Imperfect Channel State Information Research Articles

Performance analysis of full-duplex densely distributed MIMO with wireless backhaul

In this paper, the spectral efficiency of a full-duplex (FD) densely distributed multi-input multi-output (MIMO) system with wireless backhaul is considered. In-band full-duplex (IBFD) backhauling, in which the backhaul and access transmissions take place on the same spectrum, is exploited for wireless backhauling to enable an efficient spectrum reuse. However, the severe cross-tier interference and cross-link interference reduce the gains of IBFD backhauling. To evaluate the achievable spectral efficiency with imperfect channel state information (CSI), we propose a two-phase channel estimation scheme to estimate the CSI for two wireless links, and the scheme estimates an effective interference CSI between access points (APs) based on beamforming training to perform interference cancelation at APs. Given the estimated CSI, the closed-form expressions of the uplink and downlink achievable rates with maximum ratio transmission beamforming and maximum ratio combining receivers, respectively, are derived with Gamma approximation. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the two-phase channel estimation scheme for interference cancelation. Moreover, compared with half-duplex densely distributed MIMO systems, FD systems with interference cancelation have a better performance.

Backscatter-enabled CR-NOMA based cooperative V2X communication with imperfect CSI

Non orthogonal multiple access (NOMA) and backscatter communications are considered to be promising technology due to their applications in large scale communications and spectral efficiency. Cognitive radio (CR) also has distinct advantage in terms of allowing secondary users to share spectrum with the primary users. Thus CR and NOMA provides a new way to enable vehicle to everything (V2X) communication through cooperation, in terms of the achievable rate (throughput), which is further enhanced with use of backscatter devices. Moreover, in V2X it is critical to inspect imperfect channel state information (CSI) because, mobility of vehicles leads to more channel estimation uncertainties. Therefore, CR based approach which is supported by NOMA with imperfect CSI, called as B-CR-NOMA is proposed in which several vehicles connect with the base stations (BSs) through different road side units (RSUs) and backscatter devices. To optimize the performance of the proposed approach, a problem of optimal (maximization) of throughput has been formulated. Moreover, the non-convexity associated with the signal to interference plus noise ratio (SINR) is removed by the use of Lagrangian dual transformation by introducing auxiliary variables, which is further solved under Karush–Kuhn–Tucker (KKT) conditions along with using sub-gradient techniques. The exceptional attainment of the throughput for the proposed approach in comparison to the contemporary algorithms in terms of: power as well as number of RSU, number of vehicle users and channel estimation error, accentuate the capability of the proposed approach. The fortitude of B-CR-NOMA is further substantiated in terms of fairness (fairness index), which is measured and compared based on the channel CSI: perfect and imperfect. Also, the effect of the auxiliary variables and iterations on the throughput for the proposed approach is also analyzed. Along with this, two key metrics: average outage probability and average fairness index, which measures the cooperative V2X communication, are also considered for in depth analysis of the proposed approach.

On the Physical-Layer Security of a Dual-Hop UAV-Based Network in the Presence of Per-Hop Eavesdropping and Imperfect CSI

In this paper, the physical layer security of a dual-hop unmanned aerial vehicle-based wireless network, subject to imperfect channel state information (CSI) and mobility effects, is analyzed. Specifically, a source node (S) communicates with a destination node (D) through a decode-and-forward relay (R), in the presence of two wiretappers (E1,E2) independently trying to compromise the two hops. Furthermore, the transmit nodes (S,R) have a single transmit antenna, while the receivers (R,D,E1,E2) are equipped with multiple receive antennas. Based on the per-hop signal-to-noise ratios (SNRs) and correlated secrecy capacities’ statistics, a closed-form expression for the secrecy intercept probability (IP) metric is derived, in terms of key system parameters. Additionally, asymptotic expressions are revealed for two scenarios, namely (i) mobile nodes with imperfect CSI and (ii) static nodes with perfect CSI. The results show that a zero secrecy diversity order is manifested for the first scenario, due to the presence of a ceiling value of the average SNR, while the IP drops linearly at high average SNR in the second one, where the achievable diversity order depends on the fading parameters and number of antennas of the legitimate links/nodes. Furthermore, for static nodes, the system can be castigated by a 15 dB secrecy loss at IP=3×10-3, when the CSI imperfection power raises from 0 to 10-3. Lastly, the higher the legitimate nodes’ speed, carrier frequency, delay, and/or relay’s decoding threshold SNR, the worse is the system’s secrecy. Monte Carlo simulations endorse the derived analytical results.

