In this work, we consider an integrated sensing and covert communication (ISACC) system with a finite blocklengthLaided by a reconfigurable intelligent surface (RIS) withNelements. Specifically, with the aid of RIS, a transmitter Alice is to sense the potential existence of a target, and she is also probabilistically trying to send information to a receiver Bob covertly (i.e., trying to hide her transmissions from a warden Willie). Meanwhile, Willie is to detect whether Alice is sensing the target only or conducting ISACC. We consider two RIS operation modes, i.e., a non-adaptive mode, where RIS employs a common beamforming vector regardless of whether Alice performs sensing-only or ISACC transmissions, and an adaptive mode, where RIS dynamically switches between two beamforming vectors tailored to the transmission type. For each mode, we formulate and solve an optimization problem that jointly determines Alice’s power allocation fractionρfor covert information signals and RIS beamforming vectors to maximize the effective covert communication throughput, while satisfying sensing and covertness constraints. Our examination shows that in the non-adaptive mode, the optimalρdecreases with the blocklengthL, the number of RIS elementsN, or Alice’s transmit powerPa, reflecting a stronger trade-off between covert throughput and sensing reliability. In contrast, in the adaptive mode, the optimal solution isρ∗ = 1, indicating that Alice can fully rely on RIS adaptivity to conceal covert transmissions by switching its beamforming vectors. Numerical results further demonstrate a significant covert communication throughput gain of the adaptive mode over the non-adaptive mode, which increases with bothNandPa, highlighting the importance of RIS adaptivity in the ISACC systems.

Reliable uncertainty quantification is critical for trustworthy AI. Conformal Prediction (CP) provides prediction sets with distribution-free coverage guarantees, but its two main variants face complementary limitations. Split CP (SCP) suffers from data inefficiency due to dataset partitioning, while full CP (FCP) improves data efficiency at the cost of prohibitive retraining complexity. Recent approaches based on meta-learning or in-context learning (ICL) partially mitigate these drawbacks. However, they rely on training procedures not specifically tailored to CP, which may yield large prediction sets. We introduce an efficient FCP framework, termed enhanced ICL-based FCP (E-ICL+FCP), which employs a permutation-invariant Transformer-based ICL model trained with a CP-aware loss. By simulating the multiple retrained models required by FCP without actual retraining, E-ICL+FCP preserves coverage while markedly reducing both inefficiency and computational overhead. Experiments on synthetic and real tasks demonstrate that E-ICL+FCP attains superior efficiency-coverage trade-offs compared to existing SCP and FCP baselines.

The problem of bistatic integrated sensing and communications over memoryless relay channels is considered, where destination concurrently decodes the message sent by the source and estimates unknown parameters from received signals with the help of a relay. A state-dependent discrete memoryless relay channel is considered to model this setup, and the fundamental limits of the communication-sensing performance tradeoff are characterized by the capacity-distortion function. An upper bound on the capacity-distortion function is derived, extending the cut-set bound results to address the sensing operation at the destination. A hybrid-partial-decode-and-compress-forward coding scheme is also proposed to facilitate source-relay cooperation for both message transmission and sensing, establishing a lower bound on the capacity-distortion function. It is found that the hybrid-partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for three specific classes of relay channels. Numerical examples are provided to illustrate the communication-sensing tradeoff and demonstrate the benefits of integrated design.

The deployment of dual-functional communication and sensing base stations (BSs) in cellular networks will transform these networks into extensive sensing systems, enabling various emerging applications such as autonomous driving and smart cities. However, to fully realize these benefits, optimizing transmission and managing interference among different BSs is crucial. In this paper, we consider a cell-free integrated sensing and communication (ISAC) system, where multiple BSs, each equipped with multiple antennas, collaboratively communicate with multiple single-antenna users while jointly estimating the target’s location through signals reflected by the target and received at all BSs. In this context, fully utilizing all links necessitates coordinated transmit beamforming design among BSs to effectively balance communication and sensing performance. To address this challenge, we first characterize the sensing performance by deriving the Cramér-Rao lower bound (CRLB) for target location estimation. We then formulate an optimization problem to design the transmit beamforming vectors at each BS, minimizing the sensing CRLB while meeting communication quality of service constraint for each user. Due to the highly non-convex nature of this problem, we apply a series of transformations to convert it into a more tractable form and develop an iterative algorithm to solve it. Numerical results validate the effectiveness of the proposed design, highlighting its advantages in balancing the trade-off between sensing and communication compared to three benchmark designs.

In this paper, we propose an integrated sensing and communication (ISAC) system enabled by movable antennas (MAs), where the base station (BS) transmitter is equipped with MAs to enhance both sensing and communication performance. To characterize the benefits of MA-enabled ISAC systems, we focus on the line-of-sight (LoS) channel scenario and derive the Cramér-Rao bound (CRB) for angle estimation error, which is then minimized by jointly optimizing the antenna position vector (APV) and beamforming design, subject to a pre-defined signal-to-noise ratio (SNR) constraint to ensure the communication performance. Despite the non-convexity of the resulting problem, we develop a boundary traversal breadth-first search (BT-BFS) algorithm to obtain the global optimal solution, along with a lower-complexity boundary traversal depth-first search (BT-DFS) algorithm to find a local optimal solution efficiently. Extensive numerical results are presented to verify the effectiveness of the proposed algorithms, and demonstrate the superiority of the considered MA-enabled ISAC system over conventional ISAC systems with fixed-position antennas (FPAs).

Affine Frequency Division Multiplexing (AFDM) is an emerging multicarrier modulation technique well-suited for high-mobility scenarios in next-generation wireless systems. By achieving delay-Doppler orthogonality in a twisted time-frequency domain, AFDM offers robust and efficient communication while enabling integrated sensing and communication through precise environmental parameter estimation. However, a major drawback of AFDM is its high peak-to-average power ratio (PAPR), which can cause power amplifier saturation and degrade system reliability. To address this issue, we propose an autoencoder-based PAPR reduction scheme leveraging convolutional neural networks, which effectively lowers the PAPR while maintaining satisfactory bit error rate (BER) performance. Simulation results under various modulation schemes and linear time-varying channels demonstrate that the proposed approach outperforms conventional methods in both PAPR reduction and BER performance.

