Perfect Channel Knowledge Research Articles

Adaptive feedback bits and power allocation for dynamic TDD systems

In next-generation wireless communication systems, a dynamic time division duplex (TDD) system is promising to support simultaneous uplink (UL) and downlink (DL) transmissions. We herein consider a fixed number of feedback bits per DL user, where each DL user reports its quantized channel direction information (CDI) of the interference channel to a serving DL base station via its dedicated feedback channel. Based on the quantized CDI feedback, we design the UL and DL transmit zero-forcing beamforming (ZFBF) to mitigate the interference signals caused by the coexistence of the UL and DL transmissions. To maximize the DL data rate, the challenge of interference cancellation lies in how many feedback bits are adaptively allocated for the transmission of the quantized CDI of each channel link. This paper proposes an adaptive feedback bits allocation scheme which minimizes the mean rate loss between perfect channel knowledge and limited feedback system. We also propose the UL transmit power allocation scheme under the interference power constraint from the UL user to the DL user when the number of feedback bits for the quantized CDI is given. To jointly solve the feedback bits allocation and the UL transmit power allocation problems, we use an iterative scheme to maximize the DL data rate while satisfying the constraint of interference power. Finally, we investigate how many feedback bits per DL user are required to maintain a constant mean rate loss. The simulation results show that the proposed adaptive feedback bits allocation scheme significantly increases the DL data rate compared with the conventional schemes.

Iterative Approaches for Massive MIMO Uplink Processing Under Imperfect Channel Conditions

Obtaining channel state information in massive multiple-input multiple-output (MIMO) systems is challenging because of significant multiple-access interference and very limited resources available for channel estimation. Most existing equalization schemes for massive MIMO systems assume perfect channel knowledge at the receivers. When channel estimation error is present, however, significant performance degradation will be experienced, which has been demonstrated in the literature. In this paper, we model the maximum-likelihood channel estimation as the sum of real channel and random channel estimation error, and develop a minimum mean-square error (MMSE) based turbo equalization method conditioned on the channel estimate. We demonstrate that our newly developed iterative processing approach is a general expression, whereas existing methods can be viewed as special cases. We further derive an MMSE-sorted QR decomposition (MMSE-SQRD) based turbo equalization to further improve the performance for massive MIMO systems under large user numbers and severe channel estimation error conditions. Numerical results and performance comparison show that the proposed MMSE-based turbo equalization scheme greatly outperforms the existing schemes. With a large number of users being simultaneously served, our proposed MMSE-SQRD-based scheme can further enhance system performance over the MMSE-based turbo equalization scheme.

Weighted MMSE Precoder Designs for Sum-Utility Maximization in Multi-User SWIPT Network-MIMO With Per-BS Power Constraints

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to transfer information and energy signals at the same time and frequency utilizing the multi-input multi-output (MIMO) beamforming. In this correspondence, the SWIPT MIMO technique is realized in the multicell cooperative transmission environment, namely, network-MIMO where the signals from different base-stations (BSs) to all the mobile users can be jointly designed based on the perfect knowledge of the downlink channels and transmit messages. To develop an efficient precoding matrix, we adopt the weighted minimum mean squared error criterion that can be generally applied to the sum-utility maximization of each receiver's information rate. In the SWIPT network-MIMO, a number of constraints arise including the power constraint at each BS, namely, the per-BS power constraints and the energy constraint at the energy harvesting users, which makes the problem challenging. A main contribution of the paper is to propose a simple bisection-based algorithm that finds a solution. The efficiency of the proposed algorithm is verified via computer simulation results.

Analysis of channel uncertainty in ARQ relay networks.

Several power allocation algorithms for cooperative relay networks are presented in the literature. These contributions assume perfect channel knowledge and capacity achieving codes. However in practice, obtaining the channel state information at a relay or at the destination is an estimation problem and can generally not be error free. The investigation of the power allocation mechanism in a wireless network due to channel imperfections is important because it can severely degrade its performance regarding throughput and bit error rate. In this paper, the impact of imperfect channel state information on the power allocation of an adaptive relay network is investigated. Moreover, a framework including Automatic Repeat reQuest (ARQ) mechanism is provided to make the power allocation mechanism robust against these channel imperfections. For this framework, the end-to-end SNR is calculated considering imperfect channel knowledge using ARQ analytically. The goal is to emphasize the impact of imperfect channel knowledge on the power allocation mechanism. In this paper, the simulation results illustrate the impact of channel uncertainties on the average outage probability, throughput, and consumed sum power for different qualities of channel estimation. It is shown that the presented framework with ARQ is extremely robust against the channel imperfections.

