Game-Theoretic Joint Power Allocation and Feedback Rate Control for Cognitive MIMO Systems with Limited Feedback

We investigate the problem of robust joint transmitter and receiver power allocation for uplink multi-input multi-output (MIMO) transmissions. The channel model is assumed to have Rayleigh flat fading. The objective of power allocation is to minimize the total MSE each of which has limited transmit power. The problem is formulated as a non-linear optimization problem. In addition, we investigate robust joint transmitter and receiver power allocation with imperfect channel state information (CSI). The CSI error is assumed to be unknown but bounded by a constant. This problem is formulated as a semidefinite programming (SDP) problem with bilinear matrix inequality (BMI) constraints. Numerical results indicate that, with imperfect CSI, the BER performance improvement obtained by taking robustness into account in the joint power allocation process.

For the next generation wireless communication system, Multi carrier Code Division Multiple Access (MC-CDMA) has become very appealing scheme due to due to high data rates and interference limiting factors. In this research work we analyzes the capacity of MC-CDMA system using adaptive power and sub-channel allocation under perfect, imperfect and predicted channel state information (CSI). The transmitter and receiver both have been targeted in this paper. The proposed scheme is a novel idea that divides the sub-channels into small groups. Each group is assigned power according to the Eigen values and gain of the each sub-channel in that specific group. The receiver calculates optimal feedback vector for the transmitter by calculating the bit error rates using the above strategy and selects the optimal feedback vector. The simulations show that the usage of joint power and sub-channel allocation outperforms the capacity of the system. The capacity of the system is highly improved using adaptive power and sub-carriers allocation along with CSI prediction. The simulation results have been carried out for other power allocation schemes available in literature and results have been compared with perfect, imperfect and predicted CSI.

In this paper, we propose a channel assignment and power allocation sheme for non-orthogonal multiple access (NOMA) device-to-device (D2D) communications. Our optimization goal is to maximize the sum rate of the D2D system, and considering the fairness factor simultaneously. Importantly, we innovatively consider the impact of imperfect channel state information (CSI) in our system, which makes our proposed algorithm more realistic and effective. We split the problem into two sub-problems: 1) one-to-one matching for the cellular user and D2D transmitter and finding the appropriate receivers for the D2D transmitter to form the D2D group; 2) power allocation for each D2D group. For the channel assignment, we derive a closed form expression of the scheduled data rate of D2D link with considering imperfect CSI by using mathematical approximation methods. And we transform the power allocation subproblem from a non-convex problem to convex one and use the Lagrangian multiplier method to get the optimal power allocation coefficients, considering the sensitivity of the successive interference cancellation (SIC) receiver. Moreover, the theoretical analysis and simulation results are presented to evaluate our proposed scheme for the NOMA-based D2D communications.

Traditional beamforming and power control algo-rithms in cognitive radio (CR) are based on the assumption of perfect channel state information (CSI) however; this may lead to performance degradation in realistic systems.In this paper, the problem of joint beamforming and power control is investigated in underlay CR networks with imperfect CSI.Our objective is to maximize the sum utility of secondary users (SUs) under the primary users (PUs) interference power constraints and the transmission power constraint of SUs.First, the joint beamforming and power control problem is formulated under game theory framework, where the SUs compete with each other over the beamforming vectors and transmission power level made available by the PUs.Moreover, the channel uncertainty is described using ellipsoid sets and the interference power constraints can be converted into robust interference power constraints.Besides, Nash equilibrium (NE) is considered as the solution of this game.Finally, simulation results show that the proposed scheme can converge to a locally optimal pair of beamforming vector and transmission power level in the presence of channel uncertainty.