This work investigates the potential of exploiting movable antennas (MAs) to enhance the performance of a multiuser downlink integrated sensing and communication (ISAC) system. Specifically, we formulate an optimization problem to maximize the transmit beampattern gain for sensing while simultaneously meeting each user's communication requirement by jointly optimizing antenna positions and beamforming design. The problem formulated is highly non-convex and involves multivariate-coupled constraints. To address these challenges, we introduce a series of auxiliary random variables and transform the original problem into an augmented Lagrangian problem. A double-loop algorithm based on a penalty dual decomposition framework is then developed to solve the problem. Numerical results validate the effectiveness of the proposed design, demonstrating its superiority over MA designs based on successive convex approximation optimization and other baseline approaches in ISAC systems. The results also highlight the advantages of MAs in achieving better sensing performance and improved beam control, especially for sparse arrays with large apertures.

Integrated Sensing and Communication (ISAC) technology facilitates simultaneous reliable communication and high-precision sensing performance in vehicular networks. Many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Aiming to minimize the Cramér-Rao bound (CRB) for estimating vehicle's angle, distance and velocity while meeting communication rate requirements, we propose a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the superiority of the proposed algorithm and scheme.

The Wave Relay network represents an innovative large-scale wireless ad hoc network and has typical applications in tactical communication scenarios. This paper proposes an enhanced carrier sense multiple access with collision avoidance (CSMA/CA) strategy, integrated with multi-agent reinforcement learning (MARL), to improve network performance, focusing on communication latency and throughput. The state space, action space, and reward function are carefully designed to enable each agent to perform distributed deep Q-learning. This approach allows for adaptive and dynamic adjustments to the backoff parameters of the CSMA/CA strategy. Simulation results demonstrate that the proposed scheme effectively optimizes communication latency and throughput of the Wave Relay network.

In this article, we propose a movable antenna (MA)-enabled frequency-hopping (FH) multiple-input-multiple-output (MIMO) radar system and investigate its sensing resolution. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal antenna distribution to achieve the minimum main lobe width in the angular domain is characterized. We discover that this minimum width is related to the antenna size, the antenna number, and the target angle. Meanwhile, we present lower bounds of the ambiguity function in the Doppler and delay domains, and show that the impact of the antenna size on the radar performance in these two domains is very different from that in the angular domain. Moreover, the performance enhancement brought by MAs exhibits a certain tradeoff between the main lobe width and the side lobe peak levels. Therefore, we propose to balance between minimizing the side lobe levels and narrowing the main lobe of the ambiguity function by optimizing the antenna positions. To achieve this goal, we propose a low-complexity algorithm based on the Rosen’s gradient projection method, and show that its performance is very close to the baseline. Simulation results are presented to validate the theoretical analysis on the properties of the ambiguity function, and demonstrate that MAs can reduce the main lobe width and suppress the side lobe levels of the ambiguity function, thereby enhancing radar performance.

The concept of extremely large-scale reconfigurable intelligent surfaces (XL-RIS) holds great promise for enabling sixth-generation (6G) communications. However, the vast number of passive reflection coefficients and the transition from far-field to near-field electromagnetic radiation pose significant challenges for channel estimation, especially under tight pilot overhead constraints. To address these challenges, we propose a novel hybrid integrated sensing and communication architecture and a three-stage channel estimation scheme for XL-RIS-assisted millimeter wave communication systems. The proposed scheme leverages user position data, obtained through a sensing module, to accurately estimate near-field cascaded channels. First, we design an integrated base station architecture that combines a fully-digital sensing module with a hybrid communication module to achieve high-resolution distance and angle estimations using linear frequency modulation signals. Next, we introduce a distance-error-minimization based localization algorithm to effectively estimate user coordinates. To balance channel estimation performance and pilot overhead, we carefully select the appropriate number of position update iterations. Using these estimated coordinates, we calculate the channel fading coefficients for the near-field cascaded channels, facilitating accurate channel estimation. Simulation results validate the effectiveness of our proposed scheme, demonstrating reduced overhead while maintaining superior channel estimation performance.

In millimeter-wave extremely large-scale multiple-input multiple-output (XL-MIMO) communications, fast and reliable beam alignment is essential for maximizing beamforming gain during data transmission. As XL-MIMO systems integrate increasingly larger antenna arrays, the electromagnetic propagation field transitions from the far-field to the near-field regime. This shift requires beam training methods that account for both angular and distance dimensions. Although recent advancements have been made in near-field beam training, existing approaches still face challenges, including high training overhead and limited robustness. The advent of advanced wireless systems has enabled the acquisition of user position information through various techniques, presenting opportunities to reduce training overhead in near-field scenarios. In this paper, we propose an adaptive Position-Aware Beam Training (PABT) algorithm designed for near-field XL-MIMO systems. The PABT algorithm explicitly incorporates position errors into its design to enhance robustness. Specifically, it begins with an initialization phase that uses noisy position estimates to narrow the beam search space. Following this, a multi-round iterative measurement approach is employed, where Bayes’ rule is used to update the posterior distribution of the polar-domain codebook based on the measurements obtained. This approach dynamically refines the candidate beam until a predefined probability threshold or a maximum number of iterations is reached. For the PABT proposed, we also derive a theoretical lower bound on the training overhead required to achieve a given probability threshold. This analysis provides valuable insights for selecting the key algorithm parameters in subsequent simulations. Numerical evaluations validate the proposed PABT algorithm, showing significant improvements in normalized beamforming gain and achievable spectral efficiency compared to baseline methods.

Recent advancements in integrated sensing and communication (ISAC) technology offer significant potential for high-resolution localization and high-throughput communication in vehicular networks. However, many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. Additionally, the limited transmit power of roadside units (RSUs) and the small radar cross section (RCS) of vehicles can result in weak echo signals, hindering effective vehicle detection and tracking. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Our approach optimizes both RSU beamforming and IOS configuration (including refraction and reflection amplitudes and phase shifts) to minimize the Cramér-Rao bound (CRB) while meeting communication rate requirements. Solving this optimization problem is challenging due to the complex variable-coupling and the implicit CRB expression. To overcome these difficulties, we present a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the effectiveness of the proposed ZO-IPDD algorithm and the superior performance of the tracking scheme compared to existing methods.

