Individual Channel State Information Research Articles

A Framework for Transmission Design for Active RIS-Aided Communication With Partial CSI

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to compensate for the severe multiplicative fading effect of conventional passive RIS-aided systems. Each reflecting element of active RISs is assisted by an amplifier such that the incident signal can be reflected and amplified instead of only being reflected as in passive RIS-aided systems. This work addresses the practical challenge that, on the one hand, in active RIS-aided systems the perfect individual channel state information (CSI) of the RIS-aided channels cannot be acquired due to the lack of signal processing power at the active RISs, but, on the other hand, this CSI is required to calculate the expected system data rate and RIS transmit power needed for transceiver design. To address this issue, we first derive closed-form expressions for the average achievable rate and the average RIS transmit power based on partial CSI of the RIS-aided channels. Then, we formulate an average achievable rate maximization problem for jointly optimizing the active beamforming at both the base station (BS) and the RIS. This problem is then tackled using the majorization–minimization (MM) algorithm framework, and, in each iteration low-complexity solutions for the BS and RIS beamforming are found based on the Karush-Kuhn-Tucker (KKT) conditions. To ensure the quality of service (QoS) of each user, we further formulate a rate outage constrained beamforming problem, which is solved using the Bernstein-Type inequality (BTI) and semidefinite relaxation (SDR) techniques. Numerical results show that the proposed algorithms can efficiently overcome the challenges imposed by imperfect CSI in active RIS-aided wireless systems.

Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication

Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving high rate and large coverage in future wireless networks by smartly reflecting the signals with adjustable phase shifts. To design the reflection beamforming, accurate individual channel state information is required at the RIS, which is a challenge task due to the lack of signal processing ability in passive mode. In this paper, we add signal processing units for a few antennas at the RIS to partially acquire the channels and extrapolate them to the full channels, in which the active antenna selection is a key point but has not been addressed yet. We construct an active antenna selection network that utilizes the probabilistic sampling theory to select the optimal locations of these active antennas. With this active antenna selection network, we further design two deep learning-based schemes, i.e., the channel extrapolation scheme and the beam searching scheme. The former utilizes the selection network and a convolutional neural network to extrapolate the full channels from the partial channels, while the latter adopts a fully-connected neural network to achieve the direct mapping from the partial channels to the optimal beamforming vector with maximal transmission rate. Simulation results show that the proposed optimal antenna selection outperforms the trivial uniform antenna selection, and the performance of beam searching is more stable than that of channel extrapolation with fewer active antennas.

Outage probability analysis of overlay cognitive two‐way relaying scheme with opportunistic relay selection

The authors investigate an overlay cognitive two-way relaying system where max-min (MM) relay selection scheme is employed for improving the reliability of the considered system. Furthermore, they design the process of the data forwarding and relay selection in the overlay cognitive two-way relaying system. Based on the MM relay selection criterion, the exact close-form expression of outage probability is derived. From numerical simulations, it is observed that there is an outage probability floor appearing in the high signal-to-noise ratio (SNR) region, and the reason for its occurrence is stated. Besides, the approximate result of outage probability in high SNR is provided, which shows that the ratio of interference channel' transmit power and main channel' transmit power will not affect the outage probability in low SNR region, but it will significantly affect it in the high SNR region. Although each individual channel state information (CSI) will not be able to control the outage performance of this considered system in the high SNR region, the ratio of each channel's CSI will be able to do the same.

A Distributed Power Control Algorithm for Energy Efficiency Maximization in Wireless Cellular Networks

In this paper, we propose a distributed power control algorithm for addressing the global energy efficiency (GEE) maximization problem subject to satisfying a minimum target SINR for all user equipments (UEs) in wireless cellular networks. We state the problem as a multi-objective optimization problem which targets minimizing total power consumption and maximizing total throughput, simultaneously, while a minimum target SINR is guaranteed for all UEs. We propose an iterative scheme executed in the UEs to control their transmit power using individual channel state information (CSI) such that the GEE is maximized in a distributed manner. We prove the convergence of the proposed iterative algorithm to its corresponding unique fixed point also shown by our numerical results. Additionally, simulation results demonstrate that our proposed scheme outperforms other algorithms in the literature and performs like the centralized algorithm executed in the base station and maximizes the GEE using the global CSI.

