Channel State Information Scenario Research Articles

Graph traversal aided detection in uplink MBM massive MIMO based on socio‐cognitive knowledge of swarm optimization

SummaryMedia‐based modulation (MBM) plays a crucial role in enhancing the spectral efficiency and energy efficiency of massive MIMO (mMIMO) systems for 5G and beyond wireless communications. In MBM, multiple parasitic elements, also termed as radio frequency (RF) mirrors, are placed near the transmit antennas for generating different channel fade realizations. These realizations are obtained by ON/OFF switching of RF mirrors. One of those channel fade realizations is selected (using a part of the incoming information bits) for transmitting a part of the information bits utilizing a symbol chosen from the conventional constellation set (using another part of the incoming information bits). Transmission of a symbol through one of the available channel realizations constitutes a sparse transmit vector for each user in MBM‐mMIMO. The sparse nature of transmitted symbols from multiple users and inter‐user interference makes the symbol detection in uplink MBM‐mMIMO challenging. Therefore, in this article, the problem of symbol detection in MBM‐mMIMO is analyzed from a graph‐theoretical point of view, and a graph‐traversal aided low‐complexity symbol detection algorithm is proposed inspired by socio‐cognitive learning of swarm optimization. Also, the convergence characteristic of the proposed technique is investigated theoretically. Further, an analytical expression of upper bound on bit error rate performance is derived and corroborated through simulations. Viability and robustness of the proposed technique are also justified through simulations over state‐of‐art detection techniques, under both perfect and imperfect channel state information scenarios.

Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

As one of the key technologies in the fifth generation of mobile communications, massive multi‐input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single‐cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero‐forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single‐cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single‐cell perfect CSI scenario. In the single‐cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.

Performance Analysis of Downlink Non-Orthogonal Multiple Access under Imperfect CSI in Dense Network: A Stochastic Geometry Approach

A single-tier downlink Non-Orthogonal Multiple Access (NOMA) network is considered in this paper where analytical framework is given for performance evaluation. Particularly, the outage probability at the downlink users is studied under the impact of imperfect Channel State Information (CSI). The inter-cell interference and the intra-cell interference are also taken into consideration to capture practical behaviors of the proposed system. Further, we also adopt the Poisson Point Process (PPP) to model the location of the base stations and the downlink users in this work. For the imperfect CSI scenario, the results point out that the more times the user spends on performing interference cancellation, the more severe the user is affected by channel estimation error and vice versa. The results for the outage probability expressed in the analytical-forms are then confirmed by the Monte Carlo simulations.

Interference alignment in two‐hop multiple‐input multiple‐output interference channel under various CSI and antenna correlation

In this paper, the problem of interference management is investigated in the downlink of multi-user multiple-input multiple-output two-hop interference channel under various scenarios of channel state information (CSI) and antenna correlation. Interference alignment (IA) in the space dimension is a powerful tool to solve this problem. Iterative IA algorithms are adopted in both the first and the second hops transmissions independently. After aligning interfering signals, the recovery of the transmitted signal vector is performed based on zero-forcing and semi-definite relaxation detectors. Furthermore, the impacts of the imperfect CSI and antenna correlation on sum-rate are investigated for decode-and-forward (DF) and amplify-and-forward relaying protocols. When the system is proper but IA is not completely feasible, the performance of minimum interference leakage (MIL) is compared with rank-constrained rank minimisation (RCRM) algorithm in DF protocol. RCRM algorithm is more sensitive to CSI such that the channel uncertainty easily leads to an increase in the interference matrix rank. According to the simulation results, for the case of perfect CSI, RCRM makes further interference-free dimensions which leads to better performance than MIL, but for the case of imperfect CSI, close performance is observed.

Interference Alignment in Relay-Aided Networks with Imperfect CSIT

In this paper, a relay-aided interference alignment (RIA) strategy was considered based on the benefits offered by relay-based cooperative networks. Along with the benefits offered by cooperative networks, the RIA was shown as a practical solution for a K-user BC network. The relationship between the achievable DoF region of the RIA and the different levels of outdated channel state information (CSI) were derived. The trade-off between the NRIA and RIA were analysed and the specific trade-off region in each outdated CSI assumption was indicated in the numerical results. Through the achievable DoF numerical results, it was recognised that regarding achievable DoF, the RIA outperforms the NRIA in most of the outdated CSI scenarios.