Hybrid beamforming is vital in modern wireless systems, especially for massive MIMO and millimeter-wave (mmWave) deployments, offering efficient directional transmission with reduced hardware complexity. However, effective beamforming in multi-user scenarios relies heavily on accurate channel state information, the acquisition of which often requires significant pilot overhead, degrading system performance. To address this and inspired by the spatial congruence between sub-6GHz (sub-6G) and mmWave channels, we propose a Sub-6G information Aided Multi-User Hybrid Beamforming (SA-MUHBF) framework, avoiding excessive use of pilots at mmWave. SA-MUHBF employs a convolutional neural network to predict mmWave beamspace from sub-6G channel estimate, followed by a novel multi-layer graph neural network for analog beam selection and a linear minimum mean-square error algorithm for digital beamforming. Numerical results demonstrate that SA-MUHBF efficiently predicts the mmWave beamspace representation and achieves superior spectrum efficiency over state-of-the-art benchmarks. Moreover, SA-MUHBF demonstrates robust performance across varied sub-6G system configurations and exhibits strong generalization to unseen scenarios.

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results.

Integrated sensing and communication (ISAC) emerges as a critical technology for future 6G cellular networks. The distinct nature of sensing-centric and communication-centric waveforms poses a challenge, as a unified design can lead to a performance tradeoff between sensing and communication. Most of the previous studies focused on single ISAC base station (BS) scenarios and explored capacity-distortion tradeoffs for various ISAC channels. However, practical scenarios involve multiple ISAC BSs in the same region, sharing time-frequency resources and causing interference, which impacts both sensing and communication functions. To address this challenge, we propose an information-theoretic modeling of monostatic ISAC over interference channels. In the model, two interfering ISAC BSs seek to communicate with their users while performing sensing estimation through received echo signals. An achievable scheme is developed for the considered model, utilizing superposition coding and joint compression of past transmitted codewords and echo signals via distributed Wyner-Ziv coding. The corresponding achievable rate-distortion region is characterized, and a specific example is provided to demonstrate the advantages of the proposed scheme. Our results highlight that interference links, in conjunction with echo signals, can be strategically leveraged to achieve unified cooperative sensing and communication between the two BS-user pairs.

A relay-aided bistatic integrated sensing and communication (ISAC) system is considered, where the destination concurrently decodes a message and estimates unknown state parameters from its received signals. This system is modeled by a generalized state-dependent relay channel. Its fundamental limits of the communication-sensing performance tradeoff, characterized by the capacity-distortion function, are established. Specifically, an upper bound on the capacity-distortion function extending the cutset bound for relay channels to address the state estimation at the destination is developed. Additionally, a hybrid partial-decode-and-compress-forward coding scheme is proposed to facilitate source-relay cooperation for both message transmission and state estimation, establishing a lower bound on the capacity-distortion function. It is found that partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for some special classes of channels. Two numerical examples are provided to illustrate the communication-sensing tradeoff in the considered ISAC system.

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a backhaul-limited multiple access channel (MAC) for cooperative localization of the target. Different from schemes on only information domain or signal domain cooperation, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the backhaul resource allocation, together with a greedy strategy for node selection. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to outperform the baseline schemes significantly.

The orthogonal time frequency space (OTFS), a recently introduced two-dimensional modulation in the delayDoppler domain, holds great promise for high-mobility communications. OTFS-based millimeter-wave systems are gaining attraction due to their ability to offer a larger bandwidth and increased robustness against Doppler spread. However, the presence of phase noise (PHN) from high-frequency oscillators can lead to severe inter-carrier interference in OTFS. In this paper, we present a new approach that addresses the challenge of PHN in millimeter-wave OTFS systems by introducing a joint design of data detection and PHN estimation, which are performed iteratively to facilitate the exchange of the posterior information between the two components. Specifically, a lowcomplexity expectation propagation algorithm is developed for data detection, where the posterior information is updated in an inverse-free way based on the minorization-maximization method. As for the PHN estimation, a belief propagation algorithm is applied for message passing on a factor graph. Simulation results demonstrate the superior performance of our proposed design, showcasing faster convergence and substantial gains compared to existing OTFS detectors. Moreover, our approach also exhibits robustness against PHN, highlighting its effectiveness in practical scenarios.

Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is able to perform reflection and refraction of the incident signals simultaneously, making it a promising technology for omnidirectional localization. In this work, we study a STAR-RIS based millimeter-wave (mmWave) localization system in the near field. In particular, we exploit the multipath components (MPCs) as signals emitted from a virtual STAR-RIS and propose a multi-stage localization algorithm. Specifically, in the initialization stage, we present a practical two-step localization method to obtain coarse estimates of UE positions, based on the second-order Fresnel approximation of the near-field channels. In the optimization stage, to effectively leverage the MPCs for localization performance improvement, we propose to add signal weights to the received signals. Then, the signal weights, STAR-RIS energy splitting (ES) coefficients and phase shifts are jointly optimized to minimize the Cramér-Rao lower bound (CRLB). Finally, in the refinement stage, the localization accuracy is further improved based on the information obtained during the first two stages. Simulation results demonstrate the effectiveness of the proposed multipath assisted localization algorithm and show that STAR-RIS surpasses conventional RIS in the omnidirectional localization scenario.

In Millimeter-wave extremely large-scale multiple-input multiple-output (XL-MIMO) communications, fast and reliable alignment of transceiver beams is essential for optimizing beamforming gain during data transmission. As XL-MIMO systems feature increasingly large antenna arrays, the electromagnetic propagation field shifts from the far field to the near field. In addition to angular domain beam training, near-field scenarios require distance domain beam training. However, many existing near-field beam training methods are essentially adaptations of far-field techniques, leading to issues such as excessive training overhead and suboptimal alignment. This paper introduces a position-aware beam training algorithm. In the initialization phase, position information is leveraged to reduce the beam search space. Subsequently, a multi-round iterative measurement strategy is employed. This approach updates the polar-domain codebook's posterior distribution using Bayes' rule based on measurements and dynamically adjusts the candidate beam set until the iteration termination condition is met. Numerical evaluations confirm the superiority of our proposed position-aware beam training algorithm, demonstrating significant enhancements in both normalized beamforming gain and achievable spectrum efficiency.

Traditional millimeter-wave beam selection or prediction algorithms typically rely on beam scanning measurements at the transceivers, incurring substantial training overhead and exhibiting limited adaptability in diverse environments. Recent efforts have aimed to mitigate these challenges by incorporating sensing information, thereby reducing or eliminating the need for extensive beam training. However, existing works predominantly concentrate on exploiting a single sensing modality and often overlook the potential benefits of utilizing historical sensing information. In this paper, we introduce an intelligent beam prediction framework that leverages a deep integration of multimodal sensing data, encompassing GPS, camera, radar, and LiDAR data. The design proposed involves the application of customized deep neural networks to extract features from camera, radar, and LiDAR data. These extracted features, combined with user position and selected beam index, are concatenated to form an aggregated feature vector at each time instance. Subsequently, a time series of these concatenated feature vectors is utilized to exploit temporal correlation for beam prediction through a dedicated long short-term memory network module. Numerical simulations confirm the effectiveness of the proposed design and its superiority over several considered state-of-the-art baselines.

