Channel State Information At Transmitter Research Articles

Age of Error Information and Throughput Tradeoff for IoT Status Update Systems With Truncated and Layer-Coded HARQ

A novel status update system is designed by combining adaptive modulation and coding, and truncated and layer-coded hybrid automatic repeat request (T-L-HARQ). With the channel state information (CSI) at transmitter (CSIT), which is obtained from the receiver by using pilot-assisted estimation, the source adaptively selects its transmission rate. Due to the delay, the CSI at receiver (CSIR) is the outdated version of the CSIT, and the decoding error is inevitable. Firstly, since the backtrack decoding used in the T-L-HARQ, the classical age of information is insufficient to quantify the status update freshness, we propose the age of error information (AoEI), which is defined as the elapsed time since the generation of the lastly successfully backtrack-decoded status update. Secondly, we characterize the interdeparture time duration of two consecutively successfully feedforward transmissions and the backtrack decoding depth of a successful T-L-HARQ transmission cycle. As a result, the closed-form expression of average AoEI is derived. The simulations show that a tradeoff design between throughput and AoEI is essential by controlling backtrack decoding depth.

Joint Massive MIMO CSI Estimation and Feedback via Randomized Low-Rank Approximation

The acquisitionof channel state information at transmitter (CSIT) is crucial to attain the potential benefits of the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. Traditionally, CSI estimation at receiver and feedback to transmitter are separately designed, failing to maximize the estimation accuracy while minimizing the complexity/overhead. In this work, we propose a novel computation and communication efficient CSIT acquisition scheme, by jointly designing downlink CSI estimation and uplink feedback, which is inspired by randomized matrix approximation techniques. In the process of CSI estimation, we represent a large channel matrix with three sub-matrices by exploiting its inherent low-rank characteristic, and then estimate these small matrices separately. Our new estimator significantly reduces the computation complexity in massive MIMO CSI acquisition, whilst attaining the high accuracy. The convergence as well as the error bound of our CSI estimator is derived theoretically. In the following CSI feedback process, only partial elements of sub-matrices are reported to transmitter without further compression, and finally the full CSI is recovered accurately. This novel CSI feedback scheme, relying on the estimated CSI with special structure, greatly reduces both the communication overhead. Numerical simulations are provided to valid our joint CSI estimation and feedback scheme, which would have great potential in massive MIMO communications.

Enhanced precoder for secondary user of MIMO cognitive radio in the presence of CSIT uncertainties in the desired and interference links

This paper advances the design of transmit precoder for a secondary user (SU) coexisting with a primary user (PU) in multiple-input multiple-output (MIMO) cognitive radio, where the channel state information at transmitter (CSIT) of SU is inaccurate in both the desired link (DL) to its receiver and the interference link (IL) to the PU. The design seeks to maximize performance in the worst scenario, by optimizing the capacity of SU while maintaining a given quality of service (QoS) of PU at the most unfavorable uncertainty errors of the two links. The proposed technique improves the previous design by solving for the precoder in a more accurate and efficient manner, and by using the variable metric projected subgradient method. The most unfavorable realization in the DL is obtained by the alternate-iterate optimization method and the worst uncertainty realization in the IL is derived from the projected subgradient method. The convergence properties and computational complexity of the proposed algorithm are analyzed. Numerical results confirm the proposed precoder has better performance than the previous solution while having lower practical time complexity, especially when the PU has higher tolerance level of SU interference.

Cooperative Multiple-Access Channels With Distributed State Information

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e., where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e., optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading.

Polarized modulation and receivers for wireless communication

Achieving increase in spectral efficiency has always been a major aspect in communication systems. Our work emphasises the application of dual polarized modulation in a wireless environment. Our main goal is to increase the throughput of the system. We aim to increase the spectral efficiency and throughput without an increment of radiated energy without any Channel State Information at Transmitter (CSIT) and feedback at the transmitter The proposed dual Polarized Modulation (DPMOD) scheme exploits the polarization diversity reducing the required SNR (Eb/N0) and adding an extra bit and achieves the same Bit Error Rate (BER) carrying extra information.

