In this paper, we consider a large scale multiple input multiple output (LS-MIMO) relaying system, where an information source sends the message to its intended destination aided by an LS-MIMO relay, while a passive eavesdropper tries to intercept the information forwarded by the relay. The advantage of a large scale antenna array is exploited to improve spectral efficiency and enhance wireless security. In particular, the challenging issue incurred by short-distance interception is well addressed. Under very practical assumptions, i.e., no eavesdropper channel state information (CSI) and imperfect legitimate CSI at the relay, this paper gives a thorough secrecy performance analysis and comparison of two classic relaying techniques, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, asymptotical analysis is carried out to provide clear insights on the secrecy performance for such an LS-MIMO relaying system. We show that under large transmit powers, AF is a better choice than DF from the perspectives of both secrecy performance and implementation complexity, and prove that there exits an optimal transmit power at medium regime that maximizes the secrecy outage capacity.
This paper reviews emerging wireless information and power transfer (WIPT) technique with an emphasis on its performance enhancement employing multi-antenna techniques. Compared to traditional wireless information transmission, WIPT faces numerous challenges. First, it is more susceptible to channel fading and path loss, resulting in a much shorter power transfer distance. Second, it gives rise to the issue on how to balance spectral efficiency for information transmission and energy efficiency for power transfer in order to obtain an optimal tradeoff. Third, there exists a security issue for information transmission in order to improve power transfer efficiency. In this context, multi-antenna techniques, e.g., energy beamforming, are introduced to solve these problems by exploiting spatial degree of freedom.This article provides a tutorial on various aspects of multi-antenna based WIPT techniques, with a focus on tackling the challenges by parameter optimization and protocol design. In particular, we investigate the WIPT tradeoffs based on two typical multi-antenna techniques, namely limited feedback multiantenna technique for short-distance transfer and large-scale multiple-input multiple-output (LS-MIMO, also known as massive MIMO) technique for long-distance transfer. Finally, simulation results validate the effectiveness of the proposed schemes. Index TermsXiaoming Chen (e-mail: chenxiaoming@nuaa.edu.cn) is with the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, China. Zhaoyang Zhang (e-mail: ning_ming@zju.edu.cn) and Huazi Zhang (e-mail: tom.zju@gmail.com)
In successive cancellation (SC) polar decoding, an incorrect estimate of any prior unfrozen bit may bring about severe error propagation in the following decoding, thus it is desirable to find out and correct an error as early as possible. In this paper, we first construct a critical set S of unfrozen bits, which with high probability (typically > 99%) includes the bit where the first error happens. Then we develop a progressive multi-level bit-flipping decoding algorithm to correct multiple errors over the multiple-layer critical sets each of which is constructed using the remaining undecoded subtree associated with the previous layer. The level in fact indicates the number of independent errors that could be corrected. We show that as the level increases, the block error rate (BLER) performance of the proposed progressive bit flipping decoder competes with the corresponding cyclic redundancy check (CRC) aided successive cancellation list (CA-SCL) decoder, e.g., a level 4 progressive bitflipping decoder is comparable to the CA-SCL decoder with a list size of L = 32. Furthermore, the average complexity of the proposed algorithm is much lower than that of a SCL decoder (and is similar to that of SC decoding) at medium to high signal to noise ratio (SNR).
With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.
In this paper, we investigate an artificialintelligence (AI) driven approach to design error correction codes (ECC). Classic error-correction code design based upon coding-theoretic principles typically strives to optimize some performance-related code property such as minimum Hamming distance, decoding threshold, or subchannel reliability ordering. In contrast, AI-driven approaches, such as reinforcement learning (RL) and genetic algorithms, rely primarily on optimization methods to learn the parameters of an optimal code within a certain code family. We employ a constructor-evaluator framework, in which the code constructor can be realized by various AI algorithms and the code evaluator provides code performance metric measurements. The code constructor keeps improving the code construction to maximize code performance that is evaluated by the code evaluator. As examples, we focus on RL and genetic algorithms to construct linear block codes and polar codes. The results show that comparable code performance can be achieved with respect to the existing codes. It is noteworthy that our method can provide superior performances to classic constructions in certain cases (e.g., list decoding for polar codes). Code PerformanceCoding Theory Code Construction AI Techniques Fig. 1: Error correction code design logic improve its code performance. Equivalently, given a target error rate, we optimize code design to maximize the achievable code rate, i.e. to approach the channel capacity. A. Code design based on coding theoryClassical code construction design is built upon coding theory, in which code performance is analytically derived in terms of various types of code properties. To tune these properties is to control the code performance so that code design problems are translated into code property optimization problems.Hamming distance is an important code property for linear block codes of all lengths. For short codes, it is the dominant factor in performance, when maximum-likelihood (ML) decoding is feasible. For long codes, it is also important for performance in the high signal-to-noise ratio (SNR) regime. A linear block code can be defined by a generator matrix G or the corresponding parity check matrix H over finite fields. Directed by the knowledge of finite field algebra, the distance profile of linear block codes can be optimized, and in particular, the minimum distance. Examples include Hamming codes, Golay codes, Reed-Muller (RM) codes, quadratic residue (QR) codes, Bose-Chaudhuri-Hocquenghem (BCH) codes, Reed-Solomon (RS) codes, etc.Similar to the Hamming distance profile, free distance, another code property, is targeted for convolutional codes. Convolutional codes [2] are characterized by code rate and the memory order of the encoder. By increasing the memory order and selecting proper polynomials, larger free distance can be obtained at the expense of encoding and decoding
In this paper, we propose a comprehensive Polar coding solution that integrates reliability calculation, rate matching and parity-check coding. Judging a channel coding design from the industry's viewpoint, there are two primary concerns: (i) low-complexity implementation in applicationspecific integrated circuit (ASIC), and (ii) superior & stable performance under a wide range of code lengths and rates. The former provides cost-& power-efficiency which are vital to any commercial system; the latter ensures flexible and robust services. Our design respects both criteria. It demonstrates better performance than existing schemes in literature, but requires only a fraction of implementation cost. With easilyreproducible code construction for arbitrary code rates and lengths, we are able to report "1-bit" fine-granularity simulation results for thousands of cases. The released results can serve as a baseline for future optimization of Polar codes. 1
In this paper, joint energy-efficient resource allocation for both the base station and users is studied for time division duplex (TDD) systems with carrier aggregation (CA). We aim at balancing the energy efficiency (EE) between downlink and uplink, as well as the EEs among individual users, by joint bandwidth and power allocation on each carrier component (CC). We formulate the optimization problem into maximizing the weighted summation of EEs for the base station and different users, where the weights are used to reflect the levels of importance. The objective function of the problem is a sum of several fractional functions, therefore, nonlinear sum-ofratios programming needs to be used to solve it, which has not been exploited in resource allocation problems yet. Specifically, a novel transformation is performed to formulate an equivalent but better tractable problem, based on which we develop an iterative algorithm to find the global optimum of the considered problem. Numerical results validate the feasibility, fast convergence, and flexibility of the proposed algorithm in terms of EE balancing.
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