BS and SC nodes employ maximum-ratio transmission (MRT) or zero-forcing transmission (ZFT) precoding, and transmit data streams to served users simultaneously. A new pilot reuse pattern among SCs is proposed for channel estimation. Taking into account imperfect channel state information (CSI), capacity lower bounds for MRT and ZFT are derived, respectively, in closed-form expressions involving only statistical CSI, followed by asymptotic analyses for massive arrays under specific power scaling laws. Subsequently, two user scheduling algorithms, greedy scheduling and asymptotical scheduling algorithm (ASA), are proposed based on derived capacity lower bounds and asymptotic analyses, respectively. ASA is demonstrated to be a near optimal in the asymptotic regime and has low complexity. Finally, the derived closed-form expressions are verified to be accurate predictors of the system performance by Monte Carlo simulations. Numerical results demonstrate the effectiveness of the asymptotic analysis and proposed user scheduling schemes.

This paper considers a multipair amplify-and-forward massive MIMO relaying system with low-resolution analog-to-digital converters (ADCs) at both the relay and destinations. The channel state information (CSI) at the relay is obtained via pilot training, which is then utilized to perform simple maximum-ratio combining/maximum-ratio transmission processing by the relay. Also, it is assumed that the destinations use statistical CSI to decode the transmitted signals. Exact and approximated closed-form expressions for the achievable sum rate are presented, which enable the efficient evaluation of the impact of key system parameters on the system performance. In addition, optimal relay power allocation scheme is studied, and power scaling law is characterized. It is found that, with only low-resolution ADCs at the relay, increasing the number of relay antennas is an effective method to compensate for the rate loss caused by coarse quantization. However, it becomes ineffective to handle the detrimental effect of low-resolution ADCs at the destination. Moreover, it is shown that deploying massive relay antenna arrays can still bring significant power savings, i.e., the transmit power of each source can be cut down proportional to 1/M to maintain a constant rate, where M is the number of relay antennas.

The millimeter-wave (mm-wave) massive multiple-input multiple-output (MIMO) system has gained much attention for its considerable improvement in system throughput. However, the cost of complex hardware, e.g., radio frequency (RF) chains, hinders it from practical deployment. In this paper, we propose an angle domain hybrid precoding and channel tracking method by exploring the spatial features of the mm-wave massive MIMO channel. The number of the effective spatial beams, or equivalently the RF chains, is enormously decreased via the operation of spatial rotation. The users are then scheduled by the angle division multiple access scheme, which groups users according to their direction of arrivals (DOAs). Meanwhile, a channel tracking method is designed for the subsequent data transmission through a small number of pilot symbols. Specifically, the channel information is divided into the DOA information and the gain information, where the DOA information is tracked by a modified unscented Kalman filter and the gain information is estimated from beam training. Numerical results are provided to corroborate our studies.

In this paper, we study the optimum sensing of a time-varying random event with a sensor powered by energy harvesting devices. The system aims at reconstructing a band-unlimited continuous-time random process by using discrete-time samples collected by a sensor. Due to the random nature of the harvested energy, the sensor might not have sufficient energy to perform a sensing operation at a desired time instant. We propose a best- effort Poisson sensing policy. The sensing policy defines a set of random candidate sampling instants following a Poisson point process (PPP) in the time domain. At a given candidate sampling instant, the sensor collects a sample if there is sufficient energy to do so, and does nothing otherwise. By analyzing the statistical properties of the best-effort Poisson sensing policy, we develop an optimum estimator of the underlying random event. The asymptotic mean-squared error (MSE) of the estimation are expressed as an explicit closed-form expression of several key system parameters, such as the ratio between the average energy harvesting rate and consumption rate, the time correlation of the random event of interests, and the energy allocation between sensing and transmission. %The analytical results are used to identify optimum system operation parameters. Numerical results show that the proposed best-effort Poisson sensing policy outperforms existing uniform sensing policies.

We propose a semi-blind precoding aided maximum likelihood (ML) carrier frequency offset (CFO) estimation method and a precoding aided equalization based on independent component analysis (ICA) receiver structure for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems. By carefully designing a constant in the precoding process, the power between reference data and source data can be balanced to enable ML CFO estimation and ambiguity elimination for the ICA output signals at the receiver. This proposed semi-blind non-redundant structure is much more bandwidth-and-energy efficient than the pilot aided ML CFO estimation method, as no real-time training or extra transmission power is required. Simulation results show that the proposed scheme provides a bit error rate (BER) performance the same as the performance of the pilot aided ML CFO estimation method, and close to the ideal case with perfect channel state information (CSI) and no CFO.