A Framework to Channel Feedback and Reconstruction using Matrix Completion in Massive MIMO Systems

In this paper, we are interested in the problem of channel feedback and reconstruction in frequency division duplexing (FDD)-based massive multiple-input-multiple-output (MIMO) systems. We propose a general framework that allows reducing the overhead significantly in the uplink feedback control channel when assuming massive antenna arrays at both ends of the wireless link. We investigate the methods capitalize on matrix completion to reducing the feedback overhead. The fundamental idea of the proposed method is to explore the low-rank structure of the channel for its accurate reconstruction at the transmitterside (Tx) with very few uplink feedback information, using matrix completion technique. The proposed framework consisting of two stages. First, upon reception of downlink pilots, the receiver side (Rx) undersamples either the received pilot data matrix or the estimated channel matrix and feeds back only a fraction of their entries to the Tx, throwing away the remaining ones. Then, under the assumption of reduced scattering propagation, the Tx capitalizes on matrix completion to recover either the downlink pilots or directly reconstruct the downlink channel. We consider two application examples: i) backhauling communication and ii) a multi-user equipment (UE) scenario with perfect andimperfect instantaneous channel knowledge. Due to data/channel undersampling, energy consumption at a receiver can be reduced during the uplink feedback transmission. Simulation results show that, compared to the conventional full-feedback approach, which requires feedback of the entire matrix, the proposed solution can decrease the feedback overload in more than 90% for low-rank channels, while providing good channel estimation accuracy, bit error rate (BER) under maximum ratio transmission (MRT) precoding and goodput.

Reinforcement Learning-Based Adaptive Transmission in Time-Varying Underwater Acoustic Channels

This paper studies adaptive transmission in an underwater acoustic (UWA) point-to-point communication system that operates on an epoch-by-epoch basis for a long term. A fixed amount of information bits periodically arrive at the transmitter data queue, and wait for transmission via a number of packets within each epoch. To trade off energy consumption with transmission latency, the transmitter decides the transmission action at the beginning of each epoch, including to transmit or not, and the transmission power and the modulation-and-coding parameters, based on the data queue status and the predicted channel conditions in the current and future epochs. To describe both the fast fading and the large-scale shadowing of UWA channels, the channel within each epoch is characterized by a compound Nakagami-lognormal distribution, and the evolution of the distribution parameters is modeled as an unknown Markov process. Given that the channel can only be observed during active transmissions, we formulate the adaptive transmission problem as a partially observable Markov decision process, and develop an online algorithm in a model-based reinforcement learning framework. The algorithm recursively estimates the channel model parameters, tracks the channel dynamics, and computes the optimal transmission action that minimizes a long-term system cost. Emulated results based on channel measurements from two-field experiments demonstrate that the proposed algorithm achieves decent performance relative to a benchmark method that assumes perfect and non-causal channel knowledge.

Low overhead channel tracking strategy for millimetre wave multiple‐input multiple‐output system

In this, study, a new channel tracking algorithm for time-varying millimetre wave (mmWave) system is proposed. The transmitter employs hybrid beamforming architecture which is generally adopted in mmWave system and the receiver is assumed to have perfect channel knowledge. Due to the short feedback interval in mmWave system, the authors propose a low overhead channel tracking algorithm in which the receivers feedback the channel state information every several channel indices. Obviously, the proposed channel tracking algorithm can reduce feedback times and improve feedback efficiency. Based on the mutual information analysis and 3 dB criterion on signal-to-noise ratio, the upper bound of the loss on system capacity and the maximum gap of discrete channel tracking, together with the desired number of data steam are given. Simulation results are presented to verify the analysis above and demonstrate the performance in discrete channel tracking.

Performance analysis of secure AF relay networks using cooperative jamming under outdated CSI

This paper studies cooperative transmission for securing a two-hop network where the eavesdropper can wiretap the relay channels. With outdated channel state information (CSI), we propose an opportunistic relaying with destination-based jamming scheme, where one relay is chosen to forward the confidential signal and the destination sends jamming signal to confuse the eavesdropper. To reveal the capability of the proposed scheme in improving the system secrecy, we investigate the two common performance metrics under different assumptions on the eavesdropper CSI. First, lower bound on the ergodic secrecy capacity (ESC) is derived assuming perfect knowledge of the eavesdropper channel gains. We, then, extend our results to the scenario where only the legitimate channel gain is known. Here, lower bound on the secrecy outage probability (SOP) is obtained. To further improve the utility of the destination, under total power constraint, a power allocation between data signal and jamming noise is considered. Both the analysis and simulation results indicate that the system performance is significantly improved in terms of both ESC and SOP when the jamming power at the destination is adjusted according to the selected relay position. We verify that, with outdated CSI, the secrecy performance of opportunistic relaying is severely degraded.

