For polar codes, perturbing received information can enhance the successive cancellation (SC) decoding performance. This is an effective approach for realizing low-latency yet high performance for long polar codes, since all the perturbation-enhanced SC (PSC) decoding can be performed in parallel. This paper provides theoretical insights into soft information perturbation, revealing that the PSC decoding can be equivalently interpreted as perturbing thea posteriorilog-likelihood ratios (LLRs) of information bits. Such a revelation leads to the design of an improved PSC (IPSC) decoding that yields a lower perturbation complexity. By better utilizing the decodinga posterioriLLRs, a set of possibly erroneous estimations can be formed and further perturbed, resulting in the proposed hybrid PSC (HPSC) decoding. During each new SC decoding attempt, it takes turns to flip the first erroneous bit by introducing a biased perturbation, while the subsequent erroneous estimations are corrected through random perturbations. Our simulation results validate that, for various codeword lengths and rates, the proposed IPSC decoding can achieve a similar performance as the conventional PSC decoding, but yield a significantly reduced perturbation complexity. With the same number of decoding attempts, the proposed HPSC decoding outperforms several state-of-the-art SC-based decoding, such as the thresholded SC-flip (TSCF) decoding and the dynamic SC-flip (DSCF) decoding.

The weight spectrum plays a crucial role in the performance of error-correcting codes. Pre-transformation with an upper-triangular matrix improves the weight spectrum of polar codes while retaining polarization. However, a theoretical analysis to quantify the improvement for pre-transformed rate-compatible polar codes is missing. In this paper, we calculate the average spectrum of random upper-triangular pre-transformed shortened and punctured polar codes. Our approach tran-scends the limitations imposed by partial ordering and specific rate matching patterns. A key feature of our approach is its polynomial complexity in relation to the code length, making it computationally feasible. Simulation results affirm that our findings provide an accurate approximation on the performance of pre-transformed rate-compatible polar codes.

In this paper, we establish the partial orders (POs) of rate-compatible polar codes under both the binary erasure channel (BEC) and the binary memoryless symmetric channel (BMSC). Firstly, we define the POs for rate-compatible polar codes under block rate matching. Additionally, we demonstrate that certain POs for mother code lengths remain valid under rate matching in the BEC. Finally, leveraging the existing POs in the BEC, we derive POs in the BMSC under block rate matching.

For polar codes, the bit-flipping strategy can significantly improve performance of its successive cancellation (SC) decoding. However, the gain derived from SC-flip (SCF) decoding diminishes as the codeword length increases. Addressing this issue, this paper proposes a novel hybrid perturbation-based SC (HPSC) decoding. If the initial SC decoding fails, the algorithm will generate multiple SC decoding attempts, each of which introduces stochastic perturbations to the received symbols. By soft information perturbations, the SC decoding can divert from the initial erroneous estimation and converge to the intended one. Our simulation results show that the proposed HPSC decoding consistently yields stable coding gains over various codeword lengths and rates. With the same number of decoding attempts, the HPSC decoding outperforms the thresholded SCF (TSCF) decoding. Moreover, it can achieve a similar performance as the cyclic redundancy check (CRC) aided SC list (CA-SCL) decoding, without any path sorting and expansion requirements.

The weight spectrum plays a crucial role in the performance of error-correcting codes. Despite substantial theoretical exploration into polar codes with mother code length, a framework for the weight spectrum of rate-compatible polar codes remains elusive. In this paper, we address this gap by enumerating the number of minimum-weight codewords for quasi-uniform punctured, Wang-Liu shortened, and bit-reversal shortened decreasing polar codes. Notably, our algorithms operate with polynomial complexity relative to the code length. Simulation results affirm that our discoveries provide an accurate approximation on the performance of rate-compatible polar codes.

U-UV codes are good-performing short-to-medium length channel codes constructed by several algebraic component codes. They are coupled through the (U|U+V) recursive structure. With eBCH codes as the component codes, U-UV codes can be interpreted as the generalized concatenated codes (GCCs) with inner polar codes and outer eBCH codes. With ordered statistic decoding (OSD) for the outer codes, the successive cancellation list (SCL) decoding of U-UV codes can outperform that of the cyclic redundancy check (CRC)-polar codes. But the complexity of the OSD grows exponentially with its decoding order, rendering the worst-case decoding complexity of U-UV codes being too high. This paper proposes the SCL2decoding of eBCH based U-UV codes, in which both the inner codes and outer codes are decoded by the SCL decoding. In particular, the eBCH outer codes are interpreted as the concatenation of a polar code and a linear transform. Consequently, it can be decoded by SCL decoding with a sub-quadratic complexity. Our simulation results show that for eBCH based U-UV codes, SCL2decoding can reduce the binary operations required in the existing (outer) OSD-(inner) SCL decoding by an order of magnitude, while maintain the decoding performance.