Level set estimation (LSE) is the process of classifying the region(s) that the values of an unknown function exceed a certain threshold. It has a wide range of applications such as spectrum sensing or environment monitoring. In this paper, we study the the optimal LSE of a linear random field that changes with respect to time. A linear sensor network is used to take discrete samples of the spatially-temporally correlated random field in both the space and time domain, and the sensors operate under a total power constraint. The samples are congregated at a fusion center (FC), which performs LSE of the random field by using the noisy observation of the samples. Under the Gaussian process (GP) framework, we first develop an optimal LSE algorithm that can minimize the LSE error probability. The results are then used to derive the exact LSE error probability with the assistance of frequency domain analysis. The analytical LSE error probability is expressed as an explicit function of a number of system parameters, such as the distance between two adjacent nodes, the sampling period in the time domain, the signal-to-noise ratio (SNR), and the spatial-temporal correlation of the random field. With the analytical results, we can identify the optimum node distance and sampling period that can minimize the LSE error probability.

Energy efficiency (EE) is an important issue at 60 GHz due to high power consumption of devices working at such high frequency. In this paper, we investigate EE optimization for full-duplex (FD) decode-of-forward (DF) relay-assisted 60 GHz multiuser indoor networks. In contrast to the existing spectral efficiency (SE) optimization, our scheme maximizes system EE for FD relaying system under cross-layer constraints, addressing the typical problems at 60 GHz, such as the effect of imperfect channel estimation due to serious signal blockage. A low-complexity EE-orientated optimization algorithm is proposed, by which the transmission power, subcarriers and throughput are allocated jointly across multiple users. Simulation results verify our analytical results and confirm that the proposed algorithm achieves a higher EE than the SE-oriented approach, while offering a comparable SE. Also, FD relaying with the proposed algorithm outperforms HD relaying in terms of both EE and SE. In addition, a much lower throughput outage probability is guaranteed by the proposed algorithm, showing its robustness against channel estimation errors. A full range of power consumption sources and imperfect self-interference cancellation are considered to rationalize our analysis.

Nowadays, data traffic in-car communication is increasing dramatically, due to the emerging technology of self-driving and on-board infotainment applications. The direct connections between vehicles and cellular infrastructures will introduce significant signalling overhead and excessive energy consumption, especially for congested and fast moving traffic. In order to improve energy efficiency and achieve green networking, an heterogeneous network, 5G-Vehicular Ad Hoc Network (5GVANET) is presented in this paper, which coupling the high data rates of VANET and the wide coverage area of 5G. In this integrated architecture, vehicles are clustered accordingly, and one vehicle in each cluster is selected as a gateway to support aggregated traffic. To ensure the capacity of the trunk link between the gateway and base station, a Non- orthogonal Multiplexed Modulation (NOMM) scheme is proposed in this paper to effectively aggregate the Vehicle-to-Infrastructure (V2I) traffic and further improve energy efficiency. NOMM splits data stream of each user into multi-layers and modulate them simultaneously. Sparse spreading code is also applied in partially superposing the modulated symbols on several resource blocks. Furthermore, we analyzed the energy efficiency of proposed NOMM scheme and traditional M-QAM theoretically. It was also validated by simulation results that NOMM provides less power consumption than M-QAM modulation.

A pilot contamination elimination (PCE) precoding was recently proposed for multi-cell massive MIMO systems with distributed antenna subarrays, where the number of subarrays is required to be not less than the number of users. In this paper, we consider practical scenario of insufficient number of subarrays, and propose a corresponding PCE precoding scheme. The key idea of the proposed scheme is to utilize multiple pilots in one cell rather than single pilot in the original scheme. It is shown that by separating users into multiple groups according to the associated pilots, multiple downsized equivalent MIMO channels can be obtained, which linearly reduces the required number of subarrays and therefore enable the PCE precoding. This linear relation based interpretation also introduces a new optimization problem of finding subarray power normalization factors to maximize signal-to-noise-plus-interference ratio, where an optimized solution is proposed. Finally, simulation results confirm the validity of the proposed PCE precoding scheme.

In this paper, we study the impact of clock timing mismatch of analog-to-digital converter in a receiver that employs quadrature bandpass sampling of the radio frequency (RF) signal. It is shown that the clock timing mismatch introduces not only symbol timing offset but also image interference, that is, the interference between in-phase and quadrature signals. Then, a mismatch compensation scheme and a pilot-aided mismatch estimation method are proposed. We also propose a blind estimation method, where the clock timing mismatch can be estimated from the second order statistics of the sampled sequences. Numerical simulations confirm the validity of the proposed methods, even in the presence of unknown channel.