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly improve the channel estimation (CE) and target/scatterer/user sensing performance in the considered system, we propose a two-phase transmission scheme, where the coarse and refined CE/sensing results are respectively obtained in the first and second phases. Particularly, in each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is devised to solve the considered joint sensing and CE problem. The proposed algorithm incorporates the partial overlapping structured (POS) sparsity between the sensing and communication channels to improve the performance. Simulation results are provided to verify the superiority of the proposed algorithm.

Optimizing beamforming is crucial in mitigating pronounced propagation loss and ensuring reliable communication at millimeter-wave (mmWave) frequencies. Traditional beam optimization methods rely on either precise channel estimation or extensive beam training in the mm Wave band, both of which entail substantial pilot overhead. To alleviate this overhead, we leverage the spatial congruence between sub-6 GHz (sub-6G) and mm Wave channels and propose a sub-6G information and few pilots aided beam prediction network (SPBPNet) through deep learning. Specifically, the proposed SPBPNet comprises two cascaded modules: i) the angular information extraction module, which extracts angular features from the available sub-6G channel estimate and maps them to a minimal set of narrow beam directions to be measured in the mm Wave band; and ii) the beam prediction module, which takes limited beam training along the selected directions and then fuses measurements in the mm Wave band with the sub-6G channel information to generate mmWave beam predictions. Numerical results demonstrate that SPBPNet efficiently maps sub-6G channel estimates to mmWave beams and achieves a superior balance between performance and pilot overhead compared to state-of-the-art benchmarks. Moreover, SPBPNet exhibits resilience to varying sub-6G channel estimates at different signal-to-noise ratio levels.

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cramér-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.

Integrated sensing and communication (ISAC) is a promising technology for future 6G networks that enables the joint utilization of hardware and spectrum resources for sensing and communication systems. However, the co-sharing of resources leads to a fundamental tradeoff between sensing and communication performance, which is not well understood in multiple-access ISAC scenarios with perfect or imperfect channel state information at the receiver (CSIR). In this paper, we address this challenge by considering a state-dependent multiple access channel model that accounts for correlated sensing and channel states, as well as imperfect CSIR. We propose an achievable scheme that combines message cooperation and joint compression via distributed Wyner-Ziv coding at each user, resulting in unified cooperative communication and sensing. Our scheme always achieves a communication-rate-distortion region which includes that achieved by state-of-the-art coding scheme. In addition, a numerical example is provided to demonstrate strict inclusion. It is found that the compressed information not only enhances communication (especially in scenarios with imperfect CSIR) but also improves sensing performance.

In this paper, we combine two-timescale beamforming and multi-IRS aided joint spatial division and multiplexing (JSDM) in a mmWave multiuser system. Specifically, all the users are first divided into different groups and each group is associated with an IRS. Then, we propose a novel two-stage grouping-based randomized beamforming (TS-GRB) scheme, where the analog beamformer is designed based on the statistical CSI (S-CSI) in the first stage, and the short-term digital beamformer at the BS and long-term passive beam pattern control policy at the IRSs are jointly optimized in the second stage with both S-CSI and dimension-reduced effective I-CSI. In particular, in the first stage, the analog beamformer is designed to reduce the inter-group interference (IGI) and effective channel dimension, while in the second stage, a two-timescale randomized joint beamforming (TRJB) algorithm is proposed to maximize the proportional fairness utility (PFU). We show that through two-timescale beamforming, JSDM and proper problem reformulation, the pilot overhead of our TS-GRB scheme is significantly lower than existing schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed TS-GRB scheme.

This paper introduces a cooperative sensing framework designed for integrated sensing and communication cellular networks. The framework comprises one base station (BS) functioning as the sensing transmitter, while several nearby BSs act as sensing receivers. The primary objective is to facilitate cooperative target localization by enabling each receiver to share specific information with a fusion center (FC) over a limited capacity backhaul link. To achieve this goal, we propose an advanced cooperative sensing design that enhances the communication process between the receivers and the FC. Each receiver independently estimates the time delay and the reflecting coefficient associated with the reflected path from the target. Subsequently, each receiver transmits the estimated values and the received signal samples centered around the estimated time delay to the FC. To efficiently quantize the signal samples, a Karhunen-Loève Transform coding scheme is employed. Furthermore, an optimization problem is formulated to allocate backhaul resources for quantizing different samples, improving target localization. Numerical results validate the effectiveness of our proposed advanced design and demonstrate its superiority over a baseline design, where only the locally estimated values are transmitted from each receiver to the FC.

Intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) communication systems have gained considerable attention recently. However, the benefits brought by IRS require the instantaneous channel state information (I-CSI) of the cascaded base station (BS)-IRS and IRS-user channel which is difficult to obtain in practice, especially for the multiuser scenario. To address this issue, in this paper, we combine two-timescale beamforming and multi-IRS aided joint spatial division and multiplexing (JSDM) in a mmWave multiuser system. Specifically, all the users are first divided into different groups and each group is associated with an IRS. Then, we propose a novel two-stage grouping-based randomized beamforming (TS-GRB) scheme, where the analog beamformer is designed based on the statistical CSI (S-CSI) in the first stage, and the short-term digital beamformer at the BS and long-term passive beam pattern control policy at the IRSs are jointly optimized in the second stage with both S-CSI and dimension-reduced effective I-CSI. In particular, in the first stage, the analog beamformer is designed to reduce the inter-group interference (IGI) and effective channel dimension, while in the second stage, a two-timescale randomized joint beamforming (TRJB) algorithm is proposed to maximize the proportional fairness utility (PFU). We show that through two-timescale beamforming, JSDM and proper problem reformulation, the pilot overhead of our TS-GRB scheme is significantly lower than existing schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed TS-GRB scheme.

