Statistical Channel State Information Research Articles

Optimal Decoding Order and Power Allocation for Sum Throughput Maximization in Downlink NOMA Systems.

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels.

Beam pointing optimization for spatial diversity MIMO free space optical communications

In this paper, we propose a beam pointing algorithm to find optimal beam coordinates maximizing link performance in multiple-input multiple-output (MIMO) free space optical communications (FSOC) with spatial diversity. We first derive the received signal-to-noise ratio (SNR) with spatial diversity and analyze the convexity condition of SNR function with respect to transmit beam coordinates. Then, we obtain the beam coordinates maximizing the received SNR through an alternating optimization (AO) algorithm and Newton’s method. However, the proposed algorithm requires instantaneous channel state information (CSI), which requires a high signaling overhead in MIMO FSOC. Thus, instead of exploiting instantaneous CSI, we utilize statistical CSI and propose a robust beam pointing algorithm with respect to atmospheric turbulence and pointing error. Numerical results show that our proposed algorithm outperforms conventional schemes in a dynamic FSOC network. Furthermore, we confirm that the optimal coordinates which are robust to the channel conditions approach to the centroid of receiver lenses as the strength of turbulence and pointing error increases.

Outage constrained transmission design for NOMA-based integrated sensing and communication systems

This paper investigates a framework for integrated sensing and communication (ISAC) based on non-orthogonal multiple access (NOMA). In this framework, a dual-function base station (BS) utilizes NOMA technology to send superimposed signals to various users, and this superimposed signal also acts on target sensing simultaneously. Considering the channel estimation error, ensuring the communication performance, and sensing performance requirements, a transmit power optimization problem of ISAC system using NOMA is studied. Specifically, in the statistical channel state information (CSI) error model, the total system communicate power is minimized while ensuring all single users' rate outage probability (OP) constraints and the requirements for the beampattern gains of all single radar targets. Unfortunately, the proposed problem is challenging to solve and non-convex. But we have devised a feasible way to deal with this problem. First, we use Bernstein inequality to transform the rate OP constraint, and this challenging non-convex problem is then successfully solved using a method based on semi-definite relaxation (SDR). The numerical outcomes demonstrate that the system's overall transmission power will increase due to the channel estimation error. The numerical findings also show that the ISAC system performs better with NOMA assistance than with OMA when comparing the NOMA and OMA schemes.

Maximum likelihood based detector for PD-NOMA with statistical CSI: more efficient and lower complexity compared to SIC

Maximum likelihood based detector for PD-NOMA with statistical CSI: more efficient and lower complexity compared to SIC

2D Beam Domain Statistical CSI Estimation for Massive MIMO Uplink

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two-dimensional (2D) beam-based statistical channel model (BSCM) in massive multi-input multi-output (MIMO) systems. The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple received pilot signals. A received signal model showing the relation between the statistical properties of the received pilot signals and the BDCPMs is derived. On the basis of the received signal model, we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without the estimates of instantaneous CSI is proposed. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., estimation of the angle domain channel power matrix. Simulation results show that the proposed method has good convergence and can obtain sparse BDCPMs. Compared with the regularized multiple measurement vector focal underdetermined system solver (RM-FOCUSS) algorithm, the proposed algorithm obtains overall more accurate statistical CSI with much lower complexity and brings significant performance gains when used in channel estimation.

Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI

Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI

Reconfigurable Intelligent Surface Empowered Federated Edge Learning with Statistical CSI

Reconfigurable Intelligent Surface Empowered Federated Edge Learning with Statistical CSI

Receive Antenna Selection in Resource-Efficient Asymmetrical Massive MIMO IoT Networks by Exploiting Statistical CSI

Receive Antenna Selection in Resource-Efficient Asymmetrical Massive MIMO IoT Networks by Exploiting Statistical CSI

Sum-Rate Maximization for RIS-IoV: From Instantaneous to Statistical CSI

Sum-Rate Maximization for RIS-IoV: From Instantaneous to Statistical CSI

Two-Hop IRS-Assisted Wireless Communications Exploiting Statistical CSI

Two-Hop IRS-Assisted Wireless Communications Exploiting Statistical CSI

Algorithm Design and Convergence Analysis for Coexistence of Cognitive Radio Networks in Unlicensed Spectrum.