Level set estimation (LSE) is the process of using noisy observations of an unknown function to estimate the region(s) where the function values lie above a given threshold. It has a wide range of applications in many scientific and engineering areas, such as spectrum sensing or environment monitoring. In this paper, we study the optimum LSE of a time-varying random field under a total power constraint. A sensor performs uniform sampling of the random field and sends the samples to a fusion center, which estimates the level set by using distorted observations of the samples. Under a total power constraint, a higher sampling rate means less energy per sample, which may negatively impact the estimation performance, but also a stronger correlation between adjacent samples, which can improve the estimation accuracy. Thus it is critical to identify the optimum sampling rate that can minimize the LSE error provability. With the help of a Gaussian process (GP) prior model, we first develop an optimum LSE algorithm based on GP regression. The exact analytical LSE error probability of the LSE algorithm is then derived by considering a number of factors, such as the power consumptions of both sensing and transmission, the power constraint of the sensor, the sampling rate, and the probability distributions of the random field. To simplify analysis, we also obtain a closed-form upper bound of the LSE error probability. The optimum sampling rate is identified by using the analytical error probabilities.

In this paper, a circular-shift division multiple access (CSDMA) scheme with oversampling receivers is proposed for multi-carrier multi-user wireless networks. The CSDMA scheme differentiates signals from different users by requiring each user to circularly shift their respective signals by a unique number of time domain samples. At the transmitter, each modulated data symbol is spread onto a subset of orthogonal subcarriers in the frequency domain, and the signals are then circularly shifted in the time domain. At the receiver, the received signals are oversampled in the time domain and then converted into the frequency domain for processing. The combination of circularly shifting at the transmitter and oversampling at the receiver ensures full multipath diversity, and it results in a special signal structure that allows the receiver to extract the signals from different users with zero or a small amount of multiple access interference (MAI). Both analytical and simulation results show that the proposed oversampled CSDMA scheme can achieve significant performance gains over conventional orthogonal frequency division multiple access (OFDMA) with the same spectral efficiency.

We propose a joint independent component analysis (ICA) based equalization and carrier frequency offset (CFO) estimation scheme for orthogonal frequency division multiple access (OFDMA) systems, which requires only a single pilot, resulting in a very low spectral overhead. On the one hand, a low-complexity ambiguity elimination method is proposed in the ICA equalized signals. By designing and exploring a fixed order of correlation in the transmitted signals, the permutation ambiguity problem is solved, while the remaining quadrant ambiguity is eliminated by one pilot symbol. One the another hand, linear CFO estimation is performed with a closed-form solution based on the phase difference between adjacent rows in the CFO-corrupted channel structure. Simulation results show that the proposed semi-blind ICA based scheme not only outperforms some existing CFO estimation approaches, but also provides a bit error rate (BER) performance, comparable to the ideal case with perfect channel state information (CSI) and no CFO.

The cancellation of in-band self-interference caused by simultaneous transmission and reception is an essential issue to realize full-duplex wireless communication. In this paper, we investigate the impact of transmitter IQ imbalance on self-interference cancellation. From our results, it is shown that the residual self-interference in the presence of transmitter IQ imbalance has considerably large power. To cancel it, a baseband cancellation signal generated by widely linear (WL) filtering is proposed and the optimal WL filters are derived. For practical implementation, we also propose an adaptive algorithm based on the augmented complex least mean square method to directly obtain the WL filters. Simulation results demonstrate the validity of the proposed algorithm.

In this paper, a multi-carrier circular-shift division multiple access (MC-CSDMA) scheme is proposed for multiuser wireless communication systems. At the transmitter, each modulated symbol is first spread onto a subset of M subcarriers in the frequency domain through simple repetition codes. The obtained signals are then converted to the time domain, where users circularly shift their respective time domain signals by different numbers of samples. Signals from multiple users are transmitted over the same frequency at the same time. The time domain circular shifting operations render a special signal structure that allows the receiver to extract the signals from different users with zero or a small amount of multiple access interference (MAI). If the number of users is no more than M, which is the number of subcarriers bearing the same modulated symbol, then there are at most L mutually interfering users at any given subcarrier, where L is the length of the equivalent discrete-time frequency selective fading channel. Furthermore, if the number of users is no more than M/L, then MAI-free communications can be achieved. Both analytical and simulation results show that full multipath diversity gain can be achieved by the proposed MC-CSDMA scheme regardless of the presence of MAI, thus it achieves a significant performance gains over conventional orthogonal frequency division multiple access (OFDMA) with the same spectral efficiency.

We propose a low-complexity frequency synchronization approach for semi-blind independent component analysis (ICA) based coordinated multi-point (CoMP) orthogonal frequency division multiplexing (OFDM) systems, with multiple carrier frequency offsets (CFOs). The key idea is to introduce a short pilot for both multi-CFO estimation and ambiguity elimination in the ICA equalized signals. First, by minimizing the cross-correlation between original pilots and received pilots with implicit phase rotation correction, the one-dimensional search based CFO estimation can be performed without trial ICI compensation and channel state information (CSI). Second, by maximizing the real part of the cross-correlation between ICA equalized pilots and original pilots, the same pilots can be used again to eliminate the permutation and quadrant ambiguity in the ICA equalized signals. Simulation results show that, with a very low training overhead of 2%, the proposed multi-CFO estimation approach outperforms existing methods. Also, the proposed semi-blind CoMP system can achieve a bit error rate (BER) performance close to the ideal case with perfect CSI and no CFO.