Learning to Beamform in Joint Multicast and Unicast Transmission With Imperfect CSI

With the rapid development of mobile Internet, the demand for multicast is growing rapidly, such as content pushing and video streaming. The multicast service is usually offered to users without interrupting their on-going unicast transmission, and thus the multicast and unicast beamformers needs to be jointly designed, which generally requires perfect channel state information (CSI). However, perfect CSI is usually unavailable due to the channel estimation error. In this paper, we propose a learning based approach to jointly design the multicast and unicast beamformers with imperfect CSI. To learn the beamforming strategy, a new graph neural network (GNN) based architecture named unicast-multicast GNN (UMGNN) is proposed, which only requires the estimated channel. UMGNN can guarantee the permutation invariance/equivalence and model the special property in the multicast transmission, i.e., the multicast rate is determined by the worst user. Moreover, by sharing the parameters across different users, UMGNN exhibits a pretty good scalability to different number of users. Numerical results show that UMGNN outperforms a fully connected neural network and a widely used sampling-based algorithm. To highlight its performance in the multicast transmission, we also show that UMGNN can find the correct worst user that determines the multicast rate.

Hybrid Precoding Based on Active Learning for mmWave Massive MIMO Communication Systems

In this paper, a cost-effective and high-accuracy precoding technique based on ϵ-Fuzzy pareto active learning (FPAL) is proposed for millimeter wave (mmWave) massive MIMO communications. The proposed method achieves a low iteration convergence with low complexity. Two practical structures, namely fully-connected and partially-connected structures are considered for hybrid precoding. Furthermore, the effect of high and low-resolution quantization in the digital-to-analog converter, phase shifter, and imperfect channel state information are discussed. The performance results of the proposed technique and alternating minimization methods beside fully digital techniques are compared and discussed in the terms of the spectral efficiency (SE), bit error rate (BER), normalized mean square error (NMSE), energy efficiency (EE) for phase-shifter (PS) with low bit quantization, and imperfect channel state information (CSI). To address the applicability of the proposed method, experimental results are obtained from a real mmWave hardware setup compliant with 3GPP standards, and verify the simulated ones for the proposed ϵ-FPAL hybrid precoding scheme. The reduced complexity, higher performance and EE make the proposed method suitable for 5G and beyond communication systems.

Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead.

Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems With Imperfect CSI

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest.

Link Adaptation for Rate Splitting Systems With Partial CSIT

Rate Splitting (RS) has emerged as a flexible multiple-access scheme for Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems, generalizing more conventional approaches such as linear precoding or Non-Orthogonal Multiple Access (NOMA), and providing additional robustness against imperfect channel state information at the transmitter. Actual achievable rates cannot be pre-calculated when facing imperfect and/or outdated channel state information at the transmitter (CSIT), so practical real-time mechanisms in the form of link adaptation (LA) are required. The available CSIT is used to design the RS precoders, which can accommodate some degree of robustness, whereas the physical layer feedback reports the outcome of the decoding of the codewords, in the form of acknowledgement (ACK) or negative ACK (NACK), providing the additional meta-robustness needed when the statistical model of CSIT errors is not accurate, and/or the precoders cannot fully track the channel dynamics. We characterize the outage rate region of RS and analyze the convergence of the complete LA-RS system under partial CSIT (non-accurate and non-permanent), providing relevant insights for addressing the adaptation of private and common rates, designing robust precoders with throughput as relevant metric, and handling the time-variant CSIT quality.

RIS-Aided SCMA-Based SWIPT Systems: Design and Optimization

The amalgamation of sparse code multiple access (SCMA) and simultaneous wireless information and power transfer (SWIPT) is envisioned to be a promising solution to massive connectivity while providing both information and power to myriads of wireless machines and sensors in Internet-of-Things (IoT) application. However, the benefit of the SCMA-based SWIPT system has not been fully exploited yet, especially when the communications link is hostile. Fortunately, the propagation-induced impairment can be mitigated by the emerging technique of reconfigurable intelligent surface (RIS). In this paper, we investigate the beneficial role of RIS in SCMA-based SWIPT systems, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> single-antenna information receivers and one signal-antenna energy receiver are served by a multiple-antenna base station over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$J$</tex-math></inline-formula> orthogonal subcarriers with the aid of RIS. We propose transmit power minimization algorithms for both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> the perfect and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> the imperfect channel state information (CSI) scenarios, by jointly optimizing the codebook assignment, the BS active beamforming, as well as RIS reflection coefficients, relying on advanced techniques both of alternating optimization and of successive convex approximations, and on the robust design framework. Furthermore, we compare and evaluate the detection performance of the system considered. The numerical results demonstrate that our proposed RIS-aided SCMA-based SWIPT system significantly outperforms the conventional SCMA-based SWIPT system without RIS and is capable of adapting to the imperfect full channel estimation results.

