Optimum Power Allocation for Distributed Antenna Systems with Large-scale Fading-only Feedback

In this paper, an expression for the user's achievable data rate in the multi-user multiple-input multiple-output (MU-MIMO) system with limited feedback (LF) of channel state information (CSI) is derived. The energy efficiency (EE) is optimized through power allocation under quality of service (QoS) constraints. Based on mathematical equivalence and Lagrange multiplier approach, an energy-efficient unequal power allocation (EEUPA) with LF of CSI scheme is proposed. The simulation results show that as the number of transmitting antennas increases, the EE also increases which is promising for the next generation wireless communication networks. Moreover, it can be seen that the QoS requirement has an effect on the EE of the system. Ultimately, the proposed EEUPA with LF of CSI algorithm performs better than the existing energy-efficient equal power allocation (EEEPA) with LF of CSI schemes.

This study proposes an integrated framework of single-selection distributed antenna systems (DAS) and simultaneous wireless information and power transfer (SWIPT) with orthogonal multiple access (OMA), and analyzes the synergetic effects of SWIPT and DAS. To demonstrate the performance gains of the proposed SWIPT-enabled OMA with DAS, we jointly optimize power allocation for the OMA and power splitting (PS) ratio for energy harvesting of SWIPT with three different optimization goals: sum-rate maximization, user fairness, and energy efficiency. Although all of the above optimization problems are non-convex, the closed-form solutions of the problems maximizing the sum-rate and fairness are presented. Owing to difficult analytical tractability of the problem maximizing the energy efficiency, the problem is divided into three subproblems: power allocation for the base station (BS), power allocation for radio remote unit (RRU), and PS ratio assignment. Then, an iterative algorithm is presented to provide the energy-efficient SWIPT-enabled OMA with DAS. Theoretical findings are validated by numerical results, which show that the framework of applying DAS to SWIPT-OMA improves data rates and energy efficiency compared to SWIPT-OMA without DAS, while satisfying the minimum rate and harvested energy requirements.

For reentry communication, the constellation rotation caused by high-dynamic plasma sheaths leads to unacceptable demodulation performances degradation for the quadrature amplitude modulation (QAM) signal. In this article, a noncoherent downlink $2\times M$ MIMO system over hypersonic vehicle integrated channel cascading the plasma sheath channel and the Rice fading channel is introduced, where each of the two transmitters has an antenna and a ground base station is equipped with a large number of antennas. For this system, a binary orthogonal-division dual-carrier modulation (BODM) method is proposed and an efficient noncoherent maximum likelihood (ML) receiver is developed without knowing the instantaneous channel state information (CSI). If the channel parameters are available to transmitters, the optimal power allocation can be achieved by using the minimum Euclidean distance (MED) criterion. A brief discussion on how to extend these results to the multiple-carrier system is presented. Simulations demonstrate that the new system significantly reduces the average bit error rate (BER), the optimal power allocation scheme is far superior to an equal power allocation scheme, and the system is robust against the plasma sheath attenuation with moderate received antennas or large line-of-sight (LOS) components.

In this paper, the ergodic sum rate and the outage probability of a downlink single-antenna channel with K users are analyzed in the presence of Rayleigh flat fading, where limited channel state information (CSI) feedback is assumed. Specifically, only 1-bit feedback per fading block per user is available at the base station. We first study the ergodic sum rate of the 1-bit feedback scheme and consider the impact of feedback delay on the system. A closed-form expression for the achievable ergodic sum rate is presented as a function of the fading temporal correlation coefficient. It is proved that the sum rate scales as log log K, which is the same scaling law achieved by the optimal nondelayed full CSI feedback scheme. The sum-rate degradation due to outdated CSI is also evaluated in the asymptotic regimes of either large K or low SNR. The outage performance of the 1-bit feedback scheme for both instantaneous and outdated feedback is then investigated. Expressions for the outage probabilities are derived, along with the corresponding diversity-multiplexing tradeoffs (DMTs). It is shown that, with instantaneous feedback, a power allocation based on the feedback bits enables doubling of the DMT compared with the case with short-term power constraint in which a dynamic power allocation is not allowed. However, with outdated feedback, the advantage of power allocation is lost, and the DMT reverts to that achievable with no CSI feedback. Nevertheless, for finite SNR, improvement in terms of outage probability can still be obtained.