We propose a semi-blind multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, with a precoding aided carrier frequency offset (CFO) estimation approach, and an independent component analysis (ICA) based equalization structure. A number of reference data sequences are carefully designed offline and are superimposed to source data via a non-redundant linear precoding process, which can kill two birds with one stone, without introducing any extra total transmit power and spectral overhead. First, the reference data sequences are selected from a pool of carefully designed orthogonal sequences. The CFO estimation is to minimize the sum cross-correlation between the CFO compensated signals and the rest orthogonal sequences in the pool. Second, the same reference data enable elimination of the permutation and quadrant ambiguity in the ICA equalized signals by maximizing the cross-correlation between the ICA equalized signals and the reference data. Simulation results show that, without extra bandwidth and power needed, the proposed semi-blind system achieves a bit error rate (BER) performance close to the ideal case with perfect channel state information (CSI) and no CFO. Also, the precoding aided CFO estimation outperforms the constant amplitude zero autocorrelation (CAZAC) sequences based CFO estimation approach, with no spectral overhead.

We propose an orthogonal frequency division multiplexing (OFDM) based semi-blind coordinated multi-point (CoMP) system, with a low complexity estimation approach for multiple carrier frequency offsets (CFOs), and an equalization structure based on the independent component analysis (ICA). Our work is different in that a small number of well-designed pilot symbols are employed to kill two birds with one stone. On the one hand, using the structure of pilots, a complex multi-dimensional search for multiple CFOs is divided into a series of low-complexity mono-dimensional searches. On the other hand, the cross-correlation between the transmitted and the received pilot symbols is explored to allow elimination of the remaining ambiguity in the ICA equalized signals. Simulation results show that with a low training overhead of 3.1%, the proposed semi-blind system can achieve a bit error rate (BER) performance close to the ideal case with perfect channel state information (CSI) and no CFO at the receiver.

This paper considers the classification of digital modulation schemes in the presence of multipath fading channels and additive noise. A novel modulation recognition approach is proposed based on Gaussian Mixture Models (GMM). Our basic procedure involves parameter estimation using GMM to set up an offline database and then to classify the received signal into different modulation schemes based on the database by using Kullback-Leibler (K-L) Divergence. In order to mitigate the negative impact from multipath fading channels, an iterative Maximum A Posteriori (MAP)-based channel estimation is used in conjunction with the Expectation-Maximization (EM) algorithm. Furthermore, Gaussian approximation is carried out to decrease the computational complexity. Monte Carlo simulations are conducted to evaluate the performance of individual modulation scheme classification. Numerical results show that the proposed approach is capable of recognizing various modulated signals with improved performance under AWGN and multipath fading channels.

The paper proposes a novel estimation of carrier frequency offset (CFO) and I/Q imbalance, based on the popular repeated preamble. By investigating the nonlinear least squares cost function of CFO estimation, we discover a general relation among three arbitrary pilot symbols and derive several useful linear equations. Then, we propose a corresponding linear least squares estimation of unsigned CFO with explicit closed-form solution. Using the tentative CFO estimate, the I/Q imbalance can be estimated with sign ambiguity. Notice that the taps of the FIR filter representing frequency-dependent imbalance in practical frond-ends are constrained, we also propose a novel computation-free detection method to solve the sign ambiguity.

This paper proposes a novel time domain blind compensation for frequency-independent and frequency-dependent I/Q imbalance in direct-conversion receivers. Analysis of a difference filter representing the frequency-dependent I/Q imbalance shows that the filter taps are tightly constrained by analog front-end image attenuation (IA). For practical front-ends with reasonable IA, it is proved that the difference filter is minimum phase. We also show that the magnitude response of the difference filter can be obtained from the branch signal autocorrelations. Then, the magnitude response can be used to determine the corresponding minimum phase response. Subsequently, from the cross-correlation of the branch signals, we can estimate the frequency-independent phase imbalance. Since the proposed method has an analytical solution, it can be easily implemented. Simulations of various scenarios confirm its validity and its superior compensation performance.

A novel blind frequency-dependent I/Q imbalance compensation for direct-conversion receivers is proposed. By analyzing the filter which represents branch mismatches, we prove its minimum phase response, for practical front-ends with reasonable image attenuation quality. We also reveal that the received branch signals will have the same autocorrelation in the absence of frequency-dependent imbalance. The proposed method is to obtain the difference filter's magnitude response from the branch signals autocorrelations, and uniquely determine the corresponding minimum phase response afterwards. Then, the frequency-independent imbalance can be estimated subsequently from the branch signals cross-correlation. The proposed method has an analytical solution therefore can be easily implemented. Simulations confirm its superior performance in various I/Q imbalance scenarios.

In this work, a genetic algorithm (GA) based frequency domain equalization (FDE) scheme referred to as FDE-GA, which does not require any guard interval (GI),is proposed for direct sequence-ultra wideband (DS-UWB) wireless communication systems and is shown to significantly outperform the RAKE receiver. The proposed FDE-GA receiver also has a dramatic complexity reduction over the previous RAKE-GA receiver, while achieving a comparable bit error rate (BER) performance. The FDE-GA structure achieves a much higher bandwidth efficiency than conventional FDE methods, because the inter-block-interference (IBI), as a result of the absence of the GI, is removed effectively within each block before the GA.

The estimation of carrier frequency offset (CFO) and DC offset is a crucial issue in an OFDM direct-conversion receiver. Recently, a blind CFO estimator based on the correlation of the received OFDM samples has been proposed. In this paper, this symbol-wise CFO estimator is extended to consider the presence of DC offset. Assuming a constant DC offset during one OFDM symbol period, we can roughly estimate the DC offset by averaging the whole symbol, and then removing it to perform the CFO estimation. Also, by rewriting the signals in matrix forms, we propose a new cost function which can be used to estimate the CFO free of DC offset. The validity of the proposed estimators is confirmed by simulations.

The authors propose an optimal resource allocation (RA) scheme for wireless multi-destination relay systems with orthogonal frequency division multiplexing (OFDM) modulation. The authors work is different in that they consider a practical downlink OFDM-based relay network, where a relay is used to help the source (base station) communicate with multiple out-of-range destinations (users), rather than a single destination (user) as assumed in most previous work. The proposed optimal RA scheme consists of subchannel allocation (SA), subchannel pairing and joint source–relay power allocation (PA). In particular, it is shown that both SA and joint source–relay PA play a dominant role in the three steps of RA. It is also demonstrated that the shorter the distance between the source and the relay, the more impact of multiuser diversity on the system capacity. By employing SA and joint source–relay PA only, a near-optimal performance can be achieved. This work can be extended to the multi-hop, multi-relay and multi-destination uplink/downlink OFDM system.

Recent development in cognitive radio (CR) communications brings significant technical challenges in the design of adaptive and robust transmission techniques in hostile communication environments due to the dynamic channel transitions and strong co-channel interference. A novel adaptive Orthogonal Frequency Division Multiplexing (OFDM) system with a precoded cyclic prefix (PCP) is proposed in this paper to provide a dynamic CR communication platform with fast transmitter-receiver interaction capability. Besides the basic function as a guard interval for the OFDM system, the PCP, combined from two oversampled precoded Kasami sequences, provides an efficient way of sending the system parameters without the requirement of additional signaling or feedback channels. Overall system and network efficiency is substantially improved due to the simplification of the preambles and handshaking signaling required when there is any change in the CR transmission parameters. Meanwhile, the real part of the PCP is uniquely assigned to each CR transceiver as identification label for spectrum sensing. The receiver design particularly the hybrid domain equalization and PCP induced interference cancellation for the proposed OFDM system is presented in this paper. Implementation related issues including exploitation of the PCP structure and complexity reduction are investigated. The performance of the proposed PCP-OFDM system is analyzed and verified through numerical simulations.

In this paper, we propose a novel equalization method for OFDM systems without guard interval (GI). The performance of GI-free OFDM system is usually limited by inter-symbol interference (ISI) and inter-carrier interference (ICI). The proposed method is to firstly detect the tail part of transmitted OFDM symbol, then eliminate the ISI and ICI to recover the whole symbol. We find that the tail part can be obtained free of ISI and ICI, after frequency domain equalization of a proper channel, where proper means that the channel inverse is or can be approximated as FIR filter with suitable length. Since a minimum phase channel is usually proper, we also incorporate a time-domain equalizer to convert non-minimum phase channel into minimum-phase. The validity of the proposed method is confirmed by simulations.

This paper investigates carrier frequency offset (CFO) estimation and inphase/quadrature (I/Q) imbalance compensation in time-varying frequency-selective channels. We first propose a linear least squares (LLS) CFO estimation approach which has a lower complexity and a higher accuracy than the previous nonlinear CFO estimation methods. We then propose a Kalman filtering based I/Q imbalance compensation approach in the presence of CFO, which demonstrates a good ability to track the channel time variations with a fast convergence speed, by nulling the cyclic prefix (CP) and including the CFO in the state vector of the equivalent channel model. The proposed Kalman filtering based I/Q imbalance compensation approach with associated equalization tracks the time variation with a fast convergence speed. Simulation results show that the proposed compensation approach for CFO and I/Q imbalance provides a bit error rate (BER) performance close to the ideal case with perfect channel state information (CSI), no CFO and no I/Q imbalance.