For polar codes, perturbing received information can enhance the error-correction performance of successive cancellation (SC) decoding. This is an effective approach for realizing low-latency yet high decoding performance for long polar codes, since each perturbation-enhanced SC (PSC) decoding can be performed in parallel. This paper provides theoretical insights into the soft information perturbation. It first reveals that the PSC decoding can be equivalently viewed as perturbing the SC decoding a posteriori log-likelihood ratio (LLR) of the information bits. Such a revelation enables us to reduce the perturbation complexity by only targeting the information bits, resulting in an improved PSC (IPSC) decoding. By better utilizing the a posteriori LLRs, a set of possibly erroneous estimations can be formed to be perturbed, further reducing the perturbation complexity. Our simulation results show that, for various codeword lengths, the proposed IPSC decoding can achieve a similar performance as the PSC decoding, while yielding significant perturbation complexity reduction.

Ordered statistics decoding (OSD) can achieve near maximum likelihood (ML) decoding performance for BCH codes. However, Gaussian elimination (GE) that delivers the systematic generator matrix of the code has an uncompromised latency. Addressing this challenge, this paper proposes a low-latency OSD (LLOSD) for BCH codes. Since BCH codes are binary subcodes of Reed-Solomon (RS) codes, codeword candidates can be produced using the RS systematic generator matrix, whose entries can be generated in parallel. By eliminating the non-binary codeword candidates and identifying the ML codeword, the LLOSD yields a lower latency as well as complexity than the OSD. It is shown that the LLOSD can be interpreted as generating the codeoword candidates through systematic encoding of a punctured BCH codeword, explaining its low-complexity feature. Moreover, the segmented variant is proposed to further facilitate the LLOSD. In order to decode long BCH codes, a hybrid soft decoding (HSD) is finally proposed. It integrates the LLOSD and the algebraic Chase decoding that can effectively provide extra TEPs for the LLOSD, enhancing the decoding performance. Both the complexity and performance of the proposed decoding are analyzed, demonstrating their advantage over the relevant state-of-the-art decoding.

In this paper, we study the lossless analog compression fori.i.d.discrete-continuous mixed signals via the polarization-based framework. We prove that for discrete-continuous mixed source, the error probability of maximum a posteriori (MAP) estimation polarizes under the Hadamard transform, which extends the polarization phenomenon to analog domain. Building on this insight, we propose the partial Hadamard compression and develop the corresponding analog successive cancellation (SC) decoder. The proposed scheme consists of deterministic measurement matrices and non-iterative reconstruction algorithm, providing benefits in both space and computational complexity. Using the polarization of error probability, we prove that our approach achieves the information-theoretical limit for lossless analog compression developed by Wu and Verdú.

In this paper, we establish the second-order randomized identification capacity (RID capacity) of the Additive White Gaussian Noise Channel (AWGNC). On the one hand, we obtain a refined version of Hayashi's theorem to prove the achievability part. On the other, we investigate the relationship between identification and channel resolvability, then we propose a finer quantization method to prove the converse part. Consequently, the second-order RID capacity of the AWGNC has the same form as the second-order transmission capacity. The only difference is that the maximum number of messages in RID scales double exponentially in the block length.

In this paper, we define partial orders (POs) of polar codes based on the Bhattacharyya parameter and the bit-error probability, respectively. These POs are applicable to arbitrary binary memoryless symmetric channel (BMSC). Leveraging the extremal inequalities of polarization transformation, we derive new POs for BMSC based on the corresponding POs observed in the Binary Erasure Channel (BEC). We provide examples that demonstrate the inability of existing POs to deduce these novel POs. Furthermore, we establish upper bounds for the expansion parameter$\beta$if the polar codes constructed by$\beta- \mathbf{expansion}$method obey these POs.

This paper proposes a reduced complexity ordered statistics decoding (OSD) algorithm for linear block codes, the namely order skipping (OS)-OSD algorithm. An approximated correlation distance lower bound (CDLB) is derived by utilizing likelihood of the received symbols over the least reliable positions (LRPs). It enables the assessment of whether the higher-order decoding can yield a more likely codeword estimation. If not, they can be skipped. Error-correction performance of the OSOSD is analyzed. In particular, the decoding error probability of OS-OSD with order one is theoretically characterized. Our simulation results verify that the OS-OSD can achieve a significant complexity reduction over the state-of-the-art OSD without compromising the decoding performance.