Joint Beamforming Design and Receive Antenna Selection for Large-Scale MIMO Wiretap Channels

This paper investigates joint secure beamforming design and receive antenna selection problem for large-scale multiple-input-multiple-output (MIMO) wiretap channels. A Branch-And-Bound (BAB) based algorithm is utilized to search the optimal receive antenna subset under both perfect and imperfect channel state information (CSI) scenarios. First, for the perfect CSI scenario, we formulate the secrecy rate optimization problem, and an equivalent monotonically decreasing objective function is established to make the problem suitable for efficient BAB search. In each search, we propose using the weighted mean squared error minimization (WMMSE) method to tackle the optimization problem. Then, we generalize the framework to the imperfect CSI case, and the robust problem is designed to maximize the worst-case secrecy rate, the problem is challenging to solve due to the channel uncertainties. A cutting-set method is devised to solve this non-convex problem by updating the worst-case secrecy rate and channel uncertainties alternately. To reduce the design complexity, we also present an antenna selection based on the alternating optimization (AO) method. The validity of the proposed antenna selection algorithms is evaluated through numerical simulations.

Optimization of secure wireless communications for IoT networks in the presence of eavesdroppers

The problem motivates this paper is that securing the critical data of 5G based wireless IoT network is of significant importance. Wireless 5G IoT systems consist of a large number of devices (low-cost legitimate users), which are of low complexity and under strict energy constraints. Physical layer security (PLS) schemes, along with energy harvesting, have emerged as a potential candidate that provides an effective solution to address this issue. During the data collection process of IoT, PHY security techniques can exploit the characteristics of the wireless channel to ensure secure communication. This paper focuses on optimizing the secrecy rate for simultaneous wireless information and power transfer (SWIPT) IoT system, considering that the malicious eavesdroppers can intercept the data. In particular, the main aim is to optimize the secrecy rate of the system under signal to interference noise ratio (SINR), energy harvesting (EH), and total transmits power constraints. We model our design as an optimization problem that advocates the use of additional noise to ensure secure communication and guarantees efficient wireless energy transfer. The primary problem is non-convex due to complex objective functions in terms of transmit beamforming matrix and power splitting ratios. We have considered both the perfect channel state information (CSI) and the imperfect CSI scenarios. To circumvent the non-convexity of the primary problem in perfect CSI case, we proposed a solution based on the concave-convex procedure (CCCP) iterative algorithm, which results in a maximum local solution for the secrecy rate. In the imperfect CSI scenario, we facilitate the use of S-procedure and present a solution based on the iterative successive convex approximation (SCA) approach. Simulation results present the validations of the proposed algorithms. The results provide an insightful view that the proposed iterative method based on the CCCP algorithm achieves higher secrecy rates and lower computational complexity in comparison to the other algorithms.

Benefits of Improper Gaussian Signaling in Interweave Cognitive Radio With Full and Partial CSI

In this paper, we investigate the impact of using improper Gaussian signaling (IGS) scheme on the interweave cognitive radio (CR) paradigm when the cognitive user has access to full or partial channel-state-information (CSI). Throughout this work, we analyze the performance of both the primary user (PU) and secondary user (SU) in terms of instantaneous achievable rate and outage probability, for given sensing and detection capabilities. Then, we optimize the IGS scheme for full and partial CSI scenarios to maximize the achievable rate of the SU while maintaining a minimum quality-of-service (QoS) requirement for the PU. Meanwhile, we derive the corresponding feasibility conditions for the interweave cognitive radio system operation. IGS based systems are proven to provide notable gain over the conventional proper Gaussian signaling, which is considered our benchmark scheme. The IGS scheme is observed to be further beneficial at low SU detection capabilities, strong interference from the cognitive user to the licensed user, and low gain of the PU direct link.

