Imperfect Channel State Information Scenario Research Articles

Low Complexity Beamforming and User Selection Schemes for 5G MIMO-NOMA Systems

This paper investigates the resource allocation (RA) problem for the downlink multiple input multiple output-based non-orthogonal multiple access (MIMO-NOMA) system, which is usually solved through a beamforming (BF) process and a user selection (US) process. In this paper, we propose the multiple-user channel state information (CSI)-based singular value decomposition (MU-CSI-SVD) BF scheme and the minimization of power (Min-Power) US scheme. Under perfect CSI scenarios, the proposed MU-CSI-SVD achieves near optimal sum rate performance with the high-complexity BF scheme while has the same level computational complexity as the low-complexity BF scheme. For imperfect CSI scenarios, the proposed MU-CSI-SVD also achieves better outage probability performance but requires the same level computational complexity as the existing low-complexity BF scheme. Moreover, MU-CSI-SVD is helpful to decrease the computational complexity for solving the Min-Power US problem. Compared with existing US schemes, the proposed Min-Power scheme can achieve larger sum rate or lower outage probability with limited computational complexity increment. Therefore, in this paper, we develop a complete RA scheme for 5G MIMO-NOMA systems, which has excellent performance while with low computational complexity for both perfect and imperfect CSI scenarios.

On Artificial-Noise-Aided Transmit Design for Multiuser MISO Systems With Integrated Services

This paper considers artificial noise (AN)-aided transmit designs for multi-user MISO systems in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. Our goal is to jointly design the optimal input covariances for the multicast message, confidential message and AN, such that the achievable secrecy rate region is maximized subject to the sum power constraint. This secrecy rate region maximization (SRRM) problem is a nonconvex vector maximization problem. To handle it, we reformulate the SRRM problem into a provably equivalent scalar optimization problem and propose a searching method to find all of its Pareto optimal points. The equivalent scalar optimization problem is identified as a secrecy rate maximization (SRM) problem with the quality of multicast service (QoMS) constraints. Further, we show that this equivalent QoMS-constrained SRM problem, albeit nonconvex, can be efficiently handled based on a two-stage optimization approach, including solving a sequence of semidefinite programs. Moreover, we also extend the SRRM problem to an imperfect channel state information (CSI) case where a worst-case robust formulation is considered. In particular, while transmit beamforming is generally a suboptimal technique to the SRRM problem, we prove that it is optimal for the confidential message transmission whether in the perfect CSI scenario or in the imperfect CSI scenario. Finally, numerical results demonstrate that the AN-aided transmit designs are effective in expanding the achievable secrecy rate regions.

Stackelberg game based relay selection for physical layer security and energy efficiency enhancement in cognitive radio networks

In cognitive radio networks, physical layer security is a promising secure wireless communication solution against eavesdropping attacks. In this paper, we study the problems of physical layer security and energy efficiency through power control and relays’ cooperation, where both decode-and-forward and amplify-and-forward protocols are considered. We propose an one-leader one-follower Stackelberg (OLOFS) game model in the presence of multiple eavesdroppers, where optimal power allocation and pricing strategy can be determined to maximize the players’ utilities. We also present a best relay selection criterion for OLOFS game model in both perfect channel state information (CSI) and imperfect CSI scenarios, which maximizes the secrecy capacity of the network. A distributed learning algorithm, inspired by the stochastic learning automata, is then proposed to achieve the equilibrium of the proposed games. Finally, we derive closed-form intercept probability expressions of the direct transmission scheme and the proposed game model over Rayleigh fading channels in both decode-and-forward and amplify-and-forward protocols. Our simulations demonstrate that the proposed game model improves network energy efficiency and has an improved performance against eavesdropping attacks, in comparison to Nash equilibrium, rand, and direct transmission schemes. We can also reduce the intercept probability by choosing a different relaying protocol (decode-and-forward or amplify-and-forward), based on the characteristic of channels in the proposed model.

Transceiver Optimization for the Multi-Antenna Downlink in MIMO Cognitive System

Transceiver optimization in multiple input multiple output (MIMO) cognitive systems is studied in this paper. The joint transceiver beamformer design is introduced to minimize the transmit power at secondary base station (SBS) while simultaneously controlling the interference to primary users (PUs) and satisfying the secondary users (SUs) signal-to-interference-plus-noise ratio (SINR) based on the convex optimization method. Due to the limited cooperation between SBS and PUs, the channel state information (CSI) usually cannot be obtained perfectly at the SBS in cognitive system. In this study, both perfect and imperfect CSI scenarios are considered in the beamformer design, and the proposed method is robust to CSI error. Numerical results validate the effectiveness of the proposed algorithm.