This paper focuses on achieving the low-cost coexistence of the networks in an unlicensed spectrum by making them operate on non-overlapping channels. For achieving this goal, we first give a universal convergence analysis framework for the unlicensed spectrum allocation algorithm. Then, a one-timescale iteration-adjustable unlicensed spectrum allocation algorithm is developed, where the step size and timescale parameter can be jointly adjusted based on the system performance requirement and signal overhead concern. After that, we derive the sufficient condition for the one-timescale algorithm. Furthermore, the upper bound of convergence error of the one-timescale spectrum allocation algorithm is obtained. Due to the multi-timescale evolution of the network states in the wireless network, we further propose a two-timescale iteration-adjustable joint frequency selection and frequency allocation algorithm, where the frequency selection iteration timescale is set according to the slow-changing statistical channel state information (CSI), whereas the frequency allocation iteration timescale is set according to the fast-changing local CSI. Then, we derive the convergence condition of two-timescale algorithms and the upper bound of the corresponding convergence error. The experimentalresults show that the small timescale adjustment parameter and large step size can help decrease the convergence error. Moreover, compared with traditional algorithms, the two-timescale policy can achieve throughput similar to traditional algorithms with very low iteration overhead.

RIS-Assisted Device Activity Detection With Statistical Channel State Information

This paper studies reconfigurable intelligent surface (RIS)-assisted device activity detection for grant-free (GF) uplink transmission in wireless communication networks. In particular, we consider mobile devices located in an area where the direct link to an access point (AP) is blocked. Thus, the devices try to connect to the AP via a reflected link provided by an RIS. Therefore, for the RIS, a phase-shift design is desired that covers the entire blocked area with a wide reflection beam because the exact locations and times of activity of the devices are unknown in GF transmission. In order to study the impact of the phase-shift design on the device activity detection at the AP, we derive a generalized likelihood ratio test (GLRT) based detector and present an analytical expression for the probability of detection, which is a function of the channel statistics and the phase-shift design. Assuming knowledge of statistical channel state information (CSI), we formulate an optimization problem for the phase-shift design for maximization of the guaranteed probability of detection for all locations within a given coverage area. To tackle the non-convexity of the problem, we propose two different approximations of the objective function and an algorithm based on the majorization-minimization (MM) principle. The first approximation leads to a design that aims to reduce the variations of the end-to-end channel while taking system parameters such as transmit power, noise power, and probability of false alarm into account. The second approximation can be adopted for versatile RIS deployments because it only depends on the line-of-sight (LoS) component of the end-to-end channel and is not affected by system parameters. For comparison, we also consider a phase-shift design maximizing the average channel gain and a baseline analytical phase-shift design for large blocked areas. Our performance evaluation shows that the proposed approximations result in phase-shift designs that guarantee a high probability of detection across the coverage area and outperform the baseline designs.

Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

Reconfigurable-Intelligent-Surface-Aided Number Modulation for Symbiotic Active/Passive Transmission

Reconfigurable intelligent surface (RIS)-aided symbiotic active/passive transmission is a promising communication paradigm, which is able to improve the propagation environment while transmitting additional information. In this paper, a novel scheme, termed RIS-aided number modulation (RIS-NM), is proposed for symbiotic active/passive communications. In RIS-NM, the RIS elements are divided into in-phase (I-) and quadrature (Q-) subsets depending on their phase shift configurations, and the number of elements in the I-subset (or Q-subset, equivalently) is used to convey the RIS’s private information. A low-complexity yet near-optimal detector is designed for RIS-NM by shrinking the search space of constellation points. We then investigate a special case of RIS-NM, termed RIS-aided number shift keying (RIS-NSK), in which the radio-frequency source transmits unmodulated carrier signals. Statistic channel state information (CSI)-based maximum-likelihood (ML) detection is developed for RIS-NSK. We analyze the bit error rate (BER) performance of RIS-NM/NSK over Rician fading channels. BER upper bounds are derived in closed-form for RIS-NM by assuming instantaneous CSI-based ML detection, while an approximate BER expression is obtained for RIS-NSK by assuming statistic CSI-based ML detection. Furthermore, we extend RIS-NM to multiple-input multiple-output scenarios. Our simulation results in terms of BER corroborate the performance analysis and the superiority of RIS-NM over the state-of-the-art RIS-aided symbiotic active/passive transmission scheme.

IRS-Aided Joint Spatial Division and Multiplexing for mmWave Multiuser MISO Systems

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.

IRS-Assisted High-Speed Train Communications: Performance Analysis and Optimal Configuration

High-speed train (HST) communications are envisioned to provide diversified broadband services by integrating with 5G while the high mobility induces fast-fading channels and potentially degrades the system performance. To address this issue, we investigate an HST communication network empowered by intelligent reflecting surfaces (IRS) with the multiple-input multiple-output (MIMO) technology. Statistical channel state information (CSI) is exploited to mitigate the impact of the fast time-varying fading. The transceiver beamforming vectors and the IRS phase shift matrix are optimized to improve the system performance in terms of the outage probability and the ergodic capacity considering the channel uncertainty. First, we derive the analytical expression of the outage probability with a generalized Marcum Q-function. Then, we develop an alternating optimization algorithm to minimize the outage probability by capitalizing on the generalized eigenvalue-eigenvectors. Moreover, the ergodic capacity is deduced with statistical CSI and then optimized by analyzing the upper bound with Jensen’s approximation. Extensive simulations show that simulation results are consistent with the theoretical analysis, and the IRS-assisted system significantly outperforms the system without IRS in terms of the outage probability and the ergodic capacity.