It is well known that traditional water-filling provides a closed form solution for capacity maximization in orthogonal frequency division multiplex (OFDM) system. In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver model for OFDM systems. The local full channel state information (CSI) is available at the two transmitters respectively, where each transmitter has an individual power constrain. The transmitters first cooperate by sharing CSI, and then jointly optimize power allocation in the metric of sum throughput, which can be modeled as a convex optimization problem. Through an application of Karush-KuhnTucker (KKT) conditions, the convex optimization problem is reformulated as a simplified convex one. Then the closed form solution is derived, which takes a form similar to classic waterfilling principle. Based on the solution, the optimal cooperative power allocation algorithm is constructed, the structure of which can be explained as a cooperative water-filling relative to the traditional water-filling. Finally, numerical simulation is given to evaluate and demonstrate the performance of the optimal cooperative water-filling scheme.

Spectral efficiency and energy efficiency of distributed antenna systems with virtual cells

This paper presents an optimal energy-efficient power allocation scheme for a point-to-point multicarrier link over frequency-selective fading channel subject to a delay-outage probability constraint. For a target delay-outage limit, the energy efficiency (EE) objective function is formulated as the ratio of the achieved link effective capacity to the total expenditure power, expressed in units of b/J/Hz. We first prove that this objective function is quasi-concave in the transmission power, and, hence, the global maximum solution of the underlying optimization problem can be obtained using fractional programming. Subsequently, we develop a two-step optimal power allocation algorithm by first obtaining the average sum power level corresponding to the maximum achievable EE, followed by jointly distributing this obtained average power over time and frequency. Analytical results show that the EE-based power allocation has a structure similar to that of the QoS-driven spectral-efficient scheme, but with a different cut-off threshold below which no transmission power is allocated. Simulation results show that the proposed joint optimal power allocation scheme provides significant EE gains over the simple independent subcarrier optimization scheme, where these performance advantages become more pronounced with tighter delay constraints and in fading channels with more severe frequency selectivity.

Optimal power allocation in dense wireless networks is essential for managing both inter-cell and intra-cell interferences to maximize the network's total rate. Reinforcement Learning (RL) has been used in power allocation problem for maximizing capacity, but in this work, we have considered downlink power allocation for each user using only its own Channel State Information (CSI). Prior studies used CSI form neighbor cells, and it causes overhead in signal and makes delay in the network. However, in a real scenario, it is not always possible to get CSI from neighbor cells. Assuming that the RL agent only has access to its CSI, the problem becomes partially observable. Thus RL exploration becomes very difficult, and traditional RL methods do not have proper performance. To solve this problem, we have used a Bayesian RL method, Bayesby-Backprop Q-network (BBQN), for power allocation without needing CSI from neighbor cells. In this way, the Bayesian Neural Network (BNN) is used to obtain a posterior distribution over action-space value functions, and actions with higher uncertainty are explored more. Simulation results demonstrate the convergence and robustness of the BBQN, while traditional schemes fail to find an acceptable power assignment.

To improve the bandwidth efficiency, a semi-blind channel estimation method using data nulling superimposed pilots (DNSP) is designed for orthogonal frequency division multiplexing (OFDM) system without data rate loss. However, the data distortion is introduced by rank-deficient precoding. To minimize the data distortion, the optimal precoder is found to minimize the Euclidean distance between precoded sequence and source sequence. Further, the impact of power allocation between training sequence and data information on effective signal noise ratio (SNR) is analytically derived, effective SNR is found to be a quadratic function for the percentage allocated for training sequence, and the optimal power allocation is provided to maximize the effective SNR. Simulation results show that our optimal precoder design and power allocation scheme obtain the optimal performance of symbol detection.

In this letter, energy-efficient transmission and power allocation for fixed-gain amplify-and-forward relay networks with partial channel state information (CSI) are studied. In the energy-efficiency problem, the total power consumed is minimized while keeping the signal-to-noise-ratio (SNR) above a certain threshold. In the dual problem of power allocation, the end-to-end SNR is maximized under individual and global power constraints. Closed-form expressions for the optimal source and relay powers and the Lagrangian multiplier are obtained. Numerical results show that the optimal power allocation with partial CSI provides comparable performance as optimal power allocation with full CSI at low SNR.

In spatial modulation (SM) systems, when channel state information is available at the transmitter, adaptive power allocation can be exploited to enhance the SM performance. In this paper, optimal power allocation in SM systems is considered from an information-theoretical view. First, as there is no closed-form expression of the SM capacity, the instantaneous mutual information is formulated in an analytic form for SM systems with two transmit antennas. Then, two power-allocation algorithms are proposed to maximize the SM mutual information. One is a sub-optimal power-allocation algorithm that is established in closed form to maximize the upper bound of SM mutual information. The other is an iterative algorithm for optimal power allocation that is proposed to achieve the maximum mutual information of SM systems while dramatically reducing the operational complexity compared with global computer searching. To evaluate the performance of the proposed power-allocation algorithms, numerical and simulation results are reported in terms of outage capacity and ergodic capacity, which demonstrates that the proposed power-allocation algorithms significantly improve the SM performance, compared with conventional equal-power allocation.

