Quality Of Channel State Information Research Articles

Rate-splitting multiple access for downlink communication systems: bridging, generalizing, and outperforming SDMA and NOMA

Space-division multiple access (SDMA) utilizes linear precoding to separate users in the spatial domain and relies on fully treating any residual multi-user interference as noise. Non-orthogonal multiple access (NOMA) uses linearly precoded superposition coding with successive interference cancellation (SIC) to superpose users in the power domain and relies on user grouping and ordering to enforce some users to fully decode and cancel interference created by other users.In this paper, we argue that to efficiently cope with the high throughput, heterogeneity of quality of service (QoS), and massive connectivity requirements of future multi-antenna wireless networks, multiple access design needs to depart from those two extreme interference management strategies, namely fully treat interference as noise (as in SDMA) and fully decode interference (as in NOMA).Considering a multiple-input single-output broadcast channel, we develop a novel multiple access framework, called rate-splitting multiple access (RSMA). RSMA is a more general and more powerful multiple access for downlink multi-antenna systems that contains SDMA and NOMA as special cases. RSMA relies on linearly precoded rate-splitting with SIC to decode part of the interference and treat the remaining part of the interference as noise. This capability of RSMA to partially decode interference and partially treat interference as noise enables to softly bridge the two extremes of fully decoding interference and treating interference as noise and provides room for rate and QoS enhancements and complexity reduction.The three multiple access schemes are compared, and extensive numerical results show that RSMA provides a smooth transition between SDMA and NOMA and outperforms them both in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths, and qualities of channel state information at the transmitter). Moreover, RSMA provides rate and QoS enhancements over NOMA at a lower computational complexity for the transmit scheduler and the receivers (number of SIC layers).

Performance Analysis and Scaling Law of MRC/MRT Relaying With CSI Error in Multi-Pair Massive MIMO Systems

This paper provides a comprehensive scaling law and performance analysis for multi-user massive multiple-input-multiple-output (MIMO) relay networks, where the relay is equipped with a massive antenna array and uses maximal-ratio combining/maximal-ratio transmission (MRC/MRT) for low-complexity processing. Imperfect channel state information (CSI) is considered for both source-relay and relay-destination channels. First, a sum-rate lower bound is derived, which manifests the effect of system parameters, including the numbers of relay antennas and users, the CSI quality, and the transmit powers of the sources and the relay. Via a general scaling model on the parameters with respect to the relay antenna number, the asymptotic scaling law of the signal-to-interference-plus-noise-ratio (SINR) is obtained, which shows quantitatively the tradeoff of the network parameters. In addition, a sufficient condition on the parameter scalings for the SINR to be asymptotically deterministic is given, which covers existing results on such analysis as special cases. Then, the scenario where the SINR increases linearly with the relay antenna number is studied. The sufficient and necessary condition on the parameter scaling for this scenario is proved. It is shown that in this case, the interference power is not asymptotically deterministic, and then, the average bit error rate is analyzed.

Understanding the Impacts of Limited Channel State Information on Massive MIMO Cellular Network Optimization

To support the multi-gigabit per second data rates of 5G wireless networks, there have been significant efforts on the research and development of massive MIMO (M-MIMO) technologies at the physical layer. So far, however, the understanding of how M-MIMO could affect the performance of network control, and optimization algorithms remain rather limited. In this paper, we focus on analyzing the performance of the queue-length-based joint congestion control and scheduling framework over M-MIMO cellular networks with limited channel state information (CSI). Our contributions in this paper are twofold. First, we characterize the scaling performance of the queue-lengths and show that there exists a phase transitioning phenomenon in the steady-state queue-length deviation with respect to the CSI quality (reflected in the number of bits $B$ that represent CSI). Next, we characterize the congestion control rate scaling performance and show that there also exists a phase transitioning phenomenon in steady-state congestion control rate deviation with respect to the CSI quality. Collectively, the findings in this paper advance our understanding of the tradeoffs between delay, throughput, and the accuracy/complexity of CSI acquisition in M-MIMO cellular network systems.

Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels

The practical deployment of massive multiple-input multiple-output (MIMO) in future fifth generation (5G) wireless communication systems is challenging due to its high hardware cost and power consumption. One promising solution to address this challenge is to adopt the low-resolution analog-to-digital converter (ADC) architecture. However, the practical implementation of such architecture is challenging due to the required complex signal processing to compensate the coarse quantization caused by low-resolution ADCs. Therefore, few high-resolution ADCs are reserved in the recently proposed mixed-ADC architecture to enable low-complexity transceiver algorithms. In contrast to previous works over Rayleigh fading channels, we investigate the performance of mixed-ADC massive MIMO systems over the Rician fading channel, which is more general for the 5G scenarios like Internet of Things (IoT). Specially, novel closed-form approximate expressions for the uplink achievable rate are derived for both cases of perfect and imperfect channel state information (CSI). With the increasing Rician $K$-factor, the derived results show that the achievable rate will converge to a fixed value. We also obtain the power-scaling law that the transmit power of each user can be scaled down proportionally to the inverse of the number of base station (BS) antennas for both perfect and imperfect CSI. Moreover, we reveal the trade-off between the achievable rate and energy efficiency with respect to key system parameters including the quantization bits, number of BS antennas, Rician $K$-factor, user transmit power, and CSI quality. Finally, numerical results are provided to show that the mixed-ADC architecture can achieve a better energy-rate trade-off compared with the ideal infinite-resolution and low-resolution ADC architectures.

Performance Limits of Online Energy Harvesting Communications With Noisy Channel State Information at the Transmitter

In energy harvesting communications, the transceivers have to adjust the data transmission to the energy scavenged during the course of communication. The performance of the transmission depends on the channel conditions, which vary randomly due to mobility and environmental changes. In this paper, we consider the problem of power allocation, considering the energy arrivals over time and the quality of channel state information (CSI) measured at the transmitter, in order to maximize the throughput. Differently from previous work, we focus on energy harvesting communications where the CSI at the transmitter is not perfect and may include estimation errors. In this paper, we introduce a Markov process that models the energy arrival process. Indeed, we solve the throughput maximization problem with respect to energy harvesting constraints. We show that the optimal online power policy can be found using dynamic programming. Furthermore, we study the asymptotic behavior of the communication system at low and high average recharge rate regime. Selected numerical results are provided to support our analysis.

Design and Analysis of Physical Layer Security Based on Ill-Posed Theory for Optical OFDM-Based VLC System Over Real-Valued Visible Light Channel

Due to the broadcast property of the visible light beam, visible light communication (VLC) systems may suffer from malicious attack or eavesdropping in public area. In this paper, we design and analyze the physical layer security mechanism with the aid of ill-posed theory and present a channel determined subcarrier shifting (CDSS) scheme with pre-equalization. The CDSS scheme exploits the real-valued and location-sensitive channel state information (CSI) of VLC channel and performs sorting operations on the CSI, which formulates a shifting matrix with the aid of chaotic sequences, to dynamically change the available subcarrier for the optical orthogonal frequency division multiplex (OOFDM) aided VLC system. Meanwhile, we combine the CDSS scheme with the preequalization module to further improve the bit error rate (BER) performance for legitimate users. Moreover, the analytical physical layer security model for VLC system is presented using ill-posed theory, which removes the requirements of known CSI and worse channel quality of malicious users when evaluating secrecy capacity. Theoretical and simulation results are provided and compared to prove the benefits achieved by the proposed CDSS scheme, which can enhance the security with no loss of BER, while the secrecy capacity performances are also evaluated to verify the effectiveness of our design.

Coverage probability of cellular networks using interference alignment under imperfect CSI

Interference alignment (IA) is well understood to approach the capacity of interference channels, and believed to be crucial in cellular networks in which the ability to control and exploit interference is key. However, the achievable performance of IA in cellular networks depends on the quality of channel state information (CSI) and how effective IA is in practical settings is not known. This paper studies the use of IA to mitigate inter-cell interference of cellular networks under imperfect CSI conditions. Our analysis is based on stochastic geometry where the structure of the base station (BS) locations is considered by a Poisson point process (PPP). Our main contribution is the coverage probability of the network and simulation results confirm the accuracy.