Elementary trapping sets (ETSs) are the principal culprits for the performance of LDPC codes in the error floor region. Due to their large quantity, intricate structures, and high computational complexity, determining how to eliminate dominant ETSs in the design of LDPC codes has become a critical issue in improving error floor behavior. In this paper, we address this problem by avoiding particular theta graphs$(\theta(1,2,2)$and$\theta(2,2,2))$in the Tanner graph to eliminate specific ETSs. These can be characterized by a pivotal tool in graph theory - Turán numbers. Theoretically, we derive the exact Turán number for$\theta(1,2,2)$and demonstrate that all$(a, b)$-ETSs in a Tanner graph with variable-reaular degree$d_{L}(v)=\gamma$must satisfy the inequality$b\geq a\gamma-\frac{1}{2}a^{2}$. This result improves the lower bound previously obtained by Amirzade when the girth is 6. For girth 8, by constraining the relationship between any two 8-cycles in the Tanner graph, we establish a similar inequality$b\geq a\gamma-\frac{a(\sqrt{8a-7}-1)}{2}$. Our simulation results indicate that codes designed with these considerations exhibit improved performance and a lower error floor over additive white Gaussian noise channels.

Due to the high parallelism of belief propagation (BP) decoding, it is considered as a promising solution for the decoding latency challenge of long polar codes. However, the error-correction performance of the classical BP decoding is inferior to that of the successive cancellation (SC) and the SC list (SCL) decoding. In this paper, an adaptive BP (ABP) decoding algorithm is proposed to bridge this performance discrepancy. It iteratively adjusts the a priori log-likelihood ratios (LLRs) of error-prone bits, which can be efficiently detected using the frozen and information processing elements (FIPEs). Moreover, a novel low-complexity FIPE-based early termination criterion (ETC) is proposed to further reduce the decoding complexity. It functions when all the frozen bits in the FIPEs are successfully decoded with stable LLR magnitudes. Our numerical results show that for the (1024,512) polar code, the ABP decoding outperforms the classical BP decoding by 0.3 dB at the frame error rate (FER) of 10–4over the additive white Gaussian noise (AWGN) channel. It can also achieve up to 78.5% latency reduction over the fast simplified SC (FSSC) decoding, while maintaining the same performance. The proposed ETC also exhibits a lower hardware complexity over the existing G-matrix criterion.

The number of low-weight codewords is critical to the performance of error-correcting codes. In 1970, Kasami and Tokura characterized the codewords of Reed-Muller (RM) codes whose weights are less than 2wmin, wherewminrepresents the minimum weight. In this paper, we extend their results to decreasing polar codes. We present the closed-form expressions for the number of codewords in decreasing polar codes with weights less than 2wmin. Moreover, the proposed enumeration algorithm runs in polynomial time with respect to the code length.

The automorphism ensemble (AE) decoding framework for polar codes attracts much attention recently. It decodes multiple permuted codewords with successive cancellation (SC) decoders in parallel and hence has lower latency compared to successive cancellation list (SCL) decoding. However, the AE decoding framework is ineffective for permutations falling into the lower-triangular affine (LTA) automorphism group, as they are invariant under SC decoding. Therefore, the block lower-triangular affine (BLTA) group was discovered to achieve better AE decoding performance. However, the equivalence of the BLTA group and the complete affine automorphism group was unresolved. Additionally, some automorphisms in BLTA group are also SC-invariant, thus are redundant in AE decoding. In this paper, we prove that BLTA group coincides with the complete automorphisms of decreasing polar codes that can be formulated as affine transformations. Also, we find a necessary and sufficient condition related to the block lower-triangular structure of transformation matrices to identify SC-invariant automorphisms. Furthermore, We present an algorithm that efficiently identifies all SC-invariant affine automorphisms under specific constructions.

In this paper, we study the lossless analog compression for i.i.d. nonsingular signals. Through analyzing analog polarization under Hadamard transform, we propose efficient successive cancellation (SC) decoding algorithm over analog domain. Thanks to the polarization of Rényi information dimension (RID) and the absorption of discrete entropy, we prove that the proposed scheme achieves the information-theoretical limit for lossless analog compression developed by Wu and Verdