Imperfect Channel Feedback Research Articles

Real-Time Status Updates in Wireless HARQ With Imperfect Feedback Channel

We study the impact of the erroneous wireless control feedback channel on the Age of Information (AoI) performance. We consider a point-to-point communication setup employing packet combining strategies to transmit status update packets over an erroneous wireless data channel. The sender receives the positive acknowledgment (ACK) or negative acknowledgment (NACK) of packet reception over an error-prone wireless feedback channel. To mitigate the impact of the imperfect feedback channel on the system performance, we adopt an asymmetric signal detection model to control the detection accuracy of ACK and NACK signals. We then compute the explicit expressions for the average AoIs under preemptive and non-preemptive service management policies. We show the optimum parameter design for the control channel model in order to minimize the average AoI. The numerical results validate the analysis and provide detailed perspectives on the optimal signal detection setup minimizing the average AoI, and the possible trade-off between AoI and resource utilization. Generally, the analysis for a preemption setting illustrates that a better protection for the NACK messages compared to the ACK messages can preserve the minimum AoI performance. Especially, under a high noisy feedback channel setup, we show that the viable solution minimizing the average AoI is a blind transmission mechanism at the cost of increasing unnecessary utilization of the channel resources. Moreover, the analysis for a non-preemptive policy reveals the dependence of the optimal feedback signal detection design on the status packet generation rate at the sensor. Such a dependency makes the feedback signal detection approach to provide a more reliable ACK detection compared to NACK messages under the condition of more frequent packet arrival, whereas the opposite holds under the condition of less frequent packet arrival.

Enabling 5G indoor services for residential environment using VLC technology

Visible light communication (VLC) has emerged as a viable complement to traditional radio frequency (RF) based systems and as an enabler for high data rate communications for beyond-5G (B5G) indoor communication systems. In particular, the emergence of new B5G-based applications with quality of service (QoS) requirements and massive connectivity has recently led to research on the required service-levels and the development of improved physical (PHY) layer methods. As part of recent VLC standards development activities, the IEEE has formed the 802.11bb “Light Communications (LC) for Wireless Local Area Networking” standardization group. This paper investigates the network requirements of 5G indoor services such as virtual reality (VR) and high-definition (HD) video for residential environments using VLC. In this paper, we consider such typical VLC scenarios with additional impairments such as light-emitting diode (LED) nonlinearity and imperfect channel feedback, and propose hyperparameter-free mitigation techniques using Reproducing Kernel Hilbert Space (RKHS) methods. In this context, we also propose using a direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM)-based adaptive VLC transmission method that uses precomputed bit error rate (BER) expressions for these RKHS-based detection methods and performs adaptive BER-based modulation-order switching. Simulations of channel impulse responses (CIRs) show that the adaptive transmission method provides significantly improved error rate performance, which makes it promising for high data rate VLC-based 5G indoor services.

Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks With Imperfect Channels

This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> -fairness objective. In the conventional DRL framework, feedback/reward given to the agent is always correctly received, so that the agent can optimize its strategy based on the received reward. In our wireless application where the channels are noisy, the feedback/reward (i.e., the ACK packet) may be lost due to channel noise and interference. Without correct feedback, the agent (i.e., the network user) may fail to find a good solution. Moreover, in the distributed protocol, each agent makes decisions on its own. It is a challenge to guarantee that the multiple agents will make coherent decisions and work together to achieve the same objective, particularly in the face of imperfect feedback channels. To tackle the challenge, we put forth (i) a feedback recovery mechanism to recover missing feedback information, and (ii) a two-stage action selection mechanism to aid coherent decision making to reduce transmission collisions among the agents. Extensive simulation results demonstrate the effectiveness of these two mechanisms. Last but not least, we believe that the feedback recovery mechanism and the two-stage action selection mechanism can also be used in general distributed multi-agent reinforcement learning problems in which feedback information on rewards can be corrupted.

A Fully Distributed and Clustered Learning of Power Control in User-Centric Ultra-Dense HetNets

In this paper, we investigate a power control of uplink connection in the user-centric ultra-dense heterogeneous networks (HetNets), which are studied as different types of access points (APs). The main objective of this investigation is to engage users in a cooperative game to confront with the problem of per-user power control and coordinate multi-user interferences. Thus, we formulate the optimization problem as a sum of the users’ cooperative energy efficiency (EE) functions. Firstly, a realistic and new model of the user device's power consumption is proposed. This model includes the power used for operating modes and signal processing of mobile devices during the uplink data transmission. Secondly, the EE optimization problem is formulated by maximizing the sum of the users’ cooperative EE function subject to each user's quality-of-service (QoS) and a power constraint. Then, we propose a fully distributed and clustered learning scheme for solving the optimization problem, where neighboring users are clustered to engage in the cooperative game of power control in order to coordinate the multi-user interferences. It is theoretically proved that the size of the clusters has an impact on the sum of the users’ cooperative EE. Additionally, our scheme can achieve convergence with imperfect channel feedback and the local knowledge of the user's constraints. Finally, simulation results confirm the theoretical analysis and demonstrate the robust performance of the proposed scheme compared with two benchmark methods.