Asynchronous Cell-Free Massive MIMO With Rate-Splitting

In practical cell-free (CF) massive multiple-input multiple-output (MIMO) networks with distributed and low-cost access points, the asynchronous arrival of signals at the user equipments increases multi-user interference that degrades the system performance. Meanwhile, rate-splitting (RS), exploiting the transmission of both common and private messages, has demonstrated to offer considerable spectral efficiency (SE) improvements and its robustness against channel state information (CSI) imperfection. The signal performance of a CF massive MIMO system is first analyzed for asynchronous reception capturing the joint effects of propagation delays and oscillator phases of transceivers. Taking into account the imperfect CSI caused by asynchronous phases and pilot contamination, we derive novel and closed-form downlink SE expressions for characterizing the performance of both the RS-assisted and conventional non-RS-based systems adopting coherent and non-coherent data transmission schemes, respectively. Moreover, we formulate the design of robust precoding for the common messages as an optimization problem that maximizes the minimum individual SE of the common message. To address the non-convexity of the design problem, a bisection method is proposed to solve the problem optimally. Simulation results show that asynchronous reception indeed destroys both the orthogonality of the pilots and the coherent data transmission resulting in poor system performance. Besides, thanks to the uniform coverage properties of CF massive MIMO systems, RS with a simple low-complexity precoding for the common message obtained by the equal ratio sum of the private precoding is able to achieve substantial downlink sum SE gains, while the application of robust precoding to the common message is shown to be useful in some extreme cases, e.g., serious oscillator mismatch and unknown delay phase.

Rate Splitting in FDD Massive MIMO Systems Based on the Second Order Statistics of Transmission Channels

In this work, we present new results for the application of rate splitting multiple access (RSMA) to the downlink (DL) of a massive multiple-input-multiple-output system (MIMO) operating in frequency-division-duplex (FDD) mode. We propose a statistical precoding design relying on the channels’ second-order information when one-layer RS is implemented. The advantage of the statistical precoding lies in simplifying the precoder design by basing the optimization objective on the slowly-varying channel covariance matrices instead of the fast-changing instantaneous channel estimates. To this end, we use the so-called bilinear precoder, which was shown in [1] and [2] to have limited performance in the high SNR regime due to the imperfect channel state information (CSI) available at the base station (BS). We formulate the DL throughput maximization problem based on a widely used lower bound on the achievable sum rate and propose an iterative approach to solve the underlying optimization problem. Numerical results demonstrate the benefit of implementing one-layer RS. Furthermore, the proposed iterative approach achieves excellent results in terms of spectral efficiency compared to the state-of-the-art techniques.

Deep learning-based energy efficiency and power consumption modeling for optical massive MIMO systems

The fifth generation (5G) wireless communication system is considered a promising and recent research. Massive Multiple-Input Multiple-Output (MIMO) system has an influential role in improving game-changing enhancements in area throughput and energy efficiency (EE). EE refers to one of the easiest and most cost-effective ways to combat climate change, reduce energy costs for consumers, and improve the competitiveness of businesses. Deep Learning (DL) can significantly improve area throughput and EE. It plays a crucial role in the 5G wireless communication systems. Optical systems are not far from this system, which include the optical components which serve more accurately.To assess the overall power usage in up-link and down-link communications, a power dissipated model is introduced. The proposed model incorporates the overall power used by the base station (BS) power amplifier and circuit components as well as single antenna user equipment (UE). In this paper, EE and power consumption of massive MIMO systems are calculated based on Convolutional Neural Network hybrid with Long Short-Term Memory cell (CNNLSTM). This model is proposed to overcome the high complexity and over fitting by replacing the inner dense connections with convolution layers resulting in improved model performance. There are different linear processing schemes applied for detection and precoding, as Minimum Mean Squared Error (MMSE), Zero-Forcing (ZF), and Maximum Ratio Transmission/Maximum Ratio Combining (MRT/MRC). These schemes are applied to train our proposed CNNLSTM.It is observed the results are improved by 12.8% when using ZF (perfect CSI) and the system outperforms other schemes by 10%, 10.44% and 12.05% when using MRT, ZF (imperfect CSI), and MMSE, respectively, for the EE performance. The obtained results also reveal that an improvement of 7.5% is achieved when using MRT. It outperforms other schemes by 6.5%, 5% and 5%, respectively, when using ZF (perfect CSI), ZF (imperfect CSI), and MMSE for average power consumption per antenna using the CNNLSTM model. When using MRT, an improvement of 7.5% is achieved in the area throughput performance, and it outperformed the other schemes, ZF (perfect CSI), ZF (imperfect CSI) and MMSE, by 5.2%, 5% and 5.2%, respectively.