Hybrid precoding with compressive sensing based limited feedback in massive MIMO systems

ABSTRACTMassive multiple‐input multiple‐output (MIMO) systems are an enabling technology for high capacity 5G cellular networks. However, the high hardware complexity and power consumption make it difficult to apply conventional MIMO precoding schemes in massive MIMO. Additionally, thanks to the large channel dimension, applying traditional limited feedback schemes for obtaining channel state information at the transmitter is a challenge. In this paper, we develop a low‐complexity hybrid precoding scheme for multiuser massive MIMO system in mmWave communication. We design the analog beamformer as the conjugate transpose of the downlink channel from base station to the users. Then zero‐forcing baseband processing is applied over the effective channel. We get the optimal power allocation policy and compare its performance with average power allocation based on channel state information at transmitter. The channel state information at transmitter is obtained via a compressive sensing based limited feedback strategy. The effectiveness of hybrid precoding relies on the feedback quality of channel state information, which relies on the compression ratio. We quantify the impact of compression ratio on precoder performance and derive an upper bound on the sum rate. Simulation results illustrate the performance of our proposed scheme and the effect of compressive sensing based limited feedback.

Achievable Downlink Rates of MRC and ZF Precoders in Massive MIMO With Uplink and Downlink Pilot Contamination

Massive multi-input multi-output (MIMO) systems require channel state information (CSI) at base stations. In multicell environments, adjacent cells using the same spectrum cause a pilot contamination problem and degrade CSI quality. Under such practical environments, it is important to assess achievable rate of the system for a proper system design. There exist achievable rate bounds of uplink (UL) systems in the literature, but general closed-form rate bounds for downlink (DL) systems are not available yet. This paper studies a practical massive MIMO DL system with or without pilot-aided coherent detection under the scenario of pilot contamination, and derives closed-form approximate achievable DL rate expressions for both maximum ratio combining (MRC) and zero forcing (ZF) precoders. For the case with DL pilot, a corresponding DL effective channel estimator is also developed. Numerical simulation results corroborate accuracy of the closed-form rate expressions and they also show performance characteristics of pilot-contaminated massive MIMO DL systems with MRC and ZF precoders.

Optimal Binary Power Control for Underlay CR With Different Interference Constraints and Impact of Channel Estimation Errors

Adapting the power of secondary users (SUs) while adhering to constraints on the interference caused to primary receivers (PRxs) is a critical issue in underlay cognitive radio (CR). This adaptation is driven by the interference and transmit power constraints imposed on the secondary transmitter (STx). Its performance also depends on the quality of channel state information (CSI) available at the STx of the links from the STx to the secondary receiver and to the PRxs. For a system in which an STx is subject to an average interference constraint or an interference outage probability constraint at each of the PRxs, we derive novel symbol error probability (SEP)-optimal, practically motivated binary transmit power control policies. As a reference, we also present the corresponding SEP-optimal continuous transmit power control policies for one PRx. We then analyze the robustness of the optimal policies when the STx knows noisy channel estimates of the links between the SU and the PRxs. Altogether, our work develops a holistic understanding of the critical role played by different transmit and interference constraints in driving power control in underlay CR and the impact of CSI on its performance.

Application of compressive sensing to limited feedback strategy in large‐scale multiple‐input single‐output cellular networks

A novel limited feedback strategy based on a compressive sensing technique is proposed for the downlink large-scale multiple-input single-output (MISO) system. The effectiveness of transmit beamformers relies on the feedback quality of channel state information (CSI). The instantaneous CSI is compressed and sent back to the base station (BS) by receivers. An efficient recovery algorithm is applied at the BS in the presence of measurement noise and feedback link noise. The proposed feedback strategy can provide the BS with much more precise CSI as compared with some conventional vector quantisation schemes. The proposed scheme also has lower-computational complexity. Furthermore, the authors derive a tight lower bound on the user sum rate, assuming that zero-forcing beamforming is applied at the BS, and that active users are semi-orthogonal to each other. Numerical results show that the proposed compressive sensing-based limited feedback scheme yields better performance gain than the conventional channel quantisation approaches.