Dynamic Power Splitting for SWIPT With Nonlinear Energy Harvesting in Ergodic Fading Channel

We study the dynamic power splitting for simultaneous wireless information and power transfer (SWIPT) in the ergodic fading channel. Considering the nonlinearity of practical energy harvesting circuits, we adopt the realistic nonlinear energy harvesting (EH) model rather than the idealistic linear EH model. To characterize the optimal rate-energy (RE) tradeoff, we consider the problem of maximizing the R-E region, which is nonconvex. We solve this challenging problem for two different cases of the channel state information (CSI): (i) when the CSI is known only at the receiver (the CSIR case) and (ii) when the CSI is known at both the transmitter and the receiver (the CSI case). For these two cases, we develop the corresponding optimal dynamic power splitting schemes. To address the complexity issue, we also propose the suboptimal schemes with low complexities. Comparing the proposed schemes to the existing schemes, we provide various useful and interesting insights into the dynamic power splitting for the nonlinear EH. Furthermore, we extend the analysis to the scenarios of the partial CSI at the transmitter and the harvested energy maximization. Numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal scheme works very close to the optimal scheme at a much lower complexity.

Performance Analysis of a SAGE-Based Semi-Blind Channel Estimator for Pilot Contaminated MU Massive MIMO Systems

Massive multiple-input multiple-output (M-MIMO) is one of the key ingredients in the upcoming 5G technology. The benefits associated with M-MIMO rely largely on the accuracy of channel state information (CSI) available at the base station (BS). The existing literature mostly employs pilot-aided schemes which require additional pilots for improving their CSI accuracy; additionally, pilot contamination degrades their performance to a large extent. In this paper, we design a Space-Alternating Generalized Expectation-Maximization (SAGE) based semi-blind estimator for pilot contaminated multi-user (MU) M-MIMO systems. It utilizes both pilot and a few data symbols for CSI estimation through two stages, namely initialization and iterative SAGE (ISAGE). We obtain an initial channel estimate with the help of a pilot-aided linear minimum mean squared error (LMMSE) estimator in the initialization stage. The acquired initial estimate from the former stage is then iteratively updated by SAGE algorithm with the joint usage of pilot and a few data symbols in ISAGE stage. The inclusion of data information in ISAGE stages' estimation process aids in simultaneous improvement of CSI accuracy and spectral efficiency (SE) of a M-MIMO system; which is unlikely for a pilot based estimation scheme. Through simulations, we show that our estimator obtains a considerable improvement over the existing pilot-aided schemes in terms of mean squared error (MSE), bit error rate (BER), SE, and energy efficiency (EE) at a nominal increase in complexity. Besides, on average, it achieves convergence in almost two iterations. We also derive modified Cramer-Rao lower bound (MCRLB) to validate the estimation efficacy of our estimator. We evaluate a closed-form expression for lower bound on UL achievable rate of MU M-MIMO systems under both perfect and imperfect CSI scenario. We also discuss the trade-off between SE and EE.

User-Centric Joint Access-Backhaul Design for Full-Duplex Self-Backhauled Wireless Networks

Full-duplex self-backhauling is promising to provide cost-effective and flexible backhaul connectivity for ultra-dense wireless networks, but also poses a great challenge to resource management between the access and backhaul links. In this paper, we propose a user-centric joint access-backhaul transmission framework for full-duplex self-backhauled wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by multiple small base stations (SBSs). In the backhaul link, user-centric multicast transmission is proposed so that each user’s message is treated as a common message and multicast to its serving SBS cluster. We first formulate an optimization problem to maximize the network weighted sum rate through joint access-backhaul beamforming and SBS clustering when global channel state information (CSI) is available. This problem is efficiently solved via the successive lower-bound maximization approach with a novel approximate objective function and the iterative link removal technique. We then extend the study to the stochastic joint access-backhaul beamforming optimization with partial CSI. Simulation results demonstrate the effectiveness of the proposed algorithms for both full CSI and partial CSI scenarios. They also show that the transmission design with partial CSI can greatly reduce the CSI overhead with little performance degradation.

Delay-Optimal Resource Scheduling of Energy Harvesting-Based Devices

This paper investigates resource scheduling in a wireless communication system operating with energy harvesting (EH)-based devices and perfect channel state information (CSI). The aim is to minimize the packet loss that occurs when the buffer is overflowed or when the queued packet is older than a certain pre-defined threshold. So, we consider a strict delay constraint rather than an average delay constraint. The associated optimization problem is modeled as the Markov decision process (MDP) where the actions are the number of packets sent on the known channel at each slot. The optimal deterministic offline policy is exhibited through dynamic programming techniques, i.e., value iteration (VI) algorithm. We show that the gain in the number of transmitted packets and the consumed energy is substantial compared to: 1) a naive policy which forces the system to send the maximum number of packets using the available energy in the battery; 2) two variants of the previous policy that take into account the buffer state; and 3) a policy optimized with an average delay constraint. Finally, we evaluate our optimal policy under imperfect CSI scenario where only an estimate of the channel state is available.