This paper investigates the resource management problem in multi-carrier rate-splitting multiple access (MC-RSMA) systems with imperfect channel state information (CSI) and successive interference cancellation (SIC) for ultra-reliable and low-latency communications (URLLC) applications. Effective throughput (ET) is employed as the key performance metric in this study to explore the trade-off between the decoding error probability and achievable rate. A mixed-integer non-convex problem is formulated to jointly optimize power allocation, rate control, and user grouping. To tackle this challenge, the achievable ET is approximated using a lower bound, and a decomposition approach is developed to decouple the optimization variables. Specifically, for a given user grouping scheme, an iteration-based concave-convex programming (CCCP) method and an iteration-free lower-bound approximation (LBA) method are proposed for power allocation and rate control. Furthermore, a greedy search-based scheme and a minimum correlation user grouping (MCUG) scheme are designed for the user grouping problem. Simulation results verify the effectiveness of the CCCP and LBA methods in power allocation and rate control and the MCUG method in user grouping when compared to the greedy and random methods. Besides, the superiority of RSMA for URLLC services is demonstrated when compared to spatial division multiple access.

ABSTRACTBy combining Spatial modulation (SM) with cooperative communication, the diversity gain brought by cooperative relaying and high rate of SM can be obtained. In this paper, considering amplify-and-forward (AF) protocol, the performance of cooperative SM system with imperfect channel state information (CSI) over Rayleigh channel is analyzed. According to the performance analysis, the moment generating functions (MGFs) of effective signal-to-noise ratios (SNRs) are derived. With the MGFs, closed-form expressions of the error probability of antenna index detection and the error probability of symbol detection for imperfect CSI are achieved. Thereby, the approximate overall average bit error ratio (BER) is obtained, and these theoretical expressions include those under perfect CSI as special cases. Besides, the asymptotically approximate BER for large SNR is also derived. Based on this, a suboptimal power allocation (PA) scheme is developed, and the corresponding PA coefficients are attained by minimizing the asymptotical BER. As a result, closed-form PA can be attained for the system with multiple receive antennas. Computer simulations show that the theoretical BERs are close to the corresponding simulated results, which illustrates the effectiveness of the derived theoretical expressions. Compared with the conventional equal PA scheme, the proposed PA scheme enhances the BER performance efficiently.

In this paper, we investigate the application of transfer learning to train a Deep Neural Network (DNN) model for joint channel and power allocation in underlay device-todevice (D2D) communication. Based on the traditional optimization solutions, generating training dataset for scenarios with perfect channel state information (CSI) is not computationally demanding, compared to scenarios with imperfect CSI. Thus, a transfer learning-based approach can be exploited to transfer the DNN model trained for the perfect CSI scenarios to the imperfect CSI scenarios. We also consider the issue of defining the similarity between two types of resource allocation tasks. For this, we first determine the value of outage probability for which two resource allocation tasks are same, that is, for which our numerical results illustrate the minimal need of relearning from the transferred DNN model. For other values of outage probability, there is a mismatch between the two tasks and our results illustrate a more efficient relearning of the transferred DNN model. Our results show that the learning dataset required for relearning of the transferred DNN model is significantly smaller than the required training dataset for a DNN model without transfer learning.

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

Pattern division multiple access (PDMA) is a multi- carrier non-orthogonal multiple access (NOMA), which can meet the requirements of massive user connections and super-high data rate in the fifth generation (5G) wireless networks. In this paper, we work on the optimization of power allocation to improve the performance in the down-link PDMA system with imperfect channel state information (CSI) at transmitter. The outage throughput of the system is maximized by optimizing the power allocation under the constraints of maximum transmits power, minimum user data rate, and outage probability. Since this optimization problem is a probabilistic mixing problem, we first turn it into a non-probability problem. Then, assuming the pattern matrix is known, we propose an iterative power allocation scheme. The closed-form expression of power allocation is derived based on Karush-Kuhn-Tucker (KKT) conditions. The simulation results demonstrate that the proposed iterative power allocation scheme yields better performance over the existing schemes.