Robust Design for Massive CSI Acquisition in Analog Function Computation Networks

Analog function computation utilizes the superposition property of multi-access channel to compute the target function in an efficient way. However, its corresponding transceiver requires global channel state information (CSI) of the network, which incurs large latency. To tackle this challenge, a novel scheme called over-the-air signaling procedure is proposed by exploiting a defined effective CSI in this paper. We first derive the training complexity of the proposed scheme and compare it with the conventional design. It is shown that the training complexity of the proposed scheme can be greatly reduced for massive CSI acquisition by avoiding collecting individual CSI. To account for the difference of the desired CSI, a corresponding robust model is further discussed. Through modeling the channel uncertainties under the expectation-based model and the worst case model, we formulate the transceiver optimization for both the conventional scheme and the over-the-air signaling procedure. The computational time complexity is derived as a polynomial expression, and it can be significantly reduced for the over-the-air signaling procedure due to its independence of the number of nodes. Finally, the mean-square error improvement and complexity reduction of the proposed design are demonstrated via simulation.

Performance Analysis of Cooperative Multicell Precoding With Global CSI and Local Individual CSI in the Large Dimensional Regime

Channel state information (CSI) is critical for the performance of cooperative multicell transmission. In this paper, the performances of cooperative multicell precoding under global CSI and local individual CSI are analyzed and compared in the large dimensional regime. Using theoretical results from large random matrix theory, we derive large dimensional approximations of signal-to-interference-plus-noise ratio for both precoding cases. Such approximations depend only on the slow fading channel statistics instead of the fast fading channel realizations. It is easy to quantify the performance loss caused by local individual CSI in the large dimensional regime. Two different massive MIMO scenarios are considered. When the number of antennas and that of users go large at the same rate, there is a constant gap between the two CSI cases. When the number of antennas goes large, while the number of users is fixed, the performances of both CSI cases are the same. This means that the impact of limited local individual CSI vanishes. Numerical results validate the theoretical analysis and show that there is constant performance loss due to the price of limited CSI. Also, the performance of precoding with local individual CSI almost decreases linearly with the increased number of BSs for the first scenario.

Individual Channel Estimation for AF-Based Relaying Systems Using In-Band Pilots

In amplify and forward cooperative relaying system, the knowledge of individual channel state information (CSI) is essential for coherent decoding and optimal design. To assist in the estimation of the individual channels, we proposed an in-band pilot strategy, where some cooperative data at the relay are discarded to insert pilots that can be utilized to estimate the relay to destination link. The new cooperative strategy implies that relay not only helps the source to transmit cooperative data, meanwhile, assisting the destination to acquire the individual CSI at the cost of cooperative information. Although some useful data are lost due to insertion of in-band pilots, the detection performance can be maintained with a simple iterative reconstruction method. Furthermore, if the direct link is available, the received signals from both the source and the relay can be combined to achieve spatial diversity. Simulation results are presented to assess the performance of the proposed schemes.

Blind channel estimation and signal retrieving for MIMO relay systems

In this paper, we propose a blind channel estimation and signal retrieving algorithm for two-hop multiple-input multiple-output (MIMO) relay systems. This new algorithm integrates two blind source separation (BSS) methods to estimate the individual channel state information (CSI) of the source-relay and relay-destination links. In particular, a first-order Z-domain precoding technique is developed for the blind estimation of the relay-destination channel matrix, where the signals received at the relay node are pre-processed by a set of precoders before being transmitted to the destination node. With the estimated signals at the relay node, we propose an algorithm based on the constant modulus and signal mutual information properties to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, the proposed algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.

Cross-layer distributed power control: a repeated game formulation to improve the sum energy efficiency

