Hybrid cooperation through full-duplex opportunistic relaying and max-link relay selection with transmit power adaptation

Cloud radio access network (Cloud-RAN) is a promising network architecture to meet the explosive growth of the mobile data traffic. In this architecture, as all the baseband signal processing is shifted to a single baseband unit (BBU) pool, interference management can be efficiently achieved through coordinated beamforming, which, however, often requires full channel state information (CSI). In practice, the overhead incurred to obtain full CSI will dominate the available radio resource. In this paper, we propose a unified framework for the CSI overhead reduction and downlink coordinated beamforming. Motivated by the channel heterogeneity phenomena in large-scale wireless networks, we first propose a novel CSI acquisition scheme, called compressive CSI acquisition, which will obtain instantaneous CSI of only a subset of all the channel links and statistical CSI for the others, thus forming the mixed CSI at the BBU pool. This subset is determined by the statistical CSI. Then we propose a new stochastic beamforming framework to minimize the total transmit power while guaranteeing quality-of-service (QoS) requirements with the mixed CSI. Simulation results show that the proposed CSI acquisition scheme with stochastic beamforming can significantly reduce the CSI overhead while providing performance close to that with full CSI.

With the final objective of analyzing the benefits of covariance-aided uplink multi-user channel state information (CSI) acquisition in massive MIMO systems, in this paper we compare the channel estimation mean-square error (MSE) for (i) conventional CSI acquisition, which does not assume any knowledge on the users’ individual spatial covariance matrices and uses orthogonal pilot sequences; and (ii) covariance-aided CSI acquisition, which exploits the individual covariance matrices (and especially their low rank) for channel estimation and possibly uses non-orthogonal pilot sequences. We apply a large- system analysis to the latter case and provide insights about how much the CSI acquisition process can be overloaded (in the sense of allowing estimating CSI with sufficient accuracy for more users than the number resource elements allocated for training) when a covariance-aided approach is adopted. This hints at potentially significant gains in the spectral efficiency of CSI acquisition in Massive MIMO.

This paper presents indoor and outdoor experimental results on 4-by-8 single-user (SU)-multiple-input multiple-output (MIMO) multiplexing based on 730.5 MHz bandwidth transmission when applying carrier aggregation (CA) with 8 component carriers (CCs) in a 15 GHz frequency band in the downlink of 5G cellular radio access. Beam tracking with massive MIMO is implemented in a 5G testbed to support user mobility with a narrow beam. In addition, channel state information (CSI) acquisition functionality is implemented for the scheduler to select appropriate beams. Experimental results in an indoor multi-path rich environment show the gain up to 34 % with CSI acquisition compared to the case of without CSI acquisition in a line-of-sight (LoS) environment with high rank MIMO transmission. The results also show the gain up to 55 % with CSI acquisition compared to the case of without CSI acquisition in LoS conditions due to reflections by buildings.

The deployment of multiple reconfigurable intelligent surfaces (RISs) enhances the propagation environment by improving channel quality, but it also complicates channel estimation. Following the conventional wireless communication system design, which involves full channel state information (CSI) acquisition followed by RIS configuration, can reduce transmission efficiency due to substantial pilot overhead and computational complexity. This study introduces an innovative approach that integrates CSI acquisition and RIS configuration, leveraging the channel-altering capabilities of the RIS to reduce both the overhead and complexity of CSI acquisition. The focus is on multi-RIS-assisted systems, featuring both direct and reflected propagation paths. By applying a fast-varying reflection sequence during RIS configuration for channel training, the complex problem of channel estimation is decomposed into simpler, independent tasks. These fast-varying reflections effectively isolate transmit signals from different paths, streamlining the CSI acquisition process for both uplink and downlink communications with reduced complexity. In uplink scenarios, a positioning-based algorithm derives partial CSI, informing the adjustment of RIS parameters to create a sparse reflection channel, enabling precise reconstruction of the uplink channel. Downlink communication benefits from this strategically tailored reflection channel, allowing effective CSI acquisition with fewer pilot signals. Simulation results highlight the proposed methodology’s ability to accurately reconstruct the reflection channel with minimal impact on the normalized mean square error while simultaneously enhancing spectral efficiency.