Low‐overhead constant envelope precoding in multi‐cell massive MIMO systems with pilot contamination

The authors consider downlink transmission in a multi-cell massive multiple-input and multiple-output (MIMO) system and focus on decreasing the feedback overhead caused by cell cooperation in constant envelope precoding (CEP). In the literature, the single-cell case for CEP with perfect channel state information (CSI) has been studied. Here, considering full cooperation among the cells, they develop CEP for the multi-cell case. They devise a low overhead centralised construction for CEP which employs limited cooperation among the cells, providing higher system throughput. To achieve the minimum feedback overhead, a distributed realisation of CEP is proposed where each cell locally performs CEP. Furthermore, a new optimisation problem is solved to compensate for the effects of pilot contamination. In addition, to reduce the computational complexity, a relaxed iterative form is presented for the limited cooperation and the distributed scenarios. Numerical results show that in the proposed structures, despite a tremendous decrease in the system overhead, the performance confronts merely an insignificant degradation, <10%, compared to the full-cooperation case. Moreover, in the presence of imperfect CSI, the performance of the distributed structure whose imperfect CSI is compensated for, approaches the performance of this structure in the perfect CSI scenario.

On the Energy Efficiency of Interference Alignment in the $K$ -User Interference Channel

Interference alignment (IA) is regarded as an important physical-layer interference management technique. Most research contributions on IA were focused on the analysis of its achievable spectral efficiency, either from the degrees of freedom or from the capacity perspective. Meanwhile, high energy-efficiency (EE) has become a key requirement for next-generation wireless communications. Hence, we focus our attention on the EE of IA in the fully connected $K$ -user interference channel, where each user has either a single antenna or multiple antennas. We consider both perfect and imperfect channel state information scenarios. New insights into the achievable EE of IA are obtained by investigating the impact of different precoding matrices, the number of users, the number of antennas, symbol extension values, channel estimation accuracy, total transmit power, and power allocation schemes. In particular, we demonstrate that in the single-antenna-user case, the IA schemes relying on unequal power allocation achieves higher EE than their equal power allocation counterparts. However, in the scenario where each user is equipped with multiple antennas, equal power allocation achieves higher EE than unequal power allocation for the IA. Furthermore, using non-uniform precoding-matrix-generating vector ${\boldsymbol {w}}$ is not necessarily beneficial for improving the achievable EE of IA. We also find that the EE performance of IA with smaller symbol extension values is higher than that with larger symbol extension values, the achievable EE of IA decays with the increase of the total transmit power, the EE performance of IA degrades as the channel estimation accuracy becomes low and that having a larger number of transmit/receive antennas on each user achieves a higher EE in IA.

Full-duplex wireless-powered jammer aided secure communication for cognitive radio networks

A novel full-duplex (FD) wireless-powered cooperative jammer (CJ) aided transmit scheme is proposed to enhance the security for cognitive radio networks (CRNs). The CJ operates in an FD mode, so that it can harvest energy from the signals that transmitted from the secondary transmitter (ST), and send jamming noise (JN) to confuse the potential malicious eavesdroppers in the meantime. We aim to maximize the achievable secrecy rate of the secondary system by jointly designing the transmit covariance matrix at the ST and the JN covariance matrix at the CJ, while subject to the transmit power constraints at the ST and the CJ, and the interference constraint at the primary user (PU). Specifically, we formulate the secrecy rate maximization problem based on the perfect channel state information (CSI). As a solution, we propose a two-stage optimization algorithm to solve the non-convex problem. More particularly, we extend our proposed scheme to the imperfect CSI case. To tackle the robust secrecy rate maximization problem, we exploit the two-stage optimization algorithm with the extended S-procedure and rank-one relaxation. Simulation results have validated that our proposed scheme outperforms, in terms of secrecy rate, the isotropic JN scheme and the scheme without aided by CJ for the perfect CSI scenario. In addition, the robust scheme achieves higher secrecy rate gains as compared to the non-robust scheme for the imperfect CSI case.