A Survey on Robust Interference Management in Wireless Networks

Recent advances in the characterization of fundamental limits on interference management in wireless networks and the discovery of new communication schemes on how to handle interference led to a better understanding towards the capacity of such networks. The benefits in terms of achievable rates of powerful schemes handling interference, such as interference alignment, are substantial. However, the main issue behind most of these results is the assumption of perfect channel state information at the transmitters (CSIT). In the absence of channel knowledge the performance of various interference networks collapses to what is achievable by time division multiple access (TDMA). Robustinterference management techniques are promising solutions to maintain high achievable rates at various levels of CSIT, ranging from delayed to imperfect CSIT. In this survey, we outline and study two main research perspectives of how to robustly handle interference for cases where CSIT is imprecise on examples for non-distributed and distributed networks, namely broadcast and X-channel. To quantify the performance of these schemes, we use the well-known (generalized) degrees of freedom (GDoF) metric as the pre-log factor of achievable rates. These perspectives maintain the capacity benefits at similar levels as for perfect channel knowledge. These two perspectives are: First,scheme-adaptationthat explicitly accounts for the level of channel knowledge and, second,relay-aided infrastructure enlargementto decrease channel knowledge dependency. The relaxation on CSIT requirements through these perspectives will ultimately lead to practical realizations of robust interference management techniques. The survey concludes with a discussion of open problems.

Binary Fading Interference Channel With No CSIT

We study the capacity region of the two-user binary fading (or erasure) interference channel, where the transmitters have no knowledge of the channel state information. We develop new inner bounds and outer bounds for this problem. We identify three regimes based on the channel parameters: weak, moderate, and strong interference regimes. Interestingly, this is similar to the generalized degrees of freedom of the two-user Gaussian interference channel, where transmitters have perfect channel knowledge. We show that for the weak interference regime, treating interference as erasure is optimal while for the strong interference regime, decoding interference is optimal. For the moderate interference regime, we provide new inner and outer bounds. The inner bound is based on a modification of the Han–Kobayashi scheme for the erasure channel, enhanced by time-sharing. We study the gap between our inner bound and our outer bounds for the moderate interference regime and compare our results to that of the Gaussian interference channel. Deriving our new outer bounds has three main steps. We first create a contracted channel that has fewer states compared with the original channel, in order to make the analysis tractable. We then prove the correlation lemma that shows an outer bound on the capacity region of the contracted channel and also serves as an outer bound for the original channel. Finally, using the conditional entropy leakage lemma, we derive our outer bound on the capacity region of the contracted channel.

Joint MMSE Transceiver Designs for MIMO AF Relaying Systems With Direct Link

In this paper, we provide minimum mean-squared error-based source-relay-destination transceiver designs for multiple-input multiple-output amplify-and-forward relaying systems, where direct link between the source and the destination is non-negligible. In an earlier work, a local optimal technique was introduced which employs a projected gradient method and an interior point method. Since these methods may have quite high computational complexity, we investigate a new local optimal solution for the source-relay-destination transceiver which has low complexity. To this end, we first introduce the optimal closed-form solution for the relay transceiver for given source and destination filters. Then, for given relay and destination transceivers, the optimal source precoder design is derived, which requires only 1-D bisection search. Based on these solutions, we propose a joint optimization algorithm which iteratively finds a local optimal solution. Also, we introduce a simple non-iterative algorithm which computes filters in closed-forms with low complexity. Furthermore, since perfect channel knowledge may not be feasible in practical systems, a joint transceiver technique which is robust to channel uncertainties is provided. It is confirmed by simulation results that the proposed schemes outperform conventional techniques with significantly reduced complexity.

Hybrid MMSE Precoding for Millimeter-Wave (mmW) Multi-user Massive MIMO Systems

Millimeter-wave (mmWave) cellular systems is considered to be the key enabling technology for the future 5G wireless communication systems because of its high data rates, low latency, high system capacity, and huge available bandwidths. However, in order to meet the increasing demand, mmWave communications need to overcome certain challenges including high path loss and interference which can be reduced by applying large antenna arrays to achieve high beamforming gains. Although multi-user beamforming can improve spectral efficiencies, full digital beamforming strategies used in the conventional microwave systems increase the hardware cost and consumes high power for large number of antennas in mmW systems. In this paper, a multi-user hybrid precoding structure is proposed for mmWave massive-MIMO channels utilizing MMSE precoders at the BS with perfect channel knowledge. Simulations show that the sum-rate obtained by the proposed hybrid precoding scheme is nearly similar to the single-user rate and also performs better compared to other hybrid precoding approaches.