Sparse Code Multiple Access (SCMA), a non-orthogonal multiple access scheme, has been introduced as a key 5G technology to improve spectral efficiency. In this work, we propose SCMA to enable open-loop coordinated multipoint (CoMP) joint transmission (JT). The scheme combines CoMP techniques with multi-user SCMA (MU-SCMA) in downlink. This scheme provides open-loop user multiplexing and JT in power and code domains, with robustness to mobility and low overhead of channel state information (CSI) acquisition. The combined scheme is called MU-SCMA- CoMP, in which SCMA layers and transmit power of multiple transmit points (TPs) are shared among multiple users while a user may receive multiple SCMA layers from multiple TPs within a CoMP cluster. The benefits of the proposed scheme includes: i) drastic overhead reduction of CSI acquisition, ii) significant increase in throughput and coverage, and iii) robustness to channel aging. Various algorithms of MU-SCMA-CoMP are presented, including the detection strategy, power sharing optimization, and scheduling. System level evaluation shows that the proposed schemes provide significant throughput and coverage gains over OFDMA for both pedestrian and vehicular users.

Multiuser multiple-input-multiple-output (MU-MIMO) systems are known to be hindered by dimensionality loss due to channel state information (CSI) acquisition overhead. In this paper, we investigate user-scheduling in MU-MIMO systems on account of CSI acquisition overhead, where a base station dynamically acquires user channels to avoid choking the system with CSI overhead. The genie-aided optimization problem (GAP) is first formulated to maximize the Lyapunov-drift every scheduling step, incorporating user queue information and taking channel fluctuations into consideration. The scheduling scheme based on GAP, namely the GAP-rule, is proved to be throughput-optimal but practically infeasible, and thus serves as a performance bound. In view of the implementation overhead and delay unfairness of the GAP-rule, the T-frame dynamic channel acquisition scheme and the power-law DCA scheme are further proposed to mitigate the implementation overhead and delay unfairness, respectively. Both schemes are based on the GAP-rule and proved throughput-optimal. To make the schemes practically feasible, we then propose the heuristic schemes, queue-based quantized-block-length user scheduling scheme (QQS), T-frame QQS, and power-law QQS, which are the practical versions of the aforementioned GAP-based schemes, respectively. The QQS-based schemes substantially decrease the complexity, and also perform fairly close to the optimum. Numerical results evaluate the proposed schemes under various system parameters.

This paper considers the channel state information (CSI) acquisition and exploitation problem in cloud radio access networks (Cloud-RAN). A novel CSI acquisition method, called compressive CSI acquisition, is adopted to effectively reduce the CSI signaling overhead by obtaining instantaneous coefficients of only a subset of all the channel links. To deal with the uncertainty in available CSI, we propose a stochastic power control (SPC) framework, which is a highly intractable joint chance constrained program (JCCP). In contrast to conventional works, which can only find sub-optimal solutions, we propose a novel stochastic power control algorithm with optimality guarantee. Simulation results will show that the proposed compressive CSI acquisition method can reduce the overhead significantly. Moreover, the proposed SPC algorithm provides performance close to the full CSI case while only partial CSI is required.

Power adaptation has been widely studied in the literature, given its significance in designing power efficient adaptive wireless systems. Specifically, transmit power adaptation is an important technique that is naturally appealing for underlay cognitive radio systems, which are intelligent and reconfigurable. In this paper, we consider a secondary underlay transmitter whose transmissions are constrained by an average interference threshold, also it adapts its transmit power as a function of its local channel state information (CSI). In this paper, we derive the optimal power adaptation factor (PAF) that maximizes the fading-averaged bandwidth efficiency (FABE) and also calculate the corresponding energy efficiency. We develop insightful analysis for average spectral efficiency, that is, FABE and energy efficiency for two scenarios: i). CSI-independent PAF and ii). CSI-dependent PAF. Our numerical results reveal that FABE for transmit power adaptation with CSI-dependent PAF delivers superior performance than the PAF that does not depend on instantaneous CSI at the expense of slight decrease in energy efficiency.

The shift in Multi-User Multiple Input Multiple Output (MU-MIMO) has gained attention due to its wide support in very high throughput Wireless Local Area Networks (WLANs) such as the 802.11ac. However, the full advantage of MU-MIMO can be utilized only with proper user selection and scheduling. Also, providing Quality of Service (QoS) support is a major challenge for these wireless networks. Generally, user scheduling is done with the acquisition of Channel State Information (CSI) from all the users. In MU-MIMO based WLANs, the number of CSI request increases with the number of users. This results in an increased CSI overhead and in degradation of the overall throughput. Most of the proposals in the literature have not addressed the contention in the CSI feedback clearly. Hence, in this paper a Joint User Selection and Scheduling (JUSS) scheme is discussed and its performance is evaluated in terms of throughput, delay, packet loss and fairness. In the performance comparison some wellknown Medium Access Control (MAC) protocols are considered. The proposed scheme not only enhances throughput, but also avoids contention during CSI feedback period.

Massive multiple-input multiple-output (MIMO) systems use antenna arrays with a large number of antenna elements to serve many different users simultaneously. The large number of antennas in the system makes, however, the channel state information (CSI) acquisition strategy design critical and particularly challenging. Interestingly, in the context of massive MIMO systems, channels exhibit a large degree of spatial correlation which results in strongly rank-deficient spatial covariance matrices at the base station (BS). With the final objective of analyzing the benefits of covariance-aided uplink multi-user CSI acquisition in massive MIMO systems, here we compare the channel estimation mean-square error (MSE) for (i) conventional CSI acquisition, which does not assume any knowledge on the user spatial covariance matrices and uses orthogonal pilot sequences; and (ii) covariance-aided CSI acquisition, which exploits the individual covariance matrices for channel estimation and enables the use of non-orthogonal pilot sequences. We apply a large-system analysis to the latter case, for which new asymptotic MSE expressions are established under various assumptions on the distributions of the pilot sequences and on the covariance matrices. We link these expressions to those describing the estimation MSE of conventional CSI acquisition with orthogonal pilot sequences of some equivalent length. This analysis provides insights on how much training overhead can be reduced with respect to the conventional strategy when a covariance-aided approach is adopted.

Deep learning based channel state information (CSI) acquisition and feedback in frequency division duplex systems have drawn much attention in the beyond fifth-generation (B5G) wireless systems. In this paper, we focus on exploiting the CSI codebook in B5G wireless standards with deep learning to enhance the performance of CSI acquisition and feedback. Specifically, the angular-delay-domain partial reciprocity between uplink and downlink channels is considered, and part of angular-delay-domain ports are selected for measuring and feeding back the downlink CSI, where the performance of the conventional deep learning methods is limited due to the deficiency of sparse structures. To address this issue, we propose the new paradigm of adopting deep learning to improve the performance of CSI codebook. Firstly, considering the relatively low signal-to-noise ratio of uplink channels, deep learning is utilized to refine the selection of the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to reconstruct the downlink CSI by way of deep learning based on the feedback of CSI codebook at the base station, where the information of sparse structures can be effectively leveraged. Finally, a weighted shortcut module is designed to facilitate the accurate reconstruction, and a two-stage loss function with the combination of the mean squared error and sum rate is proposed for adapting to actual multi-user scenarios. Simulation results demonstrate that our proposed angular-delay-domain port selection and CSI reconstruction paradigm can improve the sum rate performance by more than 10% compared with the standard CSI codebook and traditional deep learning benchmarks.

We propose a low-complexity approach for the downlink of physically constrained massive multiple-input multiple-output (MIMO) systems with user mobility. We examine a channel state information (CSI) acquisition strategy that exploits both the spatial and temporal correlations among the channels of adjacent base station antennas. The proposed strategy solely collects CSI for a subset of antennas and time frames. Then full CSI is approximated using the CSI of adjacent antennas and previous frames. This critically reduces the CSI acquisition complexity while sacrificing the CSI quality and, hence, introduces a scalable performance-complexity tradeoff. The numerical results demonstrate that, for practical mobile speeds, the proposed scheme reduces the computational complexity and enhances the energy efficiency of massive MIMO base stations against systems with complete CSI, while approximately preserving performance.