Transceiver Optimization for the Multi-Antenna Downlink in MIMO Cognitive System

Transceiver optimization in multiple input multiple output (MIMO) cognitive systems is studied in this paper. The joint transceiver beamformer design is introduced to minimize the transmit power at secondary base station (SBS) while simultaneously controlling the interference to primary users (PUs) and satisfying the secondary users (SUs) signal-to-interference-plus-noise ratio (SINR) based on the convex optimization method. Due to the limited cooperation between SBS and PUs, the channel state information (CSI) usually cannot be obtained perfectly at the SBS in cognitive system. In this study, both perfect and imperfect CSI scenarios are considered in the beamformer design, and the proposed method is robust to CSI error. Numerical results validate the effectiveness of the proposed algorithm.

Relay Opportunistic Spectrum Sharing Based on the Full‐Duplex Transceiver

Opportunistic spectrum sharing (OSS) and cooperative spectrum sharing (CSS) are typical dynamic spectrum accessing (DSA) technologies to effectively improve spectrum efficiency (SE) and energy efficiency (EE). However, OSS provides no benefit to the primary user, whereas CSS, although simultaneously improving SE of the primary user and the secondary user, needs a proper cooperative mechanism with negotiation cost. To overcome these drawbacks, two kinds of relay OSS (ROSS) models are proposed to maximize SE and EE, respectively. When full-duplex transceiver is adopted by the secondary user, the proposed ROSS models can simultaneously improve the performance of both the primary user and the secondary user without requiring any negotiation. To make the study more complete, we consider both perfect channel state information (CSI) and imperfect CSI scenarios. Numerical results demonstrate the effectiveness of the proposed models and show that the performance of both the primary user and the secondary user can be improved under ROSS models.

Overlapping coalition formation games‐based resource allocation for device‐to‐device communication in LTE‐A network

AbstractIn the long‐term evolution‐advanced system, device‐to‐device (D2D) communication brings many significant benefits, such as low power consumption, low delay, high bit rates and efficient frequency utilisation. However, these potential advantages hinge on the intelligent resource allocation approach between D2D user equipments (DUEs) and cellular user equipments (CUEs). Different from existing work that focused on the scenario that one CUE shares spectrums with one D2D pair and thus disjoint groups are formed to use spectrums, in our paper, we proposed a coalition formulation game with overlapping coalitions. In this game, we model the resource sharing problem as a transferable coalition formation game where there exists intersection among these coalitions and the DUEs can make a decision to join or leave a coalition according to the merge‐and‐split rule. Consequently, not only one D2D pair can employ the same resource with multiple CUEs but also the channel of one CUE can be allocated to multiple D2D pairs in order to maximise the frequency efficiency and the system sum throughput. In particular, we consider the practical case and study the effect that only imperfect channel state information (CSI) can be obtained on the stable coalition formation game. A discrete‐time Markov chain‐based analysis is presented to obtain the stable coalition structure, and the complexity of employing this scheme is evaluated. Extensive numerical results show that the proposed overlapping coalition formulation game‐based scheme results in better performance compared to the traditional existing methods even in the imperfect CSI scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

Massive MIMO Multicasting in Noncooperative Cellular Networks

We study the massive multiple-input multiple-output (MIMO) multicast transmission in cellular networks where each base station (BS) is equipped with a large-scale antenna array and transmits a common message using a single beamformer to multiple mobile users. We first show that when each BS knows the perfect channel state information (CSI) of its own served users, the asymptotically optimal beamformer at each BS is a linear combination of the channel vectors of its multicast users. Moreover, the optimal combining coefficients are obtained in closed form. Then we consider the imperfect CSI scenario where the CSI is obtained through uplink channel estimation in timedivision duplex systems. We propose a new pilot scheme that estimates the composite channel which is a linear combination of the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The pilot power control for optimizing the multicast beamformer at each BS is also derived. Numerical results show that the asymptotic performance of the proposed scheme is close to the ideal case with perfect CSI. Simulation also verifies the effectiveness of the proposed scheme with finite number of antennas at each BS.