In this work, we investigate the status packet covert communication with time uncertainty in time-critical wireless networks. We model the packet generation as a Poisson process that concatenates the information timeliness and communication covertness, since the prior transmission probability is highly correlated with the packet generation. To balance between the timeliness and covertness of the status packet transmission, we propose two schemes, named random sub-slot selection (RSS) scheme and random channel use selection (RCUS) scheme, by exploiting the random transmission time to confuse a warden Willie’s binary detection on the covert communication. Subsequently, the average age of information (AoI) subject to the covertness constraint is derived for the proposed RSS and RCUS schemes. It is demonstrated that the symbol-length of the status packet introduces a non-trivial tradeoff between the covertness and timeliness. Inspired by this, further designs of the symbol-length and the transmit power for the proposed two schemes are formulated as optimization problems and solved optimally. Our numerical results demonstrate the superiority of proposed schemes over the existing covert communication strategies. In addition, our examination reveals that the RCUS scheme outperforms the RSS scheme when the covertness constraint is extremely strict. Otherwise, the RSS scheme generally outperforms the RCUS scheme in terms of achieving a lower AoI.

In millimeter-wave (mmWave) communication, accurate acquisition of channel state information is vital in enabling reliable beamforming transmission of multiple data-streams in both single-user and multi-user scenarios. Previously, compressive sensing (CS) techniques have been widely exploited for channel estimation purpose, thanks to the sparse nature of mmWave channels. However, most of CS schemes are non-adaptive and show poor estimation performance when the signal-to-noise ratio is low. Enhanced CS-based schemes, such as multi-stage adaptive CS scheme have also been proposed, yet the achievable performance is still unsatisfactory under multi-path environments. In this paper, we advance the existing studies and propose an improved two-stage adaptive channel estimation (TSACE) scheme. In the first stage, a certain number of random sensing measurements are performed over the entire space. Given the initial measurements, the posterior probability distribution of possible combinations of angular supports for the sparse channel are computed. In the second stage, adapting to the posterior information obtained, narrow beams are then deployed to probe a small set of most likely angular directions, based on which the channel estimation is performed. Numerical results validate the effectiveness of TSACE proposed and confirm its performance gain over a few CS baselines subject to the same total average number of measurements under multi-path environments.

Hardware impairments, such as power amplifier (PA) nonlinear distortion, present as the key source to system performance degradation in millimeter-wave (mmWave) communications. In this paper, we consider a mmWave massive MIMO downlink system with nonlinear PA at each transmit antenna and investigate the design of distortion-aware beamforming for efficient data transmission. In particular, we formulate a sumrate optimization problem that accounts for the characteristics of PA distortion. Rather than directly solving the original highly non-convex sum-rate optimization problem, we embrace the classic weighted-minimum-mean-square-error (WMMSE) framework and propose an algorithm to solve its equivalent WMMSE optimization problem for beamforming design. Numerical results confirm the effectiveness of our proposed WMMSE-based algorithm, which outperforms one existing distortion-aware beamforming algorithm and provides significant performance gain compared to conventional beamforming baselines that do not account for the knowledge of the PA distortion.

Millimeter-wave (mmWave) communication has emerged as a promising means for supporting high-rate covert communication. However, the use of antenna arrays with beamforming at mmWave requires precise beam alignment between legitimate parties, and this procedure may entail large beam training overhead and create additional signal leakage to eavesdroppers. In this work, we consider a multi-user mmWave communication system and address the problem of designing proper covert beam training and data transmission between legitimate parties Alice and Bobs, while keeping the underlying communication undetectable from warden Willie. We first propose a novel Covert Multi-user Beam Training Strategy (CMBTS) that adopts multi-finger beam codebook to reduce the probability of communication being detected and to enable simultaneous training for multiple users. With the proposed CMBTS, a joint optimization framework for covert beam training and data transmission with a friendly jammer is developed to maximize the effective covert throughput while ensuring the covertness constraint at warden is met. We further propose an algorithm that combines successive convex approximation and inexact block coordinate descent methods to solve the problem efficiently. Numerical results validate the effectiveness of the CMBTS proposed and confirm its superior performance as compared to several beam training baselines (including exhaustive and hierarchical search) tailored to the covert communication setup considered. Among them, CMBTS achieves the best successful alignment probability and the largest effective covert throughput yet with the least training overhead.

Covert communication prevents legitimate transmission from being detected by a warden while maintaining certain covert rate at the intended user. Prior works have considered the design of covert communication over conventional low-frequency bands, but few works so far have explored the higher-frequency millimeter-wave (mmWave) spectrum. The directional nature of mmWave communication makes it attractive for covert transmission. However, how to establish such directional link in a covert manner in the first place remains as a significant challenge. In this paper, we consider a covert mmWave communication system, where legitimate parties Alice and Bob adopt beam training approach for directional link establishment. Accounting for the training overhead, we develop a new design framework that jointly optimizes beam training duration, training power and data transmission power to maximize the effective throughput of Alice-Bob link while ensuring the covertness constraint at warden Willie is met. We further propose a dual-decomposition successive convex approximation algorithm to solve the problem efficiently. Numerical studies demonstrate interesting tradeoff among the key design parameters considered and also the necessity of joint design of beam training and data transmission for covert mmWave communication.

Millimetre wave (mmWave) beam tracking is a challenging task because tracking algorithms are required to provide consistent high accuracy with low probability of loss of track and minimal overhead. To meet these requirements, we propose in this article a new cost-effective analog beam tracking framework namely Adaptive Tracking with Stochastic Control (ATSC). Under this framework, beam direction updates are made using a novel mechanism based on measurements taken from only two beam directions perturbed from the current data beam. To achieve high tracking accuracy and reliability, we provide a systematic approach to jointly optimise the algorithm parameters. The complete framework includes a method for adapting the tracking rate together with a criterion for realignment (perceived loss of track). ATSC adapts the amount of tracking overhead that matches well to the mobility level, without incurring frequent loss of track, as verified by an extensive set of experiments under both representative statistical channel models as well as realistic urban scenarios simulated by ray-tracing software. In particular, numerical results show that ATSC can track dominant channel directions with high accuracy for vehicles moving at 72 km/hour in complicated urban scenarios, with an overhead of less than 1%.

In this paper, we propose an adaptive beam search algorithm for the initial alignment of millimetre-Wave beams. The proposed algorithm works by gradually deactivating beams that are unlikely the best beam from a pre-synthesised codebook to save overhead, based on a Bayesian probability criterion with a uniform improper prior. The beam deactivations can be implemented with low-complexity operations that require computing a low-degree polynomial or a search through a look-up table. The proposed algorithm does not require prior knowledge of channel statistics or signal to noise ratios (SNRs) to optimise the amount of searching time, and uses a suitable amount of time to achieve satisfactory beam search accuracy in different SNRs and fading scenarios. Numerical results confirm that the proposed algorithm can adapt to a wide range of channels with a fixed algorithm parameter, and can achieve better balance between beam search overhead and accuracy than non-adaptive approaches with fixed overhead.