A Robust Adaptive Objective Power Allocation in Cognitive NOMA Networks.

Cognitive radio (CR) is a candidate for opportunistic spectrum implementation in wireless communications, allowing secondary users (SUs) to share the spectrum with primary users (PUs). In this paper, a robust adaptive target power allocation strategy for cognitive nonorthogonal multiple access (NOMA) networks is proposed, which involves the maximum transmission power of each SU and interference power threshold under PU constraints. By introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor, the strategy enables single-station communication to achieve energy efficiency (EE) or high throughput (HT), thus making the target function more flexible. In the same communication scenario, different cognitive users can choose different communication targets that meet their needs. Different QoS can be selected by the same cognitive user at different times. In the case of imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case, which is solved by the dual decomposition method. Simulation results show that this strategy has good adaptive selectivity and robustness.

URLLC-oriented secure communication for UAV relay-assisted network

In this paper, we investigate the unmanned aerial vehicle (UAV) relay-assisted secure short packet communication. The UAV acts as a decode-and-forward relay to transmit control signals from the source to the actuators in the presence of a ground eavesdropper (EV) whose imperfect channel state information is available at the UAV. Specially, non-orthogonal multiple access is adopted in our work to achieve more connections and improve the fairness of communication and the short packets are employed for data transmission to reduce the latency. Explicitly, we maximize the minimum average secrecy throughput among all actuators by jointly optimizing the UAV trajectory, transmit power and blocklength allocation, which generates a challenging optimization problem. Therefore, we propose an iterative algorithm based on block coordinate descent method and successive convex approximation technique to handle the non-convex problem. Numerical results show that the proposed scheme has better performance compared to the benchmark schemes.

Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks

In this article, we propose a state-of-the-art downlink communication transceiver design for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. Specifically, a feed antenna is deployed in the transmissive RMS-based transceiver, which can be used to implement beamforming. According to the relationship between wavelength and propagation distance, the spatial propagation models of plane and spherical waves are built. Then, in the case of imperfect channel state information (CSI), we formulate a robust system sum-rate maximization problem that jointly optimizes RMS transmissive coefficient, transmit power allocation, and power splitting ratio design while taking account of the nonlinear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is nonconvex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the nonconvex original problem. In detail, the whole problem is divided into three subproblems to solve. For the nonconvexity of the objective function, successive convex approximation (SCA) is used to transform it, and the penalty function method and difference-of-convex (DC) programming are applied to deal with the nonconvex constraints. Finally, we alternately solve the three subproblems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms.

Q-Learning-Aided Offloading Strategy in Edge-Assisted Federated Learning over Industrial IoT

Federated learning (FL) is a key solution to realizing a cost-efficient and intelligent Industrial Internet of Things (IIoT). To improve training efficiency and mitigate the straggler effect of FL, this paper investigates an edge-assisted FL framework over an IIoT system by combining it with a mobile edge computing (MEC) technique. In the proposed edge-assisted FL framework, each IIoT device with weak computation capacity can offload partial local data to an edge server with strong computing power for edge training. In order to obtain the optimal offloading strategy, we formulate an FL loss function minimization problem under the latency constraint in the proposed edge-assisted FL framework by optimizing the offloading data size of each device. An optimal offloading strategy is first derived in a perfect channel state information (CSI) scenario. Then, we extend the strategy into an imperfect CSI scenario and accordingly propose a Q-learning-aided offloading strategy. Finally, our simulation results show that our proposed Q-learning-based offloading strategy can improve FL test accuracy by about 4.7% compared to the conventional FL scheme. Furthermore, the proposed Q-learning-based offloading strategy can achieve similar performance to the optimal offloading strategy and always outperforms the conventional FL scheme in different system parameters, which validates the effectiveness of the proposed edge-assisted framework and Q-learning-based offloading strategy.