Joint Channel Resources Allocation and Beamforming in Energy Harvesting Systems

We study a multiple-input single-output energy harvesting (EH) system, where the receiver is capable of harvesting energy from an energy source with a deterministic energy profile. Assuming the practical assumptions of imperfect channel state information (CSI) and limited-rate feedback channel, the minimum mean square error and the random vector quantized (RVQ) schemes are resorted to implement the CSI estimation and feedback, respectively. Considering the characteristic of the EH system, a power and time allocation strategy is proposed for the CSI training and feedback to maximize an upper bound of the downlink data rate. Using the optimization theory, the optimal resources allocation and the RVQ beamforming vector are obtained together with the optimal subset of training antennas. Numerical results verify the tightness of the upper bound and significant advantages of the proposed scheme over other approaches.

Joint base stations clustering and feedback bits allocation for multi‐cell coordinated beamforming systems

In this study, the authors investigate the problem of maximising the mean rate in a multi-cell coordinated beamforming system considering practical delayed limited feedback and constrained backhaul. To tackle this hard problem, they first derive the analytical expression of the upper bound on the mean rate loss due to the clustered coordination and the imperfect channel feedback. Particularly, this upper bound quantitatively reveals a key tradeoff between decreasing inter-cell interference with larger base station (BS) cluster and decreasing inter-user interference with smaller BS cluster but improved channel state information accuracy. In light of this, they jointly optimise the BSs clustering and feedback bits allocation (BCFA) with the objective of minimising the upper bound on mean rate loss, which is equivalent to the problem of maximising the mean rate. Utilising the relaxation-and-rounding approach, a BSs clustering and feedback bits allocation algorithm, referred to as BCFA, is designed, which has good performance with low complexity. Finally, extensive simulation results are provided to demonstrate the advantages of the proposed algorithm.

Cooperative Spectrum Sensing With Quantization Combining Over Imperfect Feedback Channels

A practical fusion rule with quantization combining is proposed for cooperative spectrum sensing over imperfect feedback channels that are inherent in the wireless environment. Local sensor nodes generate decision symbols regarding the existence of a primary user signal by multilevel detection and send them over feedback channels to the fusion center, in which demodulated symbols are combined to draw the cooperative decision. The proposed quantization combining scheme over imperfect feedback channels is analyzed, based on which detection probability and false alarm probability are derived. Operating parameters of the fusion rule with quantization combining are optimally determined under Neyman-Pearson criterion by using a differential evolution algorithm. The proposed fusion rule is compared with other schemes in terms of the detection performance, the amount of system information required for operation, the sensitivity to the channel estimation error, and the computational complexity required for parameter optimization. It is shown that the proposed fusion rule offers the reliable sensing capability even over imperfect feedback channels with requiring small amount of system information and complexity.

Unified Analysis of Cooperative Spectrum Sensing Over Composite and Generalized Fading Channels

In this paper, we investigate the performance of cooperative spectrum sensing (CSS) with multiple antenna nodes over composite and generalized fading channels. We model the probability density function (PDF) of the signal-to-noise ratio (SNR) using the mixture gamma (MG) distribution. We then derive a generalized closed-form expression for the probability of energy detection, which can be used efficiently for generalized multipath as well as composite (multipath and shadowing) fading channels. The composite effect of fading and shadowing scenarios in CSS is mitigated by applying an optimal fusion rule that minimizes the total error rate (TER), where the optimal number of nodes is derived under the Bayesian criterion, assuming erroneous feedback channels. For imperfect feedback channels, we demonstrate the existence of a TER floor as the number of antennas of the CR nodes increases. Accordingly, we derive the optimal rule for the number of antennas that minimizes the TER. Numerical and Monte-Carlo simulations are presented to corroborate the analytical results and to provide illustrative performance comparisons between different composite fading channels.