Supervised-Learning-Aided Communication Framework for MIMO Systems With Low-Resolution ADCs

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel communication framework that is inspired by supervised learning. The key idea of the proposed framework is to learn the non-linear input-output system, formed by the concatenation of a wireless channel and a quantization function used at the ADCs, for data detection. In this framework, a conventional channel estimation process is replaced by a system learning process, in which the conditional probability mass functions (PMFs) of the nonlinear system are empirically learned by sending the repetitions of all possible data signals as pilot signals. Then the subsequent data detection process is performed based on the empirical conditional PMFs obtained during the system learning. To reduce both the training overhead and the detection complexity, we also develop a supervised-learning-aided successive-interference-cancellation method. In this method, a data signal vector is divided into two subvectors with reduced dimensions. Then these two subvectors are successively detected based on the conditional PMFs that are learned using artificial noise signals and an estimated channel. For the case of one-bit ADCs, we derive an analytical expression for vector-error-rate of the proposed framework under perfect channel knowledge at the receiver. Simulations demonstrate the detection error reduction of the proposed framework compared to conventional detection techniques that are based on channel estimation.

Aligned Image Sets Under Channel Uncertainty: Settling Conjectures on the Collapse of Degrees of Freedom Under Finite Precision CSIT

A conjecture made by Lapidoth et al. at Allerton 2005 (also an open problem presented at ITA 2006) states that the degrees of freedom (DoF) of a two user broadcast channel, where the transmitter is equipped with two antennas and each user is equipped with one antenna, must collapse under finite precision channel state information at the transmitter (CSIT). That this conjecture, which predates interference alignment, has remained unresolved, is emblematic of a pervasive lack of understanding of the DoF of wireless networks-including interference and X networks-under channel uncertainty at the transmitter(s). In this paper, we prove that the conjecture is true in all non-degenerate settings (e.g., where the probability density function of unknown channel coefficients exists and is bounded). The DoF collapse even when perfect channel knowledge for one user is available to the transmitter. This also settles a related recent conjecture by Tandon et al. The key to our proof is a bound on the number of codewords that can cast the same image (within noise distortion) at the undesired receiver whose channel is subject to finite precision CSIT, while remaining resolvable at the desired receiver whose channel is precisely known by the transmitter. We are also able to generalize the result along two directions. First, if the peak of the probability density function is √ allowed to scale as O(( √P) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> ), representing the concentration of probability density (improving CSIT) due to, e.g., quantized feedback at rate (α/2) log(P), then the DoF is bounded above by 1+α, which is also achievable under quantized feedback. Second, we generalize the result to arbitrary number of antennas at the transmitter, arbitrary number of single-antenna users, and complex channels. The generalization directly implies a collapse of DoF to unity under non-degenerate channel uncertainty for the general K-user interference and M × N user X networks as well.

Scaling Laws for Noncoherent Energy-Based Communications in the SIMO MAC

We consider a one-shot communication setting in which several single antenna transmitters communicate with a receiver with a large number of antennas, i.e., the receiver decodes transmitted information at the end of every symbol time. Motivated by the optimal noncoherent detector in a Rayleigh fading channel, we consider a noncoherent energy-based communication scheme that does not require any knowledge of instantaneous channel state information at either the transmitter or the receiver; it uses only the statistics of the channel and noise. We show that, for general channel fading statistics, the performance of the considered one-shot multiuser noncoherent scheme is the same, in a scaling law sense, as that of the optimal coherent scheme exploiting perfect channel knowledge and coding across time. Furthermore, we present a numerical evaluation of the performance of this scheme in representative fading and noise statistics.

The Diversity-Multiplexing Tradeoff of Secret-Key Agreement Over Multiple Antenna Channels

We study the problem of secret-key agreement between two legitimate parties, Alice and Bob, in the presence of an eavesdropper Eve. There is a public channel with unlimited capacity that is available to the legitimate parties and is also observed by Eve. Our focus is on Rayleigh fading quasistatic channels. The legitimate receiver and the eavesdropper are assumed to have perfect channel knowledge of their channels. We study the system in the high-power regime. First, we define the secret-key diversity gain and the secret-key multiplexing gain. Second, we establish the secret-key diversity multiplexing tradeoff (DMT) under no channel state information (CSI) at the transmitter (CSI-T). The eavesdropper is shown to “steal” only transmit antennas. We show that, like the DMT without secrecy constraint, the secret-key DMT is the same either with or without full channel state information at the transmitter. This insensitivity of secret-key DMT toward CSI-T features a fundamental difference between secret-key agreement and the wiretap channel, in which secret DMT depends heavily on CSI-T. Finally, we present several secret-key DMT-achieving schemes in case of full CSI-T. We argue that secret DMT-achieving schemes are also key DMT-achieving. Moreover, we show formally that artificial noise (AN), likewise zero-forcing (ZF), is DMT-achieving. We also show that the public feedback channel improves the outage performance without having any effect on the DMT.