We proposed a precoded in-band pilots design for OFDM signal (PIP-OFDM) in [1]. With a special design, user identification in cognitive network is easily achieved by demodulating unknown information on identification pilot subcarriers embedded in the transmitted PIP-OFDM signal. In this paper, we discuss synchronization issues of PIP-OFDM system. Different from conventional OFDM system, synchronization in PIP-OFDM system is realized at low SNR. Benefit from the redundant information on pilots, performance of synchronization in PIP-OFDM system is greatly improved by processing multiple OFDM symbols. Coarse timing and frequency synchronization are briefly introduced using cyclic prefix correlation and pilots' energy correlation in frequency domain. Fine timing and frequency synchronization are realized with modified maximum likelihood estimator based on phase shift estimation in frequency and new method using the similar principle, respectively. System performance is evaluated by theoretical formulation and computer simulations in terms of mean square error. Results show that synchronization algorithms in this paper work effectively at low SNR from -5dB to 5dB.

We propose a novel pilot-aided compensation scheme for carrier frequency offset (CFO) and I/Q imbalance. The proposed scheme comprises a generalized periodic pilot and a low-complexity acquisition algorithm, where the CFO and the coefficients for I/Q imbalance compensation can be obtained in explicit closed-form.

We propose a novel semi-blind compensation scheme for both frequency-dependent and frequencyindependent I/Q imbalance based on independent component analysis (ICA) in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, where ICA is applied to compensate for I/Q imbalance and equalize the received signal jointly, without any spectral overhead. A reference signal is embedded in the transmitted signal with little power consumption and no spectral overhead introduced, to enable ambiguity elimination for the ICA output signal at the receiver. Moreover, channel interpolation is incorporated with layered space frequency equalization (LSFE) to enhance the system performance. Simulation results show that the proposed implicit compensation scheme can not only provide a better bit error rate (BER) performance and a higher bandwidth efficiency than the previous training based I/Q imbalance compensation method, but also outperform the ideal case with perfect channel state information (CSI) and no I/Q imbalance, due to additional frequency diversity.

We propose an adaptive cross-layer design for the downlink multiuser orthogonal frequency division multiplexing (OFDM) systems, to maximize the weighted sum capacity of all users, where each user has multiple heterogeneous traffic queues simultaneously. A packet dependent (PD) scheduling scheme is employed at the medium access control (MAC) layer, which determines the packet transmission order by assigning different weights to different packets, and is shown by simulations more efficient than the previous methods where all packets in a queue have the same weight. The weight design in PD scheduling considers the delay, size and quality of service (QoS) priority level of packets. Each user weight employed in resource allocation at the physical (PHY) layer is obtained by summing up the weights of selected packets for the user. By properly choosing the number of packets used for weight calculation, the proposed cross-layer design requires a much lower overall complexity than the conventional queue based designs. It is also proven to achieve the maximum system stability region. Furthermore, simulation results show that the proposed suboptimal resource allocation algorithm provides a near-optimal performance while requiring a relatively low complexity.

I/Q imbalance and carrier frequency offset (CFO) are two typical radio frequency (RF) circuit analog impairments in wireless communication systems, and degrade the system performance severely. In this paper, we propose a novel Kalman filtering based compensation scheme for I/Q imbalance and CFO in time-varying MIMO OFDM systems. To circumvent CFO and track time variations of wireless communication channels, the CFO is absorbed into the state vector of the equivalent channel model. Moreover, the inter-carrier interference (ICI) caused by phase shift of the cyclic prefix (CP) in the equivalent system is removed by decision feedback filtering, which allows low complexity compensation on each subcarrier independently. Simulation results show that the proposed approach can compensate for I/Q imbalance and CFO effectively and is robust again time variations.

We propose the optimal resource allocation for wireless multi-destination relay systems with orthogonal frequency division multiplexing (OFDM) modulation, which includes subchannel allocation (SA), subchannel pairing (SP), and joint source-relay power allocation (PA). Our work is different in that we consider a practical downlink OFDM based cooperative network, where the relay is used to help the source (base station) to communicate with multiple destinations (users) rather than a single destination as assumed in the previous work. Simulation results demonstrate that the proposed optimal resource allocation outperforms other resource allocation schemes in terms of the system capacity. This work can be extended to multi-relay multi- destination uplink/downlink OFDM relay systems.

Spectrum sensing and signal identification at low SNR, where synchronization and demodulation are not achievable, have emerged as two of the most critical challenges in cognitive radio network. A precoded in-band pilots design for OFDM signal (PIP-OFDM) is proposed in this paper to simplify the spectrum sensing and signal identification for dynamic spectrum sharing. The actual design of pilot signal in this OFDM system contains two groups of pilot tones, i.e. uniform pilot tones and identification pilot tones. Combining the two groups of pilot tones embedded in the OFDM signal, the spectrum sensing based on detecting the energy on pilot tones achieves an exceptionally good performance. In addition, the unique identification pilot code design for each transmitter in this network makes the signal identification easy and reliable. Theoretical analysis and simulation show that this design works well in spectrum sensing and signal identification at low SNR.