Robust Beamforming Design for IRS-Assisted Downlink Multi-User MISO-URLLC in an IIoT Scenario

Intelligent reflecting surfaces (IRS) have recently been regarded as having potential for supporting multi-user (MU) multiple-input single-output (MISO) ultra-reliable and low-latency communication (URLLC) by creating favorable wireless communication links in the industrial IoT (IIoT) scenario. Hence, we studied the joint robust design of a beamformer at a central controller (CC) and phase shifters at the IRS for minimizing the transmit power consumption based on perfect channel state information (CSI) and imperfect CSI, respectively. The design was formulated as a non-convex optimization problem, also taking into account the quality-of-service (QoS) demands of the actuator, unit-modulus constraints of the IRS, and the robustness against the impact of CSI imperfection. Subsequently, we proposed a computationally efficient iterative algorithm to obtain a suboptimal solution by exploiting the penalty method and the successive convex approximation (SCA) for perfect CSI, while the penalty method, S-procedure, and SCA were adopted for imperfect CSI. Finally, our simulation results revealed that (1) the required transmit power consumption for URLLC was significantly reduced by employing the proposed IRS instead of conventional wireless communication without IRS; and (2) the proposed algorithm was robust and effective for the beamforming design.

BER Performance of Co-operative Relay NOMA-assisted PS Protocol with Imperfect SIC and CSI

Objective: Wireless networks and devices are consuming a significant amount of energy as wireless communication is rapidly expanding, radio frequency (RF) energy harvesting has been envisioned as a feasible technology for powering low-power wireless systems. Aims: This paper investigates a bit error rate (BER) of the non-orthogonal multiple access with cooperative relay-assisted power splitting (CR-NOMA-PS) based energy harvesting (EH). Methods: We consider that the relay works in the decode and forward (DF) mode. For more practical scenarios, we consider the imperfect successive interference cancellation (SIC) and channel state information (CSI) are available. Results: We obtain the end-to-end (e2e) BER expressions for the CR-NOMA-PS with imperfect CSI. Under different scenarios of PS, we evaluate and discuss the BER performance of the users with imperfect SIC and CSI. We validate the derivation of the BER expressions by simulation results. Conclusion: The results indicated that the high values of the PS factor reduce the users' performance. Furthermore, in the high signal-to-noise ratio (SNR), the CSI error degrade BER performance and produced an error floor.

Robust Beamformer Design in Active RIS-Assisted Multiuser MIMO Cognitive Radio Networks

In this work, we address the problem of a joint robust transmission, reflection and reception strategy design at an active reconfigurable intelligent surface (RIS)-assisted underlay multiple input multiple output (MIMO) cognitive radio networks in which a secondary transmitter serves multiple secondary receivers simultaneously. As such, we study the impact of imperfection in channel state information (CSI) at the secondary transmitter following a norm-bounded error model. A sum mean squared error (MSE) minimization problem is formulated by jointly optimizing the transmit beamforming matrix (BFM) at the secondary transmitter, the linear reception filters (LRF) at the secondary receivers and the reflection coefficient matrix (RCM) at the RIS subject to the constraints of available transmission power at the secondary transmitter, maximum allowed interference power towards the primary receiver and the permissible amplification range at each RIS element. Due to the coupled nature of the BFM, LRF and RCM variables, the formulated problem is non-convex and, thus, cannot be solved using conventional optimization methods. As a result, we propose an alternating optimization (AO) based iterative algorithm that determines the optimal BFM, LRF and RCM using semidefinite programming and inner approximation methods with guaranteed convergence. We also discuss the impact of the limited power budget at active RIS on the performance of the considered network. We present simulation-based numerical results to validate the efficacy and robustness of the proposed algorithm. Moreover, we also highlight the superiority of the use of the active RIS over passive RIS in the considered communication network.

Short-Packet Communications for UAV-Based NOMA Systems Under Imperfect CSI and SIC

This paper studies the performance of unmanned aerial vehicle (UAV)-aided short-packet non-orthogonal multiple access (NOMA) systems under imperfect channel state information (ipCSI) and successive interference cancellation (ipSIC). Closed-form expressions for the average block-error rate (BLER), throughput, goodput, latency, reliability, and age of information (AoI) are derived. To gain useful insights, an asymptotic BLER analysis is carried out at high signal-to-noise ratio (SNR) regime, from which users’ diversity order and the impact of the UAV’s trajectory are deduced. Three optimization problems are formulated in order to: 1) minimize the overall BLER of the user with high quality-of-service by optimizing the power allocation (PA) coefficients; 2) maximize the system throughput by jointly optimizing PA and coding rate (CR) coefficients; 3) minimize AoI by optimizing block-length transmission per-hop transmission. Numerical results show that, at high SNR regime, users’ performance is not influenced by UAVs’ radius and horizontal flight angle when ipCSI occurs. The proposed system achieves better throughput than the baseline schemes and can be further enhanced by jointly optimizing PA and CR. To achieve the minimum AoI, the source and UAV should transmit a moderate block-length for low transmit power, while for moderate and high transmit power, 100 is sufficient.