The statistical priority-based multiple access protocol (SPMA) is a MAC (medium access control) protocol adopted for the Tactical Targeting Network Technology (TTNT), by virtue of high reliability and low latency. In SPMA, in order to keep the network in a favorable loading situation and ensure 99 percent first time success rate for packets of the highest priority, priority thresholds are normally set to fixed values. However, this fixed threshold strategy is not necessarily optimal when the transmission rate at each node varies due to the change of channel condition. To improve the performance, in this paper, we propose an adaptive priority-setting strategy for SPMA, accounting for the change of transmission rates at network nodes. The performance enhancement from the proposed strategy is validated through numerical simulations. In particular, it is shown that higher throughput and lower latency are achieved when the number of nodes supporting high transmission rate increases, while 99 percent first time success rate for the packets of the highest priority is guaranteed when it decreases.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication systems with a large number of antennas are power hungry when using conventional high-resolution analog-to-digital/digital-to-analog converters (A/Ds). To reduce the power consumption of mmWave MIMO systems, existing studies have considered hybrid structures with a reduced number of high-resolution or low-resolution A/Ds at either the transmitter or the receiver side. In this paper, we propose and investigate a multi-user hybrid architecture with low-resolution A/Ds equipped at both the transmitter and the receivers. To mitigate the impact of utilizing low-resolution A/Ds at the transceivers, we propose a novel data transmission scheme, which exploits a weighted phased-array to synthesize the beamforming matrix in the analog domain so as to mitigate inter-user interference. Under the scheme proposed, we derive the achievable rate and the energy efficiency to establish guidelines on the optimal resolution choice of A/Ds for hybrid mmWave systems. For a typical total transmit power at the BS, e.g., 30 dBm, the proposed scheme with 5~6-bit A/Ds can significantly improve the energy efficiency by as much as 100% over that of the conventional hybrid MIMO architecture with high-resolution A/Ds (10-bit A/Ds), without significant degradation in data rate performance.

Millimeter Wave (mmWave) communications rely on highly directional beams to combat severe propagation loss. In this paper, an adaptive beam search algorithm based on spatial scanning, called Iterative Deactivation and Beam Shifting (IDBS), is proposed for mmWave beam alignment. IDBS does not require advance information such as the Signal-to-Noise Ratio (SNR) and channel statistics, and matches the training overhead to the unknown SNR to achieve satisfactory performance. The algorithm works by gradually deactivating beams using a Bayesian probability criterion based on a uniform improper prior, where beam deactivation can be implemented with low-complexity operations that require computing a low-degree polynomial or a search through a look-up table. Numerical results confirm that IDBS adapts to different propagation scenarios such as line-of-sight and non-line-of-sight and to different SNRs. It can achieve better tradeoffs between training overhead and beam alignment accuracy than existing non-adaptive algorithms that have fixed training overheads.

Swift and accurate alignment of transmitter (Tx) and receiver (Rx) beams is one of the fundamental design challenges to support directional transmission in millimeter-wave cellular communications. In this paper, we propose a new Optimized Two-Stage Search (OTSS) algorithm for Tx-Rx beam alignment via beam training. In contrast to one-shot exhaustive search, OTSS judiciously divides the training energy budget into two stages. In the first stage, OTSS explores and trains all candidate Tx-Rx beam pairs and then discards a set of less favorable pairs learned from the measured received signal. In the second stage, OTSS takes an extra measurement for each of the remaining pairs and combines with the previous measurement to determine the best one. For OTSS, we derive fundamental bounds on its misalignment probability under a single-path channel model with ideal codebooks and establish a guideline on its optimized parameter choices. Numerical results have confirmed the advantage of OTSS over the state-of-the-art baselines.

The scheduling in a dense wireless network with interfering links is a very challenging problem, especially in the environments with additional jammers. In this work, we propose a joint jamming detection and link scheduling method based on deep neural networks (DNN). The proposed method admits a branched structure and mainly consists of two subnetworks, where the first subnetwork aims to detect and locate the jammer by utilizing the geographical information and received signal power, while the second one determines the link scheduling with the aid of the previously obtained jamming detection results. Furthermore, inspired by the multi-task learning method, we propose a hybrid-goal training approach to accelerate the training process. Numerical experiments have confirmed that the proposed DNN-based solution can achieve both superior jamming localization accuracy and highly competitive link scheduling performance.

Mobile Ad-hoc Network (MANET) is a self-configuring network that is widely used but vulnerable to the malicious jammers in practice. In this paper, we consider a jamming channel problem in MANET where a jammer intermittently interrupts the communication channels and the transmitter needs to determine which time slot to send data in order to avoid the interruption. Learning from the historical experience, a Deep Q-Network (DQN) based approach is proposed to generate transmission decisions at the transmitter. In addition, a variant of DQN, termed adaptive DQN, is introduced to cope with the change of jamming conditions. The simulation results demonstrate that the proposed scheme can learn an optimal policy to guide the transmitter to avoid jamming more quickly and efficiently than a Q-learning baseline. Moreover, the effectiveness and robustness of the adaptive DQN is also numerically verified.

We consider a Multi-Sender Unicast Index-Coding (MSUIC) problem, where in a broadcast network, multiple senders collaboratively send distinct messages to multiple receivers, each having some subset of the messages a priori. The aim is to find the shortest index code that minimizes the total number of coded bits sent by the senders. In this paper, built on the classic single-sender minrank concept, we develop a new rank-minimization framework for MSUIC that explicitly takes into account the sender message constraints and minimizes the sum of the ranks of encoding matrices subject to the receiver decoding requirements. This framework provides a systematic way to construct multi-sender linear index codes and to study their capability in achieving the shortest index codelength per message length (i.e., the optimal broadcast rate). In particular, we establish the optimal broadcast rate for all critical MSUIC instances with up to four receivers and show that a binary linear index code is optimal for all, except 15 instances with four receivers. We also propose a heuristic algorithm (in lieu of exhaustive search) to solve the rank-minimization problem. The effectiveness of the algorithm is validated by numerical studies of MSUIC instances with four or more receivers.