In OFDM systems, since carrier frequency offset (CFO) destroys the orthogonality among subcarriers, whose estimation/compensation is a critical problem. However, the CFO estimation suffers from DC offset (DCO) generated in low-cost direct-conversion receivers (DCRs). More seriously, in general, DCO is time-varying due to the automatic gain control. In this paper, we propose a novel CFO estimator in the presence of time-varying DCO. It is shown the residual DCO after high-pass filtering varies in a linear fashion. Based on this observation and the periodicity of the training sequence, we derive a CFO estimator independent of DCO. Simulation results show the validity of the proposed estimation method.

Sensing of orthogonal frequency division multiplexing (OFDM) signals at low signal-to-noise ratio (SNR) is of great importance for cognitive radio (CR) communications due to the wide applications of OFDM in many existing and evolving broadband wireless communications. In-band pilots, multiplexed with the data-carrying subcarriers, provides one distinct feature of OFDM signals. The objective of this paper is to investigate the application of in-band pilots in OFDM signal spectrum sensing, particularly when synchronization is not achievable at very low SNR. In this paper, the proposed sensing technique is to match the received OFDM signals with a self-defined pilot tone filter. Due to the extremely low requirement on sensing SNR for CR applications, synchronization of the sensing receiver and OFDM signals usually is not achievable. As a result, carrier frequency offset (CFO) and timing offset have to be dealt with during the sensing process. With the hybrid domain signal processing, the proposed sensing technique is very robust to both timing and frequency offset. We also apply the proposed technique to DVB-T to verify our proposal and subsequent analysis. Robust performance with very low false alarm probability and short sensing time were achieved under various channel conditions including Rician and Rayleigh channels, in the presence of random timing offset and CFO.

I/Q imbalance, which is one of the radio frequency (RF) circuit impairments in direct conversion transmitter and receiver, introduces severe performance degradation in wireless communication systems. In this paper, we propose a novel blind compensation algorithm for both frequency-dependent and frequency-independent I/Q imbalance based on independent component analysis (ICA) in multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, where ICA, an efficient higher order statistics (HOS) based blind source separation technique, is applied to compensate for I/Q imbalance and equalize the received signals simultaneously. Moreover, preceding is employed to resolve the ambiguity in the ICA output signals. Simulation results show that the proposed approach can not only compensate for I/Q imbalance effectively, but also achieve frequency diversity gains and outperform the case with perfect channel state information (CSI) and no I/Q imbalance.

We propose a novel subcarrier allocation (SCA) based approach for compensating carrier frequency offset (CFO) and I/Q imbalances in direct-conversion OFDM receivers. From the matrix representation of OFDM signals with CFO and I/Q imbalances, maximum likelihood CFO estimation and the associated SCA are given. Then, an SCA-based algorithm is proposed for the I/Q imbalances compensation. Moreover, the proposed approach is extended to a blind compensation scheme for OFDM signals with asymmetric SCA.

Recently, based on modified periodic pilot (MPP), a compensation scheme for carrier frequency offset (CFO) and I/Q imbalances in direct-conversion wireless receivers, has been proposed. However, it cannot be applied to IEEE 802.11 a WLAN directly, where only periodic pilot (PP) exists. In this paper, the impact of PP on the MPP-based scheme is studied. The results indicate that PP causes fatal degradation of performance, by inducing sign ambiguity in CFO estimation and invalidating the compensation of I/Q imbalances in the absence of CFO. Then, an efficient compensation scheme for the IEEE 802.11 a WLAN is proposed. In the proposed scheme, the CFO estimation is enhanced by a CFO sign detection and an autocorrelation-based estimator. Also, a novel I/Q imbalances compensation algorithm robust to the CFO value is proposed. Taking full advantage of the 802.11 a preamble, the proposed scheme is competent for various scenario of CFO and I/Q imbalances, which is confirmed by simulations.

In orthogonal frequency division multiplexing (OFDM) systems with direct-conversion transceivers, transmission performance is severely degraded by analog impairments, typically, carrier frequency offset, transmitter/receiver I/Q imbalance, and DC offset. The existing research considered only part of these impairments. In this paper, we analyze the OFDM signal corrupted by all these impairments. Then, a hybrid domain compensation scheme consisting of the time-domain and frequency-domain compensation is proposed. For the purpose of practical implementation, we also propose a comparable preamble, with which the compensation coefficients can be obtained by a low- complexity acquisition algorithm.