Optimal Antenna Cluster Size in Cell-Free Distributed Large-Scale Antenna Systems with Imperfect CSI and Inter-Cluster Interference

In cell-free large-scale distributed antenna systems (L-DASs), the antennas are distributed over the intended coverage area. Introducing cooperation among the antennas can significantly improve the system throughput. However, only finite-cluster-size (limited) cooperation with L-DAS antenna clusters is realistic due to practical limitations. In this paper, the impact of antenna cluster size on the downlink sum rate of frequency-division duplex (FDD) cell-free L-DASs is analyzed, considering imperfect channel state information (CSI) and intercluster interference (ICLI). We investigate the optimal cluster size in terms of maximizing the downlink sum rate with respect to the overhead of imperfect channel training and feedback. When the number of users is sufficiently large, closed-form lower bounds of the ergodic sum capacity, which are leveraged to analyze the system performance, are derived by constructing different rate-achieving user-scheduling schemes. Both analog and digital feedback schemes are considered to evaluate the imperfect channel feedback. Based on these, closed-form expressions of the optimal cluster size for 1-D antenna topology systems and the corresponding achievable rates are derived. The scaling law of the optimal cluster size for 2-D antenna topology systems is also given. Numerical results demonstrate the impact of the signal-to-noise ratio (SNR) and the block length on the achievable rates and the optimal cluster size, which agree with our analytical results.

Cooperative Spectrum Sensing Scheme over Imperfect Feedback Channels

A practical cooperative spectrum sensing scheme suitable for wireless cognitive radio with imperfect feedback channels is proposed. In the proposed scheme, binary local decision bits are transmitted over feedback channels and combined to form a soft-valued decision statistics by a majority-decision-aided weighting rule in the fusion center. The proposed scheme provides high sensing reliability with avoiding overhead problems caused by imperfect feedback channels such as bandwidth expansion and instantaneous channel estimation.

Capacity Analysis of Downlink MIMO-OFDMA Resource Allocation with Limited Feedback

Opportunistic resource allocation for multiple users utilizing multiple-input multiple-output (MIMO) channel in orthogonal frequency division multiple access (OFDMA) is used to attain the spectral efficiency required in future wireless communications networks. In this paper, we express analytical outage capacity results and average capacity results with optimal rate adaptation for MIMO-OFDMA systems with limited feedback. A resource block (RB)-wise SNR quantization feedback scheme and a best-M feedback method are studied for RB allocation. Feedback based transmit antenna selection (AS) is considered in the analysis. An imperfect feedback channel has a significant impact on the performance of practical feedback based communication. The proposed analysis also considers imperfect feedback in the RB allocation and AS.

Adaptive dual‐radio spectrum‐sensing scheme in cognitive radio networks

ABSTRACTIn this paper, a novel spectrum‐sensing scheme, called adaptive dual‐radio spectrum‐sensing scheme (ADRSS), is proposed for cognitive radio networks. In ADRSS, each secondary user (SU) is equipped with a dual radio. During the data transmission, with the received signal‐to‐noise ratio of primary user (PU) signal, the SU transmitter (SUT) and the SU receiver (SUR) are selected adaptively to sense one channel by one radio while communicating with each other by the other one. The sensing results of the SUR are sent to the SUT through feedback channels (e.g., ACK). After that, with the sensing results from the SUT or the SUR, the SUT can decide whether the channel switching should be carried out. The theoretical analysis and simulation results indicate that the normalized channel efficiency, defined as the expected ratio of time duration without interference to PUs in data transmission to the whole frame length, can be improved while satisfying the interference constraint to PUs. After that, an enhanced ADRSS is designed by integrating ADRSS with cooperative spectrum sensing, and the performance of ADRSS under imperfect feedback channel is also discussed. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Optimal PET Protection for Streaming Scalably Compressed Video Streams With Limited Retransmission Based on Incomplete Feedback

For streaming scalably compressed video streams over unreliable networks, Limited-Retransmission Priority Encoding Transmission (LR-PET) outperforms PET remarkably since the opportunity to retransmit is fully exploited by hypothesizing the possible future retransmission behavior before the retransmission really occurs. For the retransmission to be efficient in such a scheme, it is critical to get adequate acknowledgment from a previous transmission before deciding what data to retransmit. However, in many scenarios, the presence of a stochastic packet delay process results in frequent late acknowledgements, while imperfect feedback channels can impair the server's knowledge of what the client has received. This paper proposes an extended LR-PET scheme, which optimizes PET-protection of transmitted bitstreams, recognizing that the received feedback information is likely to be incomplete. Similar to the original LR-PET, the behavior of future retransmissions is hypothesized in the optimization objective of each transmission opportunity. As the key contribution, we develop a method to efficiently derive the effective recovery probability versus redundancy rate characteristic for the extended LR-PET communication process. This significantly simplifies the ultimate protection assignment procedure. This paper also demonstrates the advantage of the proposed strategy over several alternative strategies.