The main objective of this work is to improve the energy-efficiency (EE) of a multiple access channel (MAC) system, through power control, in a distributed manner. In contrast with many existing works on energy-efficient power control, which ignore the possible presence of a queue at the transmitter, we consider a new generalized cross-layer EE metric. This approach is relevant when the transmitters have a non-zero energy cost even when the radiated power is zero and takes into account the presence of a finite packet buffer and packet arrival at the transmitter. As the Nash equilibrium (NE) is an energy-inefficient solution, the present work aims at overcoming this deficit by improving the global energy-efficiency. Indeed, as the considered system has multiple agencies each with their own interest, the performance metric reflecting the individual interest of each decision maker is the global energy-efficiency defined then as the sum over individual energy-efficiencies. Repeated games (RG) are investigated through the study of two dynamic games (finite RG and discounted RG), whose equilibrium is defined when introducing a new operating point (OP), Pareto-dominating the NE and relying only on individual channel state information (CSI). Accordingly, closed-form expressions of the minimum number of stages of the game for finite RG (FRG) and the maximum discount factor of the discounted RG (DRG) were established. The cross-layer model in the RG formulation leads to achieving a shorter minimum number of stages in the FRG even for higher number of users. In addition, the social welfare (sum of utilities) in the DRG decreases slightly with the cross-layer model when the number of users increases while it is reduced considerably with the Goodman model. Finally, we show that in real systems with random packet arrivals, the cross-layer power control algorithm outperforms the Goodman algorithm.

On the Adaptive Sum-Capacity of Distributed Multiple Access with Individual CSI

In wireless communications, a fading multiple-access channel (MAC) is typically used to model the uplink communication. Conventional MACs assume a centralized system, where the transmission rate and power are chosen centrally for every fading vector realization. On the other hand, there is considerable interest in the performance of distributed multiple access systems, where the lack of global channel state information (CSI) demands novel communication strategies. We consider a block-fading MAC where each transmitter is aware only of its own link CSI, which we term as the individual CSI MAC. The receiver has access to the full CSI of all links. This model was recently introduced in the information theory literature, and naturally leads to a distributed access system with several applications. An important utility of interest for this model is known as the power-controlled adaptive sum-capacity, whose evaluation is an open problem. This is the main subject of the current paper. We present the power-controlled adaptive sum-capacity of a wide class of popular fading MAC models. In particular, we characterize the sum-capacity when the statistics of the channel are identical across users. The proposed schemes also allow a low-complexity successive-cancellation decoding using rate-splitting. Furthermore, the optimal schemes are extended to situations in which each transmitter has additional finite-rate partial CSI on the link quality of others.

Channel Estimation for Two-Way MIMO Relay Systems in Frequency-Selective Fading Environments

In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We apply the method of superimposed channel training to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the mean-squared error (MSE) of channel estimation. Moreover, the optimal power allocation between the source and relay training sequences is derived to improve the performance of channel estimation. Numerical examples are shown to demonstrate the performance of the proposed superimposed channel training algorithm for two-way MIMO relay systems in frequency-selective fading environments.

Channel Training Algorithms for Two-Way MIMO Relay Systems

Two-way relay systems are known to be capable of providing higher spectral efficiency compared with conventional one-way relay systems. However, the channel estimation problem for two-way relay systems is more complicated than that of one-way relay systems. In this paper, we propose and compare two channel estimation algorithms, namely the superimposed channel training scheme and the two-stage channel estimation algorithm, for two-way multiple-input multiple-output (MIMO) relay communication systems, where the individual channel state information (CSI) for the first-hop and second-hop links is estimated. For both algorithms, we derive the optimal structure of the source and relay training sequences which minimize the mean-squared error (MSE) of channel estimation. In the superimposed channel training scheme, the power allocation between the source and relay training sequences is optimized. For the two-stage channel estimation algorithm, we optimize the power allocation at the relay node between two stages to improve the performance of the algorithm. Numerical examples are shown to demonstrate and compare the performance of the proposed channel training algorithms.

Hermitian Precoding for Distributed MIMO Systems with Individual Channel State Information

We consider a distributed multiple-input multiple-output (MIMO) system in which multiple transmitters cooperatively serve a common receiver. It is usually very costly to acquire full channel state information at the transmitter (CSIT) in such a scenario, especially for large-scale antenna systems. In this paper, we assume individual CSIT (I-CSIT), i.e., each transmitter has perfect CSI of its own link but only slow fading factors of the others. A linear Hermitian precoding technique is proposed to enhance the system performance. The optimality of the proposed precoding technique is analyzed. Numerical results demonstrate that the performance loss incurred by the I-CSIT assumption is negligible as compared to the full-CSIT case.