Egoistic vs. altruistic beamforming in multi-cell systems with feedback and back-haul delays

Base station cooperation is an attractive technique to increase the spectral efficiency of multi-cell systems. One of the challenges in multi-cell systems is obtaining accurate channel state information (CSI) at the base station. One source of CSI inaccuracy is the delay incurred in obtaining CSI and exchanging it among base stations. The presence of delay is inevitable when CSI is exchanged over the back-haul. In addition, CSI is commonly obtained using limited feedback techniques which further contribute to its inaccuracy. In this paper, we re-visit the comparison between competitive (egoistic) and cooperative (altruistic) beamforming strategies in the presence of imperfect CSI. The impact of CSI inaccuracy due to delay and finite codebook size on the achieved sum rate is analyzed. Closed-form expressions for a lower bound and a first-order approximation of the average sum rates achieved by these beamforming strategies are derived. Using the closed-form expressions, a mode switching criterion is proposed to switch between competitive and cooperative beamforming based on the signal-to-noise ratio (SNR), delay, Doppler frequency and codebook size. It is shown that competitive beamforming is preferred in the limit of low SNR irrespective of the quality of CSI, while cooperative beamforming is preferred only at high SNR and low Doppler frequencies, confirming that base station cooperation is not always advantageous.

Performance analysis of MIMO beamforming with imperfect feedback

SUMMARYThe quality of channel state information at the transmitter (CSIT) is critical to MIMO beamforming systems. However, in practical wireless systems, CSIT suffers from imperfections originating from quantization effects, feedback error and feedback delay. In this paper, we study the impact of feedback error and delay on the symbol error rate of MIMO beamforming systems with finite rate feedback. The feedback channel is modeled as a uniform symmetric channel. We derive an symbol error rate upper bound that is tight for a good beamformer. We also quantify the diversity gain and array gain loss due to the feedback error and delay. The codebook design method that is applicable to the beamforming systems with error or delay feedback is discussed. Both analytical and simulation results show that feedback error and delay will make the system behave badly at high signal‐to‐noise ratios. Copyright © 2012 John Wiley & Sons, Ltd.

Outage Probability and Outage-Based Robust Beamforming for MIMO Interference Channels with Imperfect Channel State Information

In this paper, the outage probability and outage-based beam design for multiple-input multiple-output (MIMO) interference channels are considered. First, closed-form expressions for the outage probability in MIMO interference channels are derived under the assumption of Gaussian-distributed channel state information (CSI) error, and the asymptotic behavior of the outage probability as a function of several system parameters is examined by using the Chernoff bound. It is shown that the outage probability decreases exponentially with respect to the quality of CSI measured by the inverse of the mean square error of CSI. Second, based on the derived outage probability expressions, an iterative beam design algorithm for maximizing the sum outage rate is proposed. Numerical results show that the proposed beam design algorithm yields better sum outage rate performance than conventional algorithms such as interference alignment developed under the assumption of perfect CSI.

MIMO Capacity Analysis Using Adaptive Semi Blind Channel Estimation with Modified Precoder and Decoder for Time Varying Spatial Channel

Multiple Input Multiple Output(MIMO) has been in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel with modified precoder and decoder has been considered when the channel state information (CSI) is considered partial. Due to delay in acquiring transmitted information at the receiver end, the time selective fading wireless channel often induces incomplete or partial CSI. The dynamic CSI model has also been implemented consisting channel mean and covariance which leads to extracting of channel estimates and error covariance which then further applied with the modified precoder and decoder utilizing both the parameters indicating the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partially or fully blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI which has been manipulated according to the precoder and decoder conditions. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new approach which maximizes the capacity of spatial channel model with modified precoder and decoder has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach, where by utilizing the detected symbols from the previous iteration, multiuser/MIMO channel estimation and symbol detection is improved.

Capacity Analysis of MIMO Spatial Channel Model using Novel Adaptive Semi Blind Estimation Scheme

Multiple Input Multiple Output (MIMO) has been remained in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel has been considered when the channel state information (CSI) is considered imperfect or partial. The dynamic CSI model has also been tried consisting of channel mean and covariance which leads to extracting of the channel estimates and error covariance. Both parameters indicated the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partial or full blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new estimation approach which maximizes the capacity of spatial channel model has been tried. The interference interactively reduced by employing the iterative channel estimation and data detection approach.