In this paper, we propose a new coding scheme and establish new bounds on the capacity region for the multi-sender unicast index-coding problem. We revisit existing partitioned distributed composite coding (DCC) proposed by Sadeghi et al. and identify its limitations in the implementation of multi-sender composite coding and in the strategy of sender partitioning. We then propose two new coding components to overcome these limitations and develop a multi-sender cooperative composite coding (CCC). We show that CCC can strictly improve upon partitioned DCC, and is the key to achieve optimality for a number of index-coding instances. The usefulness of CCC and its special cases is illuminated via non-trivial examples, and the capacity region is established for each example. Comparisons between CCC and other non-cooperative schemes in recent works are also provided to further demonstrate the advantage of CCC.

Swift and accurate alignment of transmitter (Tx) and receiver (Rx) beams is a fundamental design challenge to enable the reliable outdoor millimeter-wave communications. In this paper, we propose a new optimized two-stage search (OTSS) algorithm for Tx–Rx beam alignment via spatial scanning. In contrast to one-shot exhaustive search, the OTSS judiciously divides the training energy budget into two stages. In the first stage, OTSS explores and trains all candidate beam pairs and, then, eliminates a set of less favorable pairs learned from the received signal profile. In the second stage, OTSS takes an extra measurement for the each of the survived pairs and combines with the previous measurement to determine the best one. For the OTSS, we derive an upper bound on its misalignment probability, under a single-path channel model with training codebooks having an ideal beam pattern. We also characterize the decay rate function of the upper bound with respect to the training budget and further derive the optimal design parameters of OTSS that maximize the decay rate. OTSS is proved to asymptotically outperform the state-of-the-art beam alignment algorithms and is numerically shown to achieve better performance with limited training budget and practically synthesized beams.

Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM) has been intensively studied, and becomes a very potential candidate in future wireless communication system because of its numerous advantages. This paper presents a framework of Artifical Neural Network (ANN)-aided receiver design for the FBMC system. Specifically, two new joint channel estimation and equalization architectures are developed, which are based on two classical ANN algorithms, Multi-layer Perceptron (MLP) and Functinal Link Artificial Neural Network (FLANN). In addition, a powerful Loss Function (LF) is proposed by combining intrinsic characteristics of FBMC and is applied in the ANN-aided FBMC receiver. Numerical results validate the effectiveness of the proposed ANN-aided design and demonstrate its remarkable bit-error-ratio (BER) performance under multi-path channel environment. Furthermore, the performance advantage of the proposed LF is also confirmed by simulations.

We establish new capacity bounds for the multi-sender unicast index-coding problem. We first revisit existing bounds proposed by Sadeghi et al. and identify the suboptimality of their inner bounds in general. We then present a simplified version of the existing multi-sender maximal-acyclic-induced-subgraph outer bound. For the inner bound, we propose joint link-and-sender partitioning to replace sender partitioning in partitioned Distributed Composite Coding (DCC). This leads to a modified DCC (mDCC) that outperforms partitioned DCC and suffices to achieve optimality for some index-coding instances. We also propose cooperative compression of composite messages in composite coding to exploit messages common to different senders to support larger composite rates than those by point-to-point compression in the existing schemes. We then develop a new multi-sender Cooperative Composite Coding (CCC) scheme. CCC further improves upon mDCC in general, and is instrumental to achieve optimality for a number of index-coding instances.

Infrastructure-less and energy-limited multi-hop ad- hoc networks are vulnerable to the Denial-of-service (DoS) attacks launched by malicious jammers. In this paper, we propose a power allocation scheme with cooperative anti-jamming strategy to enhance communication reliability against jamming in ad-hoc networks under limited resource. In the proposed anti-jamming strategy, multiple relays are employed to assist the transmission in multiple hops. Under this strategy, we propose the optimal power allocation scheme with the objective of minimizing the outage probability at the destination. We decompose the outage minimization problem into two sub-problems and obtain the near-optimal solution of each sub-problem based on the upper bound of the outage probability. Simulation results demonstrate the effectiveness of the cooperative jamming strategy and the power allocation scheme proposed.

In millimeter wave cellular communication, fast and reliable beam alignment via beam training is crucial to harvest sufficient beamforming gain for the subsequent data transmission. In this paper, we establish fundamental limits in beam-alignment performance under both the exhaustive search and the hierarchical search that adopts multi-resolution beamforming codebooks, accounting for time-domain training overhead. Specifically, we derive lower and upper bounds on the probability of misalignment for an arbitrary level in the hierarchical search, based on a single-path channel model. Using the method of large deviations, we characterize the decay rate functions of both bounds and show that the bounds coincide as the training sequence length goes large. We go on to characterize the asymptotic misalignment probability of both the hierarchical and exhaustive search, and show that the latter asymptotically outperforms the former, subject to the same training overhead and codebook resolution. We show via numerical results that this relative performance behavior holds in the non-asymptotic regime. Moreover, the exhaustive search is shown to achieve significantly higher worst case spectrum efficiency than the hierarchical search, when the pre-beamforming signal-to-noise ratio (SNR) is relatively low. This paper hence implies that the exhaustive search is more effective for users situated further from base stations, as they tend to have low SNR.

In this paper, we develop an analytical framework for the initial access (also known as base station (BS) discovery) in a millimeter-wave communication system and propose an effective strategy for transmitting the reference signals (RSs) used for BS discovery. Specifically, by formulating the problem of BS discovery at user equipments (UEs) as hypothesis tests, we derive a detector based on the generalized likelihood ratio test and characterize the statistical behavior of the detector. The theoretical results obtained allow analysis of the impact of key system parameters on the performance of BS discovery, and show that RS transmission with narrow beams may not be helpful in improving the overall BS discovery performance due to the cost of spatial scanning. Using the method of large deviations, we identify the desirable beam pattern that minimizes the average miss-discovery probability of UEs within a targeted detectable region. We then propose to transmit the RS with sequential scanning, using a pre-designed codebook with narrow and/or wide beams to approximate the desirable patterns. The proposed design allows flexible choices of the codebook sizes and the associated beam widths to better approximate the desirable patterns. Numerical results demonstrate the effectiveness of the proposed method.