On the Performance of Wireless Powered Cognitive Relay Network With Interference Alignment

In this paper, a two-hop decode-and-forward wireless powered relaying cognitive radio network with interference alignment over Rayleigh fading channels is investigated. The energy-constrained secondary relay node harvests energy from both the information and interference signals. Then, the harvested energy is used by the relay to forward the information signal from the source to the destination node. By applying beamforming matrices to primary and secondary networks, the performance metrics, such as outage probability, capacity, and bit error rate are studied under perfect and imperfect channel state information scenarios for both power-splitting relaying (PSR) and time-switching relaying (TSR) protocols. In addition, the optimal network performance is achieved by calculating the optimal energy harvesting time-switching and power-splitting coefficients. Finally, closed-form expressions for the outage probability of primary and secondary users are derived. Monte Carlo simulation results corroborate the analytical ones and show that the PSR technique outperforms TSR in the considered system model.

Joint Prioritized Scheduling and Resource Allocation for OFDMA-Based Wireless Networks

In this paper, we study the joint prioritized link scheduling and resource allocation for OFDMA-based wireless networks, which serve two classes of wireless links, namely, non-prioritized (low-priority) and prioritized (high-priority) links. Our design aims to maximize the number of scheduled non-prioritized links and their sum rate, while guaranteeing the minimum required rates of all active prioritized and non-prioritized links. We present the problem formulation as a single-stage optimization problem, which simultaneously maximizes the number of scheduled non-prioritized links and their sum rate. We propose a monotonic-based optimal approaching (MBOA) algorithm to solve this problem by employing the monotonic global optimization technique and an efficient rounding procedure. We prove that the MBOA algorithm can schedule the maximum number of non-prioritized links with slight and controllable degradation in the minimum required rates of non-prioritized links. For low-complexity design, we propose an iterative convex approximation algorithm, which sequentially performs power allocation and link removal in each iteration. We then describe how the proposed algorithms can be implemented in the standardized LTE-based cellular system. Finally, we conduct numerical studies for device-to-device communications underlaid cellular networks under perfect or imperfect channel state information (CSI). Numerical results demonstrate that the proposed algorithms can be applied to the imperfect CSI scenario with slight degradation in the network performance. Moreover, in the perfect CSI scenario, the proposed algorithms significantly outperform the conventional algorithms both in the number of scheduled non-prioritized links and their sum rate.

Unified Performance Analysis of Transmit Antenna Selection With OSTBC and Imperfect CSI Over Nakagami- m Fading Channels

A unified error analysis of multi-antenna systems with orthogonal space-time block coding (OSTBC) using transmit antenna selection (TAS) over Nakagami- $m$ channels in the presence of feedback delay and channel estimation error is presented, where the number of transmit antennas is arbitrarily selected. For imperfect channel state information (CSI), the Nakagami- $m$ channel is modeled as the sum of Nakagami- $\hat{m}$ channel and independent Gaussian channel error. By this model, accurate and closed-form approximate bit error rate (BER) expressions of TAS-OSTBC for M-ary quadrature amplitude modulation and M-ary phase-shift keying are derived by using the moment generating function of received SNR. With the approximate BER formula, the number of antennas to be selected for transmission can be adapted by minimizing the BER of the system with imperfect CSI. The diversity and coding gains of TAS-OSTBC with $K$ receive antennas and $M$ antennas selected from $N$ transmit antennas for transmission are analyzed at high SNR. For noninteger $mK$ , the upper and lower bounds of the diversity gain for different CSI and estimation error scenarios are derived. But for integer $mK$ , the results indicate that the upper and lower bounds are equal achieving the full diversity order of $mKN$ for perfect CSI, zero diversity order and partial diversity order of $mMK$ for estimation error variance being fixed and being a decreasing function of average SNR, respectively. The coding gain is affected by $M$ , $N$ , $K$ , Nakagami parameter $m$ , code rate, modulation, and channel correlation coefficients. Simulation results verify the validity of the theoretical analysis and effectiveness of the adaptive antenna selection scheme.

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.