Tradeoff Analysis of Delay-Power-CSIT Quality of Dynamic Backpressure Algorithm for Energy Efficient OFDM Systems

In this paper, we analyze the fundamental power-delay tradeoff in point-to-point OFDM systems under imperfect channel state information quality and non-ideal circuit power. We consider the dynamic back- pressure (DBP) algorithm, where the transmitter determines the rate and power control actions based on the instantaneous channel state information (CSIT) and the queue state information (QSI). We exploit a general fluid queue dynamics using a continuous time dynamic equation. Using the sample-path approach and renewal theory, we decompose the average delay in terms of multiple unfinished works along a sample path, and derive an upper bound on the average delay under the DBP power control, which is asymptotically accurate at small delay regime. We show that despite imperfect CSIT quality and non-ideal circuit power, the average power (P) of the DBP policy scales with delay (D) as P = O(Dexp(1/D)) at small delay regime. While the impacts of CSIT quality and circuit power appears as the coefficients of the scaling law, they may be significant in some operating regimes.

Opportunistic Multiple Relay Selection With Outdated Channel State Information

The quality of channel state information (CSI) plays a significant role in the performance of relay selection (RS) schemes in cooperative communications. Particularly in a highly mobile environment when there exists delay between the selection and data transmission instants, the quality of CSI often degrades, given to what is known as outdated CSI phenomena. In this paper, we first outline several single RS schemes that achieve full diversity when using perfect CSI and prove that the diversity order of these schemes reduces to unity if outdated CSI exists. To deal with the performance deterioration due to the outdated CSI phenomena, we propose a new multiple RS scheme for both amplify-and-forward (AaF) and decode-and-forward (DaF) relay systems, namely, the plus normalized threshold opportunistic RS . In particular, we first opportunistically select best relays from all candidate relays with respect to end-to-end signal-to-noise ratios (SNRs) for AaF and relay-to-destination SNRs for DaF. Then, the ratios of the SNRs on the rest of the candidate relays to that of the th highest SNR are tested against a normalized threshold , and only those relays passing this test are selected in addition to the best relays. The performance of the scheme is theoretically analyzed in terms of outage probability, and its asymptotic diversity order is also examined. In addition, we outline a distributed selection protocol that makes no assumption of the global CSI at each relay. Numerical results confirm the analysis and show the advantage of the proposed scheme against existing counterparts in highly dynamic networks.

Degrees-of-Freedom Based on Interference Alignment with Imperfect Channel Knowledge

The impact and benefits of channel state information (CSI) are analyzed in terms of degrees-of-freedom (DoFs) in a K-user interference network operating over time-selective channels, where the error variance of CSI estimation is assumed to scale with an exponent of the received signal-to-noise ratio (SNR). The original interference alignment (IA) scheme is used with a slight modification in the network. Then, it is shown that the DoFs promised by the original IA can be fully achieved under the condition that the CSI quality order, represented as a function of the error variance and the SNR, is greater than or equal to 1. Our result is extended to the case where the number of communication pairs, K, scales with the SNR, i.e., infinite K scenario, by introducing the user scaling order. As a result, this letter provides vital information to the system designer in terms of allocating training resources for channel estimation in practical cellular environments using IA.

Robust Power Allocation Algorithms for Wireless Relay Networks

Resource allocation promises significant benefits in wireless networks. In order to fully reap these benefits, it is important to design efficient resource allocation algorithms. Here, we develop relay power allocation (RPA) algorithms for coherent and noncoherent amplify-and-forward (AF) relay networks. The goal is to maximize the output signal-to-noise ratio under individual as well as aggregate relay power constraints. We show that these RPA problems, in the presence of perfect global channel state information (CSI), can be formulated as quasiconvex optimization problems. In such settings, the optimal solutions can be efficiently obtained via a sequence of convex feasibility problems, in the form of second-order cone programs. The benefits of our RPA algorithms, however, depend on the quality of the global CSI, which is rarely perfect in practice. To address this issue, we introduce the robust optimization methodology that accounts for uncertainties in the global CSI. We show that the robust counterparts of our convex feasibility problems with ellipsoidal uncertainty sets are semi-definite programs. Our results reveal that ignoring uncertainties associated with global CSI often leads to poor performance, highlighting the importance of robust algorithm designs in practical wireless networks.