On the Performance of Cluster-Based MIMO-NOMA in Multi-Cell Dense Networks

This paper develops an analytical framework for exploring the benefits of applying cluster-based multi-antenna non-orthogonal multiple access (NOMA) in dense wireless networks. Using the tools of stochastic geometry, a new explicit expression and a tight approximation for per-cluster average data rates are derived in terms of relevant system parameters. Moreover, simulation results are provided to validate the accuracy of our analytical results and draw some essential system design insights. Based on the tractable expressions, we further consider the analysis and optimization of area spectral efficiency (ASE). It is analytically demonstrated that: 1) In terms of the per-cluster average data rate, there exists a wide range for the power allocation coefficient within a cluster where NOMA outperforms orthogonal multiple access (OMA); 2) The per-cluster average data rate of NOMA benefits more from the additional deployment of antennas at base stations (BSs) than that of OMA when the accuracy of channel state information at transmitter (CSIT) is fixed; 3) Regarding to ASE, there exists an optimal combination of the number of clusters and power allocation coefficient to maximize ASE. The performance gain of NOMA relative to OMA becomes marginal when the requirement of user fairness is stringent; 4) As for a special case that the power allocation coefficient is fixed, the analytical results indicate that ASE scales linearly with the number of antennas when the number of clusters is set optimally.

Secure Retrospective Interference Alignment

In this paper, the K-user interference channel with secrecy constraints is considered with delayed channel state information at transmitters (CSIT). We propose a novel secure retrospective interference alignment scheme in which the transmitters carefully mix information symbols with artificial noises to ensure confidentiality. Achieving positive secure degrees of freedom (SDoF) is challenging due to the delayed nature of CSIT, and the distributed nature of the transmitters. Our scheme works over two phases: Phase one, in which each transmitter sends information symbols mixed with artificial noises, and repeats such transmission over multiple rounds. In the next phase, each transmitter uses the delayed CSIT of the previous phase and sends a function of the net interference and artificial noises (generated in previous phase), which is simultaneously useful for all receivers. These phases are designed to ensure the decodability of the desired messages while satisfying the secrecy constraints. We present our achievable scheme for three models, namely: (1) K-user interference channel with confidential messages (IC-CM), and we show that SDoF is achievable; (2) K-user interference channel with an external eavesdropper (IC-EE); and (3) K-user IC with confidential messages and an external eavesdropper (IC-CM-EE). We show that for the K-user IC-EE, SDoF is achievable, and for the K-user IC-CM-EE, is achievable. To the best of our knowledge, this is the first result on the K-user interference channel with secrecy constrained models and delayed CSIT that achieves an SDoF which scales with , square-root of number of users.

Ring Compute-and-Forward Over Block-Fading Channels

The Compute-and-Forward protocol in quasi-static channels normally employs lattice codes based on the rational integers $\mathbb{Z}$, Gaussian integers $\mathbb{Z}\left[i\right]$ or Eisenstein integers $\mathbb{Z}\left[\omega\right]$, while its extension to more general channels often assumes channel state information at transmitters (CSIT). In this paper, we propose a novel scheme for Compute-and-Forward in block-fading channels without CSIT, which is referred to as Ring Compute-and-Forward because the fading coefficients are quantized to the canonical embedding of a ring of algebraic integers. Thanks to the multiplicative closure of the algebraic lattices employed, a relay is able to decode an algebraic-integer linear combination of lattice codewords. We analyze its achievable computation rates and show it outperforms conventional Compute-and-Forward based on $\mathbb{Z}$-lattices. By investigating the effect of Diophantine approximation by algebraic conjugates, we prove that the degrees-of-freedom (DoF) of the optimized computation rate is ${n}/{L}$, where $n$ is the number of blocks and $L$ is the number of users.

Joint User Selection, Power Allocation, and Precoding Design With Imperfect CSIT for Multi-Cell MU-MIMO Downlink Systems

In this paper, a new optimization framework is presented for the joint design of user selection, power allocation, and precoding in multi-cell multi-user multiple-input multiple-output (MU-MIMO) systems when imperfect channel state information at transmitter (CSIT) is available. By representing the joint optimization variables in a higher-dimensional space, the weighted sum-spectral efficiency maximization is formulated as the maximization of the product of Rayleigh quotients. Although this is still a non-convex problem, a computationally efficient algorithm, referred to as generalized power iteration precoding (GPIP), is proposed. The algorithm converges to a stationary point (local maximum) of the objective function and therefore it guarantees the first-order optimality of the solution. By adjusting the weights in the weighted sum-spectral efficiency, the GPIP yields a joint solution for user selection, power allocation, and downlink precoding. The GPIP can be extended to the multi-cell scenario where cooperative base stations perform joint user-cell selection and design their precodes by taking into account the inter-cell interference by sharing global imperfect CSIT. System-level simulations show the gains of the proposed approach with respect to conventional user selection and linear downlink precoding.