This paper considers the wiretap channel in which a source node communicates with a destination node with the aid of a single helper and in the presence of an eavesdropper. The helper can apply either amplify-and-forward (AF) relaying or jamming. The secrecy capacity and respective optimal power allocation schemes at the source and the helper are investigated under both individual and joint power constraints. Given that the links among the nodes might not always be available for transmission (e.g., due to heavy shadowing), several network topologies are considered for relaying. To deal with the related nonconcave optimization problems in solving the optimal power allocation schemes, we first propose an analytical approach based on Descartes' rule of signs. Using this approach, optimal power allocation strategies are derived for some relay topologies and for the jamming system. Insights into the proposed schemes for different transmission power ranges and comparisons to the optimal information rate power allocations are also discussed. Simulation results are finally provided to quantify the optimality of the proposed schemes.

This chapter discusses fundamentals of information theory and channel coding (error correction coding ECC, Forward error correction FEC). First, key results of Shannon, including channel capacity and the relationship between transmit power and bandwidth, are discussed. Block codes multiply a block of data with an encoding matrix. For systematic codes, the resulting codeword consists of systematic bits and parity check bits. Decoding is achieved by multiplication with a parity check matrix and determination of the syndrome vector. Correction of errors within the correction sphere can be done, e.g., through lookup tables. Convolutional codes send the source data stream through shift registers and adders, a process described by a trellis diagram. Maximum-likelihood sequence estimation (Viterbi algorithm) determines the (transmit) bit sequence with best distance metric (Hamming or Euclidean) from the received signal. Methods for combining coding and higher-order modulation include trellis coded modulation (using set partitioning for code design) and bit-interleaved coded modulation (BICM). Turbo codes are close to capacity-achieving. They are based on transmitting differently interleaved convolutional codes; receivers use soft decoding of the constituent codes and iterative exchange of soft information (log-likelihood ratios). Low-density parity check codes (LDPCC) are decoded by belief propagation (message passing), where variable nodes and constraint nodes exchange beliefs about the codeword. In fading channels, (block or convolutional) interleaving is essential to provide diversity. From an information-theoretic point of view, in fading channels we can distinguish between the ergodic capacity and the outage capacity. If the channel state information is known at the transmitter, waterfilling is the optimum power allocation strategy. Automatic Repeat Request ARQ) and Hybrid ARQ (including chase combining or incremental redundancy) are described in the appendix.

This paper investigates the problem of adaptive power allocation for distributed best linear unbiased estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless sensor network (WSN). An optimal power-allocation scheme is proposed that minimizes the $L^2$-norm of the vector of local transmit powers, given a maximum variance for the BLUE estimator. This scheme results in the increased lifetime of the WSN compared to similar approaches that are based on the minimization of the sum of the local transmit powers. The limitation of the proposed optimal power-allocation scheme is that it requires the feedback of the instantaneous channel state information (CSI) from the FC to local sensors, which is not practical in most applications of large-scale WSNs. In this paper, a limited-feedback strategy is proposed that eliminates this requirement by designing an optimal codebook for the FC using the generalized Lloyd algorithm with modified distortion metrics. Each sensor amplifies its analog noisy observation using a quantized version of its optimal amplification gain, which is received by the FC and used to estimate the unknown parameter.

Multi-hop cooperative MIMO network that consists of multiple nodes each equipped with single antenna is introduced and investigated. Optimal power allocation scheme with selective decode-and-forward (SDF) cooperative strategy is formulated and solved by means of optimization method with the purpose of minimization of system’s symbol error rate (SER), which are derived and analyzed with numerical results presented. Distributed space-time block Code (DSTBC) in multi-hop cooperative MIMO is analyzed and compared with optimal power allocation scheme with SDF strategy. Simulation results reveal that, cooperative diversity could be achieved with optimal power allocation. Optimum power allocation scheme depends on the relay’s position and channel quality between source to relay and relay to destination. Novel performance comparison between optimal power allocation and DSTBC are researched and presented. Simulation results confirm our theoretical analysis, which indicates that multi-node DSTBC outperforms optimal power allocation scheme due to the fact that cooperative diversity gain and coded gain could be obtained simultaneously for DSTBC in multi-hop cooperative MIMO network.