Efficient multiple antenna–relay selection algorithms for MIMO unidirectional–bidirectional cognitive relay networks

AbstractIn this paper, we consider a multiple‐input multiple‐output (MIMO) cooperative cognitive radio (CR) system using amplify‐and‐forward protocol under a spectrum sharing scenario, where licensed users and unlicensed users operate on the same frequency band. Indeed, combined CR, cooperative communication and MIMO antennas provide a smart solution for a more efficient usage of the frequency band and the data rate. The main objective of this work is to maximise the sum rate of the unlicensed users allowed to share the spectrum with the licensed users by respecting a tolerated interference threshold under perfect and imperfect channel state information scenarios. Practical approaches based on iterative and genetic algorithms for multiple antenna–relay selections with generalised MIMO model for both unidirectional and bidirectional transmissions are proposed to solve our formulated optimization problems. Interestingly, selected numerical results show that our proposed approaches reach a performance close to the performance of the optimal solution either with discrete or continuous power distributions while providing a considerable saving in terms of computational complexity. Copyright © 2014 John Wiley & Sons, Ltd.

A robust and distributed design for coordinated downlink beamforming for secure MISO interference channels

ABSTRACTIn this paper, we address the optimization problems of downlink coordinated beamforming over the K‐user multiple‐input single‐output interference channel with confidential messages. Our design objective is to minimise the total transmit power subject to the constraints of individual signal‐to‐interference plus noise ratio and equivocation rates. For the case with perfect channel state information (CSI), by deriving the equivalent forms for the signal‐to‐interference plus noise ratio and the secrecy constraints, the optimization problem is reformulated as a second‐order cone programme problem. Then, an optimal distributed algorithm using the partial Lagrangian duality is discussed. In addition we extend the analysis to the imperfect CSI scenario with bounded ellipsoidal errors, the centralised algorithm has been proposed by solving a semi‐definite programme with the help of rank relaxation, whereas the distributed robust algorithm is devised by using ADMM. Finally, the simulation results have shown that with perfect CSI, the centralised and distributed algorithms have nearly the same performance, but in the case of imperfect CSI, the centralised algorithm shows a slightly better performance. Copyright © 2014 John Wiley & Sons, Ltd.

Novel Robust Optimization and Power Allocation of Time Reversal-MIMO-UWB Systems in an Imperfect CSI

Time Reversal (TR) technique is an attractive solution for a scenario where the transmission system employs low complexity receivers with multiple antennas at both transmitter and receiver sides. The TR technique can be combined with a high data rate MIMO-UWB system as TR-MIMO-UWB system. In spite of TR's good performance in MIMO-UWB systems, it suffers from performance degradation in an imperfect Channel State Information (CSI) case. In this paper, at first a robust TR pre-filter is designed together with a MMSE equalizer in TR-MIMO-UWB system where is robust against channel imperfection conditions. We show that the robust pre-filter optimization technique, considerably improves the BER performance of TR-MIMO-UWB system in imperfect CSI, where temporal focusing of the TR technique is kept, especially for high SNR values. Then, in order to improve the system performance more than ever, a power loading scheme is developed by minimizing the average symbol error rate in an imperfect CSI. Numerical and simulation results are presented to confirm the performance advantage attained by the proposed robust optimization and power loading in an imperfect CSI scenario.

Mitigation of channel estimation error in TR–UWB system based on a novel MMSE equalizer

The time reversal (TR) technique combined with the ultra-wideband (UWB) system offers a new potential for decreasing the cost and complexity of the UWB receivers. In spite of TR–UWB's good performance in perfect channel state information (CSI), it is very sensitive to the channel estimation error. The effect of channel imperfection on the TR–UWB system is considered in this paper. At first, based on a minimum mean square error (MMSE) equalizer receiver, a prefilter is calculated in closed form to improve the performance of the TR–UWB system in an imperfect CSI scenario. Furthermore, for comparison purposes, a similar calculation for prefilter is carried out based on a simple matched filter (MF) receiver. Then, in order to improve the MF receiver performance, a two-stage iteration-based algorithm is developed. The initial value for this iteration-based improved algorithm is considered to be a prefilter which is calculated in the TR–UWB system with MMSE equalizer. This optimized algorithm causes the channel estimation error in the TR–UWB system to become zero in some steps. Finally, exhaustive simulations are done to demonstrate the performance advantage attained by the improved algorithm.