Secure degrees of freedom of two‐user X‐channel with synergistic alternating channel state information at transmitters

In this study, a two-user single-input single-output X-channel with confidential messages is addressed. In this model, the authors assume that the transmitters have access to synergistic alternating channel state information. During different time slots, the channel state information at transmitters (CSIT) alternate between three states including perfect CSIT, delayed CSIT, and no CSIT. By using the eminent synergistic benefits of the CSIT pattern, some schemes capable of attaining the maximum achievable secure degrees of freedom (SDoF) are presented. Additionally, in devising the schemes, the minimal CSIT patterns required to achieve optimal SDoF are introduced. It is shown that for CSIT patterns which are weaker than minimal ones, using a half-duplex relay can assist the network in obtaining the optimal SDoF. Indeed, the relay alleviates the effects of the lack of knowledge at transmitters on achievable SDoF.

Topological Interference Management for MIMO Interference Broadcast Channels

In this paper, we investigate the topological interference management (TIM) for MIMO interference broadcast channels (IBC) under the fixed topology, where only topology information is needed to cancel the inter-cell interference (ICI). For the single user case, where the MIMO IBC network is reduced to the MIMO interference channel (IC) networks, we propose one novel TIM scheme by exploiting the solution to the corresponding SISO IC TIM based on topology information. And also, we present one theorem to characterize the achievable degrees of freedom (DoFs). For the multiple user case, assuming that each BS not only knows topology information but also knows the channel state information at transmitters (CSIT) within its own cell, we propose two novel TIM schemes, i.e., the virtual user compression-based scheme and the virtual base station extension-based scheme. For each scheme, we design transceivers to cancel both the ICI only based on the topology information by exploiting the solution to the corresponding SISO IC TIM and the multiple user interference (MUI) within its own cell based on the CSIT. Furthermore, we present one theorem to characterize the achievable DoF, respectively. Moreover, we prove that the DoF achieved by the virtual base station extension-based scheme is higher than or equal to that achieved by the virtual user compression-based scheme. Hence, the achievable DoF of MIMO IBC TIM is lower bounded by the optimal DoF attainable for the base station extension scheme. Furthermore, the numerical results show that our schemes can achieve significant performance gain against the traditional interference avoidance scheme.

Energy-Efficient Power Allocation for NOMA With Imperfect CSI

An optimum user power allocation solution to maximize the energy efficiency (EE) in a non-orthogonal multiple access (NOMA) system with imperfect channel state information at transmitter (CSIT) and user quality of service constraint is proposed. The EE objective function is a non-convex nonlinear function that leads to a fractional programming problem. To deal with this problem, an auxiliary variable is defined, which converts the problem into a convex one. A closed form optimal solution for the power allocated to each user is found using the Karush–Kuhn–Tucker conditions. Furthermore, difference of two concave functions—DC—programming and Dinkelbach structure are used to find close-to-optimal total power value (sum of powers being allocated to all users) for the multiuser NOMA system. Simulation results show that in terms of EE, for users with low rate requirement and with CSIT error, NOMA has a better performance compared with conventional orthogonal multiple access (OMA).

Approximate Capacity of Fast Fading Interference Channels With no Instantaneous CSIT

We develop a characterization of fading models, which assigns a number called logarithmic Jensen’s gap to a given fading model. We show that as a consequence of a finite logarithmic Jensen’s gap, an approximate capacity region can be obtained for fast fading interference channels (FF-ICs) for several scenarios. We illustrate three instances where a constant capacity gap can be obtained as a function of the logarithmic Jensen’s gap. First, for an FF-IC with neither feedback nor instantaneous channel state information at transmitter (CSIT), if the fading distribution has finite logarithmic Jensen’s gap, we show that a rate-splitting scheme based on the average interference-to-noise ratio can achieve its approximate capacity. Second, we show that a similar scheme can achieve the approximate capacity of FF-IC with feedback and delayed CSIT, if the fading distribution has finite logarithmic Jensen’s gap. Third, when this condition holds, we show that point-to-point codes can achieve approximate capacity for a class of FF-ICs with feedback. We prove that the logarithmic Jensen’s gap is finite for common fading models, including Rayleigh and Nakagami fading, thereby obtaining the approximate capacity region of FF-IC with these fading models.