The integration of non-orthogonal multiple access (NOMA) in vehicle-to-everything (V2X) communications has recently shown great potential to improve traffic efficiency, control, and reliability of beyond 5G transportation systems. In V2X communications, it is vital to inspect imperfect channel state information (CSI) because the high mobility of vehicles leads to more channel estimation uncertainties. This paper proposes an energy-efficient power allocation scheme for the road-side unit (RSU) assisted NOMA multicasting in beyond 5G cellular V2X networks. In particular, the energy efficiency maximization problem is investigated under the outage probability of vehicles under imperfect CSI, quality of services (QoS), and power limit constraints. Since the problem is non-convex and difficult to solve directly, we first convert outage probability constraint to non-probabilistic constraint through approximation and adopt a low complexity gradient assisted binary search (GABS) method to obtain the efficient power allocation at RSUs. Then, a successive convex approximation (SCA) technique is exploited to transform the power allocation problem of vehicles associated with each RSU into a tractable concave-convex fractional programming (CCFP) problem. The optimal solution to the CCFP problem is achieved through Dinkelbach and the dual decomposition method. The global optimal power allocation through the GABS-Exhaustive scheme act as a benchmark, which has considerable computational complexity. Simulation results unveil that the proposed suboptimal scheme (GABS-Dinkelbach) can achieve near-optimal performance with very low complexity.

SummaryThe simultaneous wireless information and power transfer or energy harvesting (EH) can be combined in nonorthogonal multiple access (NOMA) as green applications towards 5G. This paper investigates a new cooperative EH‐NOMA protocol, where the intermediate relay has not equipped the fixed power source and acts as a wireless powered relay to help signal transmission to representative weak user and strong user in NOMA. However, impacts of imperfect channel estimation contribute to outage system performance evaluations. We formulate the power resource assignment paradigms as two schemes, namely, fixed power allocation and dynamic power allocation, by considering imperfect channel state information (CSI). To solve this problem, we derive the closed‐form expressions of outage probability under imperfect CSI and the power allocation constraints. The expected numerical results related to the derived expressions for the outage probability are examined that numerical and the Monte Carlo simulations are strictly matching lines.

Joint Beamforming and power allocation schemes are considered for cognitive radio (CR) multiple input single output (MISO) system in this paper. We considered the robust downlink beamforming and power allocation problem with per-antenna power constraints which is more realistic in practice, instead of sum-power constraint, under the imperfect channel state information (CSI). Since the given optimization problem is a non-convex problem which is difficult to handle, we transformed it into second order cone program (SOCP) problem, which can be solved efficiently. Simulation shows that the proposed scheme indeed has a better performance in CR system.

Many papers have studied the joint optimal transceiver design based on the assumption that channel state information (CSI) is perfect at both sides of transceiver, the same imperfect CSI is available at both ends or that transmitter has knowledge of channel distribution. To our knowledge, little effective work has been done on the practical situation that transmitter only knows the imperfect CSI different from the imperfect CSI at the receiver. So, we proposed a simple practical joint transceiver scheme for such CSI mismatch in this paper and applied it to three popular joint transceiver designs to analyze its performance. Simulation results show that the proposed scheme outperforms the conventional scheme at different channel estimation errors and vector quantization errors.

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. Compared with the system using perfect channel state information (CSI), we analyze the achievable rate loss due to CSI mismatch arising from both channel estimation and CSI quantization feedback. An upper bound to the rate loss is firstly derived to characterize the effect of imperfect CSI. Given the rate loss bound, we then present a limited feedback strategy for both two-hop channels in the relay system to guarantee a bounded rate loss for growing SNRs. This newly presented strategy reveals the relationship between CSI quantization level and other system parameters, including the transmit power and the pilots for channel estimation, on the entire two-hop system. Finally, computer simulations are carried out for verifying the correctness of our derived results.

Since Maddah-Ali and Tse showed that the completely stale transmitter-side channel state information (CSIT) still benefits the Degrees of Freedom (DoF) of the Multiple-Input-Multiple-Output (MISO) Broadcast Channel (BC), there has been much interest in the academic literature to investigate the impact of imperfect CSIT on DoF region of time correlated broadcast channel. Even though the research focus has been on time correlated channels so far, a similar but different problem concerns the frequency correlated channels. Indeed, the imperfect CSIT also impacts the DoF region of frequency correlated channels, as exemplified by current multi-carrier wireless systems. This contribution, for the first time in the literature, investigates a general frequency correlated setting where a two-antenna transmitter has imperfect knowledge of CSI of two single-antenna users on two adjacent subbands. A new scheme is derived as an integration of Zero-Forcing Beamforming (ZFBF) and the scheme proposed by Maddah-Ali and Tse. The achievable DoF region resulted by this scheme is expressed as a function of the qualities of CSIT.