Coordinated scheduling/beamforming (CS/CB) is a cost-effective coordinated multipoint (CoMP) transmission paradigm that has been incorporated in the recent long-term evolution cellular standard. In this paper, we study CS/CB with the aim of developing low-complexity multicell coordinated user scheduling policies. We focus on a class of multicell interfering broadcast networks in which base stations have only local data and local channel state information, but each has sufficient antennas to serve multiple users using zero-forcing beamforming. The coordination problem is formulated as finding scheduling decisions across the cells such that the network sum rate is maximized. Starting from the two-cell model, we uncover the structure for a good scheduling decision, which in turn leads to the definition of two distributed scheduling policies of differing complexity and intercell coordination. Asymptotic theoretical bounds on the average sum rate are derived to predict the performance of the policies proposed. We extend to some example networks containing more than two cells and develop network-wide coordination policies. Numerical results confirm the effectiveness of the proposed policies and shed light on practical coordinated system design.

We introduce a novel Multi-Carrier Code Division Multiple Access (MC-CDMA) system, where random sparse signatures are deployed in the frequency domain. Data symbols transmitted from base station (BS) to mobile stations (MSs) are drawn from discrete finite alphabets, such as M-QAM constellations. Transmitter-based precoding is introduced so as to allow simple despreading followed by single-user detection at MSs. A power-efficient non-linear precoding optimization problem is formulated by imposing minimum Symbol Error Probability (SEP) targets at MSs. We first elaborate on how to translate the SEP targets into exact constraint regions on noiseless received components at MSs. With relaxation on the exact regions, a tractable convex problem is obtained. A dual-decomposition-based algorithm is then developed to accommodate parallel processors to perform precoding calculation. The signature sparsity turns out to be vital to reduce interprocessor communication overhead and computational complexity for pre-coding. The scheme proposed offers considerable transmit power reduction compared with the conventional zero-forcing precoder.

We investigate a coordinated scheduling problem in a two-cell network where in each cell, two users are scheduled for simultaneous communication. Zero-forcing (ZF) beamforming is employed at each base station to suppress both intra- and inter-cell interference. The coordinated scheduling/beamforming problem is formulated as finding proper scheduling decisions and hence beamformers across the network such that a weighted sum-throughput is maximized. We propose three distributed scheduling policies that only require local data and local channel state information at each cell, and consume much less computation and communication overhead than the global optimization approach via exhaustive search. The proposed policies illustrate the complexity-performance tradeoff for the coordinated system. Nevertheless, numerical results show that at all levels of complexity, the proposed policies perform close to the global optimization approach with ZF beamforming and outperform the scheme with matched filtering beamforming even with global coordination.

We consider a coordinated multiuser scheduling problem for a multicell mutually interfering broadcast network. In particular, we focus on a two-cell cluster, where both base stations have only local data and local channel state information, but each has sufficient number of antennas to serve multiple homogeneous users under a full zero-forcing beamforming transmission. The scheduling problem is formulated as finding proper scheduled users and hence beamformers across the cells such that the sum rate is maximized. We uncover the structure for a good scheduling decision, which in turn motivates three distributed coordinated scheduling policies of different levels of complexity. For the simplest policy, we derive a lower bound on the expected achievable sum rate. It is shown in the large user population limit, the simplest policy suffices to preserve the best possible multiplexing gain and multiuser diversity gain for the model studied, but it does induce a pairing loss on the sum rate due to the limited coordination between cells.

This work studies a novel power-efficient precoder design problem for a linear cellular array with base station (BS) cooperation: data symbols intended for mobile stations (MSs) are drawn from discrete finite alphabets, precoding is performed among BSs to produce appropriate signals transmitted over the channel, symbol-by-symbol detection is performed at each MS, and a minimum Symbol Error Probability (SEP) for detection is introduced as the Quality-of-Service (QoS) metric at each MS. With regular constellations such as 16-QAM deployed as system data inputs, the SEP constraints are formulated and characterized by a set of convex relaxations on the received signals. A convex power optimization problem is then formulated subject to the SEP constraints. By the primal-dual decomposition approach, a distributed algorithm is developed to solve the problem in which only local communication among BSs is required. Our scheme is shown to significantly outperform linear zero-forcing precoder in terms of transmit power consumption.

It has been recently shown by Lapidoth and Steinberg that strictly causal state information can be beneficial in multiple access channels (MACs). Specifically, it was proved that the capacity region of a two-user MAC with independent states, each known strictly causally to one encoder, can be enlarged by letting the encoders send compressed past state information to the decoder. In this study, a generalization of the said strategy is proposed whereby the encoders compress also the past transmitted codewords along with the past state sequences. The proposed scheme uses a combination of long-message encoding, compression of the past state sequences and codewords without binning, and joint decoding over all transmission blocks. The proposed strategy has been recently shown by Lapidoth and Steinberg to strictly improve upon the original one. Capacity results are then derived for a class of channels that include two-user modulo-additive state-dependent MACs. Moreover, the proposed scheme is extended to state-dependent MACs with an arbitrary number of users. Finally, output feedback is introduced and an example is provided to illustrate the interplay between feedback and availability of strictly causal state information in enlarging the capacity region.

A state-dependent degraded broadcast diamond channel is studied where the source-to-relays cut is modeled with two noiseless, finite-capacity digital links with a degraded broadcasting structure, while the relays-to-destination cut is a general multiple access channel controlled by a random state. It is assumed that the source has noncausal channel state information and the relays have no state information. Under this model, first, the capacity is characterized for the case where the destination has state information, i.e., has access to the state sequence. It is demonstrated that in this case, a joint message and state transmission scheme via binning is optimal. Next, the case where the destination does not have state information, i.e., the case with state information at the source only, is considered. For this scenario, lower and upper bounds on the capacity are derived for the general discrete memoryless model. Achievable rates are then computed for the case in which the relays-to-destination cut is affected by an additive Gaussian state. Numerical results are provided that illuminate the performance advantages that can be accrued by leveraging noncausal state information at the source.

The state-dependent multiple access channel (MAC) is considered where the state sequences are known strictly causally to the encoders. First, a two-user MAC with two independent states each known strictly causally to one encoder is revisited, and a new achievable scheme inspired by the recently proposed noisy network coding is presented. This scheme is shown to achieve a rate region that is potentially larger than that provided by recent work for the same model. Next, capacity results are presented for a class of channels that include modulo-additive state-dependent MACs. It is shown that the proposed scheme can be easily extended to an arbitrary number of users. Finally, a similar scheme is proposed for a MAC with common state known strictly causally to all encoders. The corresponding achievable rate region is shown to reduce to the one given in the previous work as a special case for two users.