Sparsity-aided codebook for limited feedback in FDD massive MIMO system

Channel state information at transmitter (CSIT) is vital for massive multiple-input-multiple-output (MIMO) system to achieve unprecedented improvement in capacity and spectral efficiency. Unfortunately, CSIT is acquired via channel feedback in frequency-division duplexing (FDD) system, which suffers from large volume of feedback overhead and size of codebook, since they increase with the number of antennas at transmitter. In this paper, by exploiting the channel sparsity in virtual angular domain, a sparsity-aided codebook is proposed for FDD massive MIMO system to alleviate the channel feedback overhead and codebook size. Specifically, a sparsity aided limited channel feedback model is proposed, where the high dimensional channel is decomposed to the limited linear combination with channel coefficients in virtual angular domain. Then, the high dimensional channel can be quantized by the proposed codebook in a more precise way with significantly small codebook size and fed back in low overhead within virtual coherence time. Furthermore, the upper bound of inter-user interference and the per user rate loss incurred by the proposed sparsity-aided codebook are theoretically analyzed, in which the required quantization bits only increase linearly with the channel sparsity level rather than number of BS antennas when the rate loss is limited to a constant level.

The Generalized Degrees of Freedom Region of the MIMO Z-Interference Channel With Delayed CSIT

The generalized degrees of freedom (GDoF) region of the multiple-input multiple-output (MIMO) Gaussian Z-interference channel with an arbitrary number of antennas at each node is established under the assumption of delayed channel state information at transmitters (CSIT). The GDoF region is parameterized by $\alpha$ , which links the interference-to-noise ratio (INR) to the signal-to-noise ratio (SNR) via $\mathrm {INR}=\mathrm {SNR}^{\alpha }$ . A new outer bound for the GDoF region is established by maximizing a bound on the weighted sum-rate of the two users, which in turn is obtained by using a combination of genie-aided side-information and an extremal inequality. The maximum weighted sum-rate in the high SNR regime is shown to occur when the transmission covariance matrix of the interfering transmitter has full rank. An achievability scheme based on block-Markov encoding and backward decoding is developed which uses interference quantization and digital multicasting to take advantage of the channel statistics of the cross-link, and the scheme is separately shown to be GDoF-optimal in both the weak ( $\alpha \leq 1$ ) and strong ( ${\alpha >1}$ ) interference regimes. This is the first complete characterization of the GDoF region of any interference network with delayed CSIT, as well as the first such GDoF characterization of a MIMO network with delayed CSIT and arbitrary number of antennas at each node. For all antenna tuples, the GDoF region is shown to be equal to or larger than the degrees of freedom (DoF) region over the entire range of $\alpha$ , which leads to a V-shaped maximum sum-GDoF as a function of $\alpha$ , with the minimum occurring at $\alpha =1$ . The delayed CSIT GDoF region and the sum-DoF are compared with their counterparts under perfect CSIT, thereby characterizing all antenna tuples and ranges of $\alpha$ for which delayed CSIT is sufficient to achieve the perfect CSIT GDoF region (or sum-DoF). It is also shown that treating interference as noise is not, in general, GDoF-optimal for the MIMO Z-IC, even in the weak interference regime.