In direct-conversion orthogonal frequency division multiplexing receivers, the compensation of carrier frequency offset (CFO) and I/Q imbalance is critical. In the existing modified periodic pilot based scheme, I/Q imbalance compensation completely depends on CFO estimation, which however needs a computational expensive one-dimensional search and correct timing synchronization. In this paper, we propose a novel periodic pilot and an associated compensation method. Taking advantage of the pilot's periodicity, the proposed scheme simultaneously estimates the CFO and coefficients of I/Q imbalance compensation, in closed forms based on the linear least squares algorithm. The proposed scheme not only has significantly reduced complexity, but also is inherently robust to timing error. Simulations confirm the validity and superiority of the proposed scheme.

We propose a novel packet batch dependant cross-layer scheduling scheme for the downlink multiuser orthogonal frequency division multiplexing (OFDM) system, which significantly outperforms the existing queue dependant scheduling scheme over a wide range of signal-to-noise (SNR) in terms of the system bandwidth efficiency, best-effort (BE) traffic throughput and quality of service (QoS) traffic delay, while requiring a lower complexity. Our batch dependant scheduling also provides a better tradeoff between the QoS traffic and BE traffic. It is also shown to guarantee the system stability.

In this letter, a blind joint carrier frequency offset and DC offset (DCO) estimator for OFDM systems is proposed. By exploiting the underlying subspace of the received OFDM signals after the time-domain-average based coarse DCO cancellation, the proposed estimator can achieve excellent performance, which is demonstrated by simulations.

Carrier frequency offset (CFO) and I/Q imbalances severely degrade the performance of direct-conversion OFDM receiver. In this paper, based on the subcarrier allocation (SCA) of OFDM signal, a novel approach for compensating CFO and I/Q imbalances is proposed. First, we derive a useful matrix representation of OFDM signal with CFO and I/Q imbalances. Then, maximum likelihood CFO estimation, as well as the associated SCA, are given. With the CFO estimate, a SCA-based algorithm which has a close-form solution, is proposed for the compensation of I/Q imbalances. Furthermore, due to the correspondence between the CFO estimate and the compensation of I/Q imbalances, the proposed approach can be extended to a blind compensation scheme, suitable for OFDM signals with asymmetric SCA. Finally, computer simulations demonstrate the validity of the proposed approach.

In an OFDM receiver with direct-conversion architecture, DC offset (DCO) and carrier frequency offset (CFO) cause a severe performance degradation. Recently, by investigating the subspace of OFDM signal after removing the coarse DCO estimate obtained via a time-domain average, we have proposed a null space based estimator (NSE), for blind CFO and DCO estimation. In this paper, we analyze the cost function of the NSE, and show that the full performance NSE is complexity reducible, which can indeed lead to a common null space based estimator (CNSE). Also, we develop a maximum-likelihood estimator (MLE), and prove that the CNSE is equivalent to the MLE. Furthermore, the Cramer-Rao bound of CFO estimation in the presence of DCO is given. Finally the analysis results are verified by simulations.

In wireless OFDM systems, the coexistence of direct current (DC) offset and carrier frequency offset (CFO) will severely degrade the transmission performance. This problem was only partially solved by pilot-aided approaches, at the expense of bandwidth. In this paper, we analyze the subspace structure of the received OFDM signal after coarse DC offset cancelation by time-domain average. Then, a novel blind estimator, which is able to estimate both CFO and DC offset in the absence of the pilot, is proposed. The validity of the proposed estimator is demonstrated by simulation results.

Carrier frequency offset (CFO) in orthogonal frequency divisional multiplexing (OFDM) system severely degrades the transmission performance. This frequency offset can be divided into an integer part and a fractional part. The fractional part mainly introduces inter-carrier interference (ICI), while the integer part causes frequency ambiguity. In this paper, based on the underlying structure of the received OFDM symbols, a novel blind algorithm is proposed to estimate the integer part of CFO. Compared with the conventional M&M's blind integer CFO estimator, the proposed one has considerably reduced computational load. Furthermore, simulation results show that under the multipath fading channel, the proposed estimator has lower probability of failure than the M&M's estimator

The purpose of the paper is to derive a simplex genetic algorithm (GA) for blind equalization using RBF networks. In order to reduce the computation cost, this algorithm searches the center's elements instead of centers because of the inter-relation between the centers. Furthermore, by introducing the simplex operator into the GA, the proposed algorithm presents a fast convergence characteristic in simulation.

The radial basis function (RBF) network has been used in a digital channel's equalization for its identical structure to the optimal Bayesian Symbol-decision equalizer. The important problem in the equalization using RBF is to find the correct centers. A clustering algorithm has been proposed to find the desired centers. In fact, the problem can be changed to find the center's element set because of the inter-relation between the center's elements. A fast clustering algorithm using the inter-relation for RBF network equalization is proposed. The simulation results show its better convergency and less calculation than the traditional clustering algorithm.