Effect of channel estimation error on performance of time reversal-UWB communication system and its compensation by pre-filter

Time reversal (TR) technique is an effective and simple method for data transmission in extremely multipath indoor ultra-wideband (UWB) channels. Temporal focusing property supported by TR, mitigates the complexity of receiver by reducing the effective channel taps. In spite of its good performance in perfect channel state information (CSI), TR method is very sensitive to the channel estimation error. The effect of channel imperfection on TR technique is considered the study. At first, the bit error rate (BER) of the TR-UWB communication system under the assumptions of the simple matched filter (MF) receiver under the channel estimation errors is derived in closed-form. Then, based on optimal minimum mean square error (MMSE) estimator receiver, a pre-filter is calculated in closed-form to improve the performance of the TR-UWB system in an imperfect CSI scenario. Furthermore, in order to compare the results, similar calculation for pre-filter is carried out based on a simple MF receiver. Unfortunately, it was not possible for us to drive a closed-form solution for pre-filter in this case. To overcome this shortcoming, a two stage iteration-based algorithm is developed at transmitter to calculate a pre-filter in MF receiver. This improved algorithm causes the channel estimation error tends to zero in some steps for the TR-UWB system with MF. The initial value for this iteration-based improved algorithm is considered to be closed-form pre-filter where is calculated based on TR-UWB system with an optimal MMSE estimator. Finally, exhaustive simulations are done to demonstrate the performance advantage attained by the proposed algorithms.

Statistically robust design of linear filter for non-regenerative MIMO relay systems

This paper addresses the robust linear filter design issues for non-regenerative multiple input multiple output (MIMO) relay systems with imperfect channel state information (CSI) in both or partial hops. By considering statistical Kronecker channel model involving channel mean and antenna correlation, the robust linear processing schemes in imperfect CSI scenario for both hops are first derived based on mean squared error (MSE) criterion. In addition to this, the result is also extended to two practical scenarios, i.e. imperfect CSI for relay link with perfect CSI for access link and imperfect CSI for access link with perfect CSI for relay link. Simulation results show that the proposed scheme is capable of mitigating the performance degradation caused by the imperfect CSI.

Outage Behavior of Discrete Memoryless Channels Under Channel Estimation Errors

Communication systems are usually designed by assuming perfect channel state information (CSI). However, in many practical scenarios, only a noisy estimate of the channel is available, which may strongly differ from the true channel. This imperfect CSI scenario is addressed by introducing the notion of estimation-induced outage (EIO). We derive a single-letter characterization of the maximal EIO rate and prove an associated coding theorem and its strong converse for discrete memoryless channels (DMCs). The transmitter and the receiver rely on the channel estimate and the statistics of the estimate to construct codes that guarantee reliable communication with a certain outage probability. This ensures that in the non-outage case the transmission meets the target rate with small error probability, irrespective of the quality of the channel estimate. Applications of the EIO capacity to a single-antenna (nonergodic) Ricean fading channel are considered. The EIO capacity for this case is compared to the EIO rates of a communication system in which the receiver decodes by using a mismatched maximum-likelihood (ML) decoder. The effects of rate-limited feedback to provide the transmitter with quantized CSI are also investigated.

Tomlinson–Harashima precoding with imperfect channel state information

Nonlinear Tomlinson–Harashima precoding (THP) is an attractive solution for a scenario where the transmission system employs multiple antennas at transmitter and multiple users with a single antenna at the receiver, so that the cooperation among the receive antennas are impossible (downlink scenario). THP solution based on zero forcing (ZF) and minimum mean square error (MMSE) criteria is one of the important techniques to achieve near multiple input multiple output channels capacity with reasonable complexity. In this paper, the effect of channel imperfection on THP is considered. At first, the achievable rate of THP with respect to ZF criterion in an imperfect channel state information (CSI) scenario is calculated. Moreover, based on MMSE criterion, a new robust solution is derived which provides a significant improvement with respect to the conventional optimisation method. Then, the effect of channel estimation error on THP is considered as an improved optimisation where THP filters are optimised together with a channel estimator. Spatial power loading is found to be important to the THP performance. This loading for robust/joint optimisation of MMSE THP is developed by minimum average symbol error rate sense. Simulation results show the capacity loss, the performance advantage attained by the robust/joint optimisation and the power loading in an imperfect CSI scenario.