Resource Allocation Optimization for Delay-Sensitive Traffic in Fronthaul Constrained Cloud Radio Access Networks

The cloud radio access network (C-RAN) provides high spectral and energy efficiency performances, low expenditures and intelligent centralized system structures to operators, which has attracted intense interests in both academia and industry. In this paper, a hybrid coordinated multi-point transmission (H-CoMP) scheme is designed for the downlink transmission in C-RANs, which fulfills the flexible tradeoff between cooperation gain and fronthaul consumption. The queue-aware power and rate allocation with constraints of average fronthaul consumption for the delay-sensitive traffic are formulated as an infinite horizon constrained partially observed Markov decision process (POMDP), which takes both the urgent queue state information (QSI) and the imperfect channel state information at transmitters (CSIT) into account. To deal with the curse of dimensionality involved with the equivalent Bellman equation, the linear approximation of post-decision value functions is utilized. A stochastic gradient algorithm is presented to allocate the queue-aware power and transmission rate with H-CoMP, which is robust against unpredicted traffic arrivals and uncertainties caused by the imperfect CSIT. Furthermore, to substantially reduce the computing complexity, an online learning algorithm is proposed to estimate the per-queue post-decision value functions and update the Lagrange multipliers. The simulation results demonstrate performance gains of the proposed stochastic gradient algorithms, and confirm the asymptotical convergence of the proposed online learning algorithm.

An Efficient Precoder Size for Interference Alignment of the $K$ -User Interference Channel

In this letter, we explore interference alignment for the $K$ -user single input single output interference channel with more efficient precoder sizes and lower sensitivity to channel state information at transmitters (CSIT). The main idea in this letter is to divide transmission time into some distinct blocks and select a subset of transmitters and receivers for signaling in each block. We show that our method can achieve more sum degrees-of-freedom compared with other schemes in the limited number of channel extensions. Furthermore, our method needs no full CSIT to be applicable which can be considered as another advantage of this letter.

Optimal Resource Allocation for Power-Efficient MC-NOMA With Imperfect Channel State Information

In this paper, we study power-efficient resource allocation for multicarrier non-orthogonal multiple access (MC-NOMA) systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which jointly designs the power allocation, rate allocation, user scheduling, and successive interference cancellation (SIC) decoding policy for minimizing the total transmit power. The proposed framework takes into account the imperfection of channel state information at transmitter (CSIT) and quality of service (QoS) requirements of users. To facilitate the design of optimal SIC decoding policy on each subcarrier, we define a channel-to-noise ratio outage threshold. Subsequently, the considered non-convex optimization problem is recast as a generalized linear multiplicative programming problem, for which a globally optimal solution is obtained via employing the branch-and-bound approach. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. To strike a balance between system performance and computational complexity, we propose a suboptimal iterative resource allocation algorithm based on difference of convex programming. Simulation results demonstrate that the suboptimal scheme achieves a close-to-optimal performance. Also, both proposed schemes provide significant transmit power savings than that of conventional orthogonal multiple access (OMA) schemes.

Channel Reciprocity Calibration in TDD Hybrid Beamforming Massive MIMO Systems

Hybrid analog-digital (AD) beamforming structure is a very attractive solution to build low cost massive multiple-input multiple-output (MIMO) systems. Typically these systems use a set of fixed beams for transmission and reception to avoid the need to obtain channel state information at transmitter (CSIT) for each antenna element individually. However, such a method can not fully exploit the potential of hybrid AD beamforming systems. Alternatively, CSIT can be estimated by assuming a model for the propagation channel, whereas this model is only validated in millimeter-wave (mmWave) band thanks to its poor scattering nature. In this paper, we focus on time division duplex (TDD) systems with hybrid beamforming structure and propose a reciprocity calibration scheme that allows to acquire full CSIT. Different to existing CSIT acquisition methods, our approach does not require any assumption on the channel model and can, in theory, estimate the CSIT up to an arbitrary small error.

Cooperative Channel Estimation for Coordinated Transmission With Limited Backhaul

Obtaining accurate global channel state information (CSI) at multiple transmitter devices is critical to the performance of many coordinated transmission schemes. Practical CSI local feedback often leads to noisy and partial CSI estimates at each transmitter. With rate-limited bi-directional backhaul, transmitters have the opportunity to exchange few CSI-related bits to establish global channel state information at transmitter (CSIT). This paper investigates possible strategies toward this goal. We propose a novel decentralized algorithm that produces minimum mean square error-optimal global channel estimates at each device from combining local feedback and information exchanged through backhauls. The method adapts to arbitrary initial information topologies and feedback noise statistics and can do that with a combination of closed-form and convex approaches. Simulations for coordinated multi-point transmission systems with two or three transmitters exhibit the advantage of the proposed algorithm over conventional CSI exchange mechanisms when the coordination backhauls are limited.