Cooperative vehicular networks over Nakagami-[formula omitted] fading: Joint power control and spectrum scheduling

Facilitated by the rapid development of wireless communication technologies and the broad deployment of cellular networks, vehicular communications have evolved towards ultra-reliable low-latency communications (URLLC). In this paper, we consider a device-to-device (D2D)-enabled vehicular networks, where the uplink from roadside units (RSUs) to the base station (BS) shares spectrum with D2D-enabled vehicle-to-vehicle (V2V) links. The sum capacity of RSUs is maximized by jointly optimizing the transmit power and orthogonal spectrum bands scheduling, with the minimum data rate of RSUs and the reliability of V2V links guaranteed. Specifically, under Nakagami fading channels, the statistical channel state information (CSI) of vehicular communication links is utilized instead of the instantaneous CSI to adapt to the extra overheads resulting from frequent channel estimation. Moreover, user fairness is also considered, through maximizing the minimum ergodic capacity achieved by RSUs. To solve the formulated non-convex problem, we decomposed it into a power allocation subproblem that is analytically solved, and a spectrum bands scheduling subproblem that is tackled by resorting to the Hungarian method. The validity of the proposed methods is confirmed by numerical results.

Transceiver Beamforming for Over-the-Air Computation in Massive MIMO Systems

This paper investigates the transmitter and receiver beamforming (TB and RB) for over-the-air computation (AirComp) in massive multiple-inputs and multiple-outputs (MIMO) systems. First, we propose a two-phase hybrid beamforming algorithm to design TB and hybrid RB. In the first phase, we adopt a projected gradient descent with momentum (PGDM) algorithm to search for the optimal fully-digital TB matrices. Compared with the benchmarks on the mean square error (MSE) performances, PGDM can achieve up to 5 dB gain in signal-to-noise ratio (SNR) with less algorithm execution time when fully-digital RB is assumed. In the second phase, we plug the TB matrices obtained in PGDM as well as the optimal baseband RB (BBRB) matrix into the MSE objective, and adopt gradient descent to search for the optimal radio-frequency RB (RFRB) matrix. Compared with the state-of-the-arts, the proposed two-phase algorithm reduces up to 30% of the algorithm execution time and 18% of the MSE. Second, we propose a statistical TB algorithm to reduce the communication overheads, in which TB completely depends on statistical channel state information (CSI) and thus does not rely on the feedback from the base station (BS). We prove that orthonormal matrices are asymptotically optimal for statistical TB when uncorrelated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rayleigh</i> channels are assumed and the number of receiving antennas approaches to infinity. For correlated channels, experimental results show that the proposed statistical TB can achieve about 5 dB gain in SNR compared with orthonormal matrices in terms of the MSE performance. Third, a large scale system analysis is made in this paper. As the number of the receiving antennas approaches to infinity, asymptotically optimal choices for TB and RB are provided, and upper bounds of MSE are derived in terms of the number of clients and receiving antennas. For hybrid RB, an upper bound of the squared distance between the optimal hybrid RB and the optimal fully-digital RB is also derived.

A Unified Joint Optimization of Training Sequences and Transceivers Based on Matrix-Monotonic Optimization

Channel estimation and data transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions.

Achievable Rate Analysis of the STAR-RIS-Aided NOMA Uplink in the Face of Imperfect CSI and Hardware Impairments

Reconfigurable intelligent surfaces (RIS) are capable of beneficially ameliorating the propagation environment by appropriately controlling the passive reflecting elements. To extend the coverage area, the concept of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) has been proposed, yielding supporting 360° coverage user equipment (UE) located on both sides of the RIS. In this paper, we theoretically formulate the ergodic sum-rate of the STAR-RIS assisted non-orthogonal multiple access (NOMA) uplink in the face of channel estimation errors and hardware impairments (HWI). Specifically, the STAR-RIS phase shift is configured based on the statistical channel state information (CSI), followed by linear minimum mean square error (LMMSE) channel estimation of the equivalent channel spanning from the UEs to the access point (AP). Afterwards, successive interference cancellation (SIC) is employed at the AP using the estimated instantaneous CSI, and we derive the theoretical ergodic sum-rate upper bound for both perfect and imperfect SIC decoding algorithm. The theoretical analysis and the simulation results show that both the channel estimation and the ergodic sum-rate have performance floor at high transmit power region caused by transceiver hardware impairments.