Performance of MUTP-aided MIMO systems over correlated frequency-selective wireless communication channels: a multi-cell perspective

In this article, we investigate the performance of multiuser transmitter preprocessing (MUTP)-aided multiple-input multiple-output (MIMO) systems in a multi-cell multiuser setting where co-channel interference (CCI) is the major channel impairment, for both uplink (UL) and downlink (DL) transmissions. CCI can considerably reduce data rates resulting in outages in cellular systems, particularly at the cell edges in DL transmission. The MUTP considered in this article is based on singular value decomposition (SVD), which exploits the channel state information (CSI) of all the users at the base stations (BSs) with the aid of BS cooperation, and only the individual users' CSI at the mobile stations (MSs) for both UL and DL transmissions. In particular, in this article, we study the effects of three types of delay spread distributions coupled with different interferer configurations over correlated and uncorrelated frequency-selective channels. Our simulation study shows that SVD-aided MUTP perfectly eliminates CCI with lesser detection complexity under perfect CSI. Also, we provide performance comparisons of SVD-aided MUTP with various precoding techniques widely addressed in literature, and the results show that it provides better achievable symbol error rate (SER) by mitigating multi-stream interference (MSI) and CCI. Further, simulation results demonstrate that compared to equal CCI, the presence of a dominant interferer can lead to more degradation in the system performance in terms of achievable SER while, further degradation results when noise is dominant. Furthermore, this study confirms that imperfect CSI as well as imperfect power control can lead to degradation in the system performance.

A Repeated Game Formulation of Energy-Efficient Decentralized Power Control

Decentralized multiple access channels where each transmitter wants to selfishly maximize this transmission energy-efficiency are considered. Transmitters are assumed to choose freely their power control policy and interact (through multiuser interference) several times. It is shown that the corresponding conflict of interest can have a predictable outcome, namely a finitely or discounted repeated game equilibrium. Remarkably, it is shown that this equilibrium is Pareto-efficient under reasonable sufficient conditions and the corresponding decentralized power control policies can be implemented under realistic information assumptions: only individual channel state information and a public signal are required to implement the equilibrium strategies. Explicit equilibrium conditions are derived in terms of minimum number of game stages or maximum discount factor. Both analytical and simulation results are provided to compare the performance of the proposed power control policies with those already existing and exploiting the same information assumptions namely, those derived for the one-shot and Stackelberg games.

MIMO-CDMA systems: signature and beamformer design with various levels of feedback

We investigate the signature and beamformer design problem for the uplink of multiple-input multiple-output code-division multiple-access (MIMO-CDMA) systems with the sum capacity and the systemwide mean-square error (MSE) as performance metrics. We first construct iterative algorithms to find the jointly optimum temporal signatures and transmit beamformers under the assumption of perfect temporal signature and transmit beamforming feedback. Next, motivated by the need to reduce the amount of feedback, we present a low-complexity sequential orthogonal temporal signature assignment algorithm for given transmit beamformers. Our approach is based on minimizing the difference between the performance of the MIMO-CDMA system and the described upper bounds at each signature assignment step. We next note that the transmit beamformers can be shaped depending on the channel state information (CSI) available at the transmitter. We investigate the cases of various levels of available feedback resulting in different beamformer structures and present a joint orthogonal temporal signature assignment and beamforming algorithm. We observe that as the available feedback level is increased, the performance of the joint signature and beamformer assignment algorithm approaches the performance upper bounds. In particular, we observe that a substantial performance gain is obtained when the individual channel state information is available at the transmitter side

Time-Slotted Multiuser MIMO Systems: Beamforming and Scheduling Strategies

We investigate the problem of scheduling for the uplink of a time-slotted multiuser multiple-input multiple-output (MIMO) system with sum capacity as the performance metric. We first consider scheduling users' transmissions for fixed transmit beamformers. Upper bounds for the sum capacity of the time-slotted system with the optimum scheduler are found via relaxing a set of structural constraints. Next, we present a low-complexity scheduling algorithm that aims to approach the capacity's upper bound. The performance of the multislot multiuser MIMO system is a function of the users' transmit beamformers. In turn, the transmit beamformers can be shaped depending on the channel state information available at the transmitter. We investigate how the transmit beamformers should be chosen with different levels of feedback, and combine the proposed scheduling algorithm with antenna selection, eigen transmit beamforming, and perfectly controlled transmit beamforming models. We observe that as the available feedback level is increased, the performance of the scheduling algorithm approaches the upper bounds developed. In particular, a substantial sum capacity gain is attained when the individual channel state information is available at the transmitter side.