On the Ergodic Secret-Key Agreement Over Spatially Correlated Multiple-Antenna Channels With Public Discussion

We consider secret-key agreement with public discussion over multiple-input multiple-output (MIMO) Rayleigh fast-fading channels under correlated environment. We assume that transmit, legitimate receiver, and eavesdropper antennas are correlated. The legitimate receiver and the eavesdropper are assumed to have perfect channel knowledge while the transmitter has only knowledge of the correlation matrices. First, we derive the expression of the secret-key capacity under the considered setup. We prove that the optimal transmit strategy achieving the secret-key capacity consists in transmitting independent Gaussian signals along the eingenvectors of the transmit correlation matrix. The powers allocated to each channel mode are determined as the solution to a numerical optimization problem. A necessary and sufficient condition for beamforming (i.e., transmitting along the strongest channel mode) to be capacity-achieving is derived. Moreover, we analyze the impact of correlation matrices on the system performance. Finally, we study the system’s performance in the two extreme power regimes. In the high-power regime, we provide closed-form expressions of the gain/loss due to correlation. In the low signal-to-noise ratio (SNR) regime, we investigate the energy efficiency of the system by determining the minimum energy required for sharing a secret-key bit and the wideband slope while highlighting the impact of correlation matrices.

Robust Weighted Sum-Rate Maximization for the Multi-Stream MIMO Interference Channel With Sparse Equalization

In this paper, we study the problem of per-stream maximum sum-rate joint precoder and minimum mean-squared error equalizer design for the multi-input multi-output interference channel. We consider the general case of more than three users with more than one stream per user. We propose a generalized iterative algorithm which directly maximizes the sum-rate without assuming the signal-to-noise ratio to be infinite. To reduce complexity, which can become prohibitive for large network size, we examine the performance-complexity tradeoffs involved in a sparse equalizer design. Joint precoder and equalizer optimization requires alternation between the forward and reverse links and assumes perfect synchronization between the transmitters and receivers at each network node, resulting in extensive overhead and spectral efficiency loss. To overcome this serious drawback, we propose a new design approach based on weighted-sum-rate maximization assuming a virtual equalizer type at the transmitter to limit the optimization process to the transmitter side. In addition, we quantify the sum-rate loss due to mismatched equalizer types and demonstrate the robustness of our proposed sum-rate weighting strategy to such mismatches with perfect or imperfect channel knowledge. Finally, we derive asymptotic performance expressions and verify their accuracy numerically even for a moderate number of users.

Effects of Multiple Antennas and Imperfect Channel Knowledge on Secrecy Multiuser Diversity

In this letter, we investigate the effects of multiple antennas at a legitimate receiver and imperfect channel knowledge on secure user-scaling law in uplink multiuser wiretap networks. We generalize the conventional analysis of the secure user-scaling law for a single antenna system with perfect channel knowledge by considering multiple antennas at the receiver and the channel estimation errors.

Interference alignment and game-theoretic power allocation in MIMO Heterogeneous Sensor Networks communications

Interference alignment (IA) can eliminate the interference among wireless nodes. In a multi-input multi-output (MIMO) heterogeneous sensor network (HSN), nodes need to send out their sensed information, and secondary nodes can coexist with primary nodes without generating any interference by using the IA technology. However, few works have considered the sum utility of secondary nodes. In this paper, we not only eliminate the interference by IA, but also maximize the sum utility of secondary nodes by using a game-theoretic power allocation algorithm in Heterogeneous Sensor Networks. Without losing generality, consider there are one primary node and K secondary nodes in the network. Assuming perfect channel knowledge at the primary node, the rate of the primary node is maximized by a water-filling algorithm and the interference from secondary nodes to the primary node is aligned into the unused spatial dimension. Also, an improved maximum signal-to-interference-plus-noise algorithm using channel reciprocity is proposed for the secondary nodes to eliminate the interference between secondary nodes. In addition, the sum rate of the secondary nodes is maximized by a non-cooperative game-theoretic power allocation algorithm. Simulation results show the effectiveness of the proposed algorithms in terms of suppressed interference and maximized sum utility of secondary nodes.