Cooperative spectrum sensing with imperfect feedback channel in the cognitive radio systems

AbstractThe primary object of cognitive radio is to increase spectral efficiency, while causing limited interference to primary users who are using the spectrum. Hence, an essential part of cognitive radio is a spectrum sensing that determines whether a particular spectrum is occupied or not by a primary user. However, the sensing decision of a local secondary user alone may not be reliable enough due to shadowing and multipath fading of wireless channels. Recently, cooperative spectrum sensing has emerged as a remedy to the problems of local sensing. To sense the spectrum in a cooperative manner, secondary users report their local decisions to a cognitive base station through a feedback channel that is subject to fading and shadowing. Therefore, though the local sensing result is obtained accurately, it might not be suitable for making a cooperative decision due to feedback error. To alleviate this problem, we propose a hard decision combining‐based cooperative spectrum sensing scheme in the presence of a feedback error caused by imperfect channel condition. In the proposed scheme, only the most favorable secondary users, those whose reporting channel conditions are peak, are allowed for cooperation. Through the proposed scheme, we can maximize the detection probability while guaranteeing that the desired false alarm probability is maintained. The simulation results show that the proposed scheme provides better spectrum sensing performance than the conventional scheme. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Performance analysis of transmit beamforming for MISO systems with imperfect feedback

In this paper, we analyze the performance of transmit beamforming on multiple-antenna Rayleigh fading channels with imperfect channel feedback. We characterize the feedback imperfections in terms of noisy channel estimation, feedback delay, and finite-rate channel quantization. We develop a general framework, that is valid for an arbitrary two-dimensional linear modulation, to capture the aforementioned imperfections and derive the symbol and bit error probability expressions for both M-PSK and M-ary rectangular QAM constellations with Gray code mapping. We show that the proposed analytical formulation is valid for a frequency-domain duplexing system with/without finite-rate channel quantization and a time-domain duplexing system. We validate the accuracy of the analysis through simulations, and assess the relative effects of channel estimation inaccuracy, feedback delay, and finite-rate quantization on the symbol and bit error performances for various constellations.

Robust transmit processing for frequency-selective fading channels with imperfect channel feedback

Reliable channel state information at the transmitter (CSIT) can improve the throughput of wireless networks significantly. In a realistic scenario, there is a mismatch between the CSIT and the true channel. The CSIT may be outdated due to channel variations or could be erroneous due to quantization effects. In this paper, robust transmit beamforming (rBF) and the combination of time-reversal space-time block codes (TR-STBCs) with rBF, both optimized for transmission over frequency-selective fading channels with decision-feedback equalization (DFE), are considered. The reliability of the CSIT is taken into account for the design of channel state dependent transmit signal processing algorithms to achieve robustness against imperfect CSIT. Noisy (quantized) CSIT, outdated CSIT, and the combination of both cases are considered. We show that the performance of the rBF scheme is upper and lower bounded by that of transmit BF with perfect CSIT and optimized delay diversity (ODD) without instantaneous CSIT, respectively. The combination of a TR-STBC with rBF yields significant BF gains in addition to the diversity gains of the TR-STBC for reliable CSIT, whereas for unreliable CSIT, the performance is identical to that of the TR-STBC.

Space-time transmit precoding with imperfect feedback

The use of channel feedback from receiver to transmitter is standard in wireline communications. While knowledge of the channel at the transmitter would produce similar benefits for wireless communications as well, the generation of reliable channel feedback is complicated by the rapid time variations of the channel for mobile applications. The purpose of this paper is to provide an information-theoretic perspective on optimum transmitter strategies, and the gains obtained by employing them, for systems with transmit antenna arrays and imperfect channel feedback. The spatial channel, given the feedback, is modeled as a complex Gaussian random vector. Two extreme cases are considered: mean feedback, in which the channel side information resides in the mean of the distribution, with the covariance modeled as white, and covariance feedback, in which the channel is assumed to be varying too rapidly to track its mean, so that the mean is set to zero, and the information regarding the relative geometry of the propagation paths is captured by a nonwhite covariance matrix. In both cases, the optimum transmission strategies, maximizing the information transfer rate, are determined as a solution to simple numerical optimization problems. For both feedback models, our numerical results indicate that, when there is a moderate disparity between the strengths of different paths from the transmitter to the receiver, it is nearly optimal to employ the simple beamforming strategy of transmitting all available power in the direction which the feedback indicates is the strongest.