We consider power and rate adaptations in multicarrier (MC) direct-sequence code-division multiple-access (DS/CDMA) communications under the assumption that channel state information is provided at both the transmitter and the receiver. We propose, as a power allocation strategy in the frequency domain, to transmit each user's DS waveforms over the user's sub-band with the largest channel gain, rather than transmitting identical DS waveforms over all sub-bands. We then adopt channel inversion power adaptation in the time domain, where the target user's received power level maintains at a fixed value. We also investigate rate adaptation in the time domain, where the data rate is adapted such that a desired transmission quality is maintained. We analyze the BER performance of the proposed power and rate adaptations with fixed average transmission power, and show that power adaptation in both the frequency and the time domains or combined power adaptation in the frequency domain and rate adaptation in the time domain make significant performance improvement over the power adaptation in the frequency domain only. We also compare the performance of the proposed power and rate adaptation schemes in MC-DS/CDMA systems to that of power and rate adapted single carrier DS/CDMA systems with RAKE receiver.

With the evolution of wireless networks, new techniques including massive multiple-input multiple- output (MIMO) and millimeter wave are adopted to satisfy the demands for diversified services. However, it has been verified by field tests that the traditional wide sense stationary assumption for wireless channel does not hold anymore. As a result, traditional channel state information (CSI) acquisition methods, especially the statistical CSI acquisition, cannot be applied straightforwardly in such a circumstance. In this paper, we propose a pre-processing method for channel sensing in the non-stationary environment. Specifically, the data sampled from channel training is treated as a channel image, where the statistical channel state is represented by gray-scale. Then the computer vision technique, specifically, the edge detection method, is used on the channel image to detect the homogeneous sub-regions. Within each sub-region, the channel is statistically stationary, and then the CSI can be obtained by existing methods. It is verified by simulation results that, the proposed method can help to improve the CSI acquisition accuracy in the non- stationary environment.

In bidirectional atmospheric channel transmission with channel reciprocity, the correlation between two transmission channel turbulence noises is high, and different techniques can be used to extract channel state information (CSI) in forward transmission, and adaptive power techniques can be used to inhibit turbulence effects in reverse transmission to improve the performance of free-space optical (FSO) systems. In atmospheric FSO communication systems, the scintillation response from turbulence effects increases the bit error rate (BER) of the communication system and reduces the system performance. In this paper, we propose two different adaptive power transmission (APT) techniques, namely gated recurrent unit (GRU)-based APT with cascading LPF and recurrent neural network (RNN)-based APT with cascading LPF, which utilizes the CSI of the channel for adaptive transmission to reduce BER. The proposed adaptive power transfer technique can improve the BER performance of the system and effectively mitigate the scintillation effect caused by atmospheric turbulence on the FSO communication system. A bidirectional atmospheric channel with different turbulence intensities is constructed in the simulation software, different background noises are added to change the channel reciprocity, the effect of reciprocity on the signal transmission is investigated, and the performance of different deep learning models in bidirectional channels. The future development of the technique is promising. According to the simulation results, APT technology based on deep learning achieves a lower bound that stabilizes at 10−4 ∼10−5 in turbulent channels under high signal-to-noise ratio conditions. Specifically, the LPF-RNN-APT technology excels due to its lightweight structure and parameter efficiency, delivering outstanding performance in strongly symmetric channels. Received: 30 November 2024 | Revised: 24 March 2025 | Accepted: 18 June 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Wen-Yao Liu: Investigation, Resources, Data curation, Writing – original draft, Visualization. Yan-Qing Hong: Conceptualization, Methodology, Validation, Formal analysis, Writing – review & editing, Supervision, Project administration. Xu Liu: Software, Validation, Resources. Xue-Heng Chen: Data curation, Writing – review & editing. Peng-Fei Lv: Formal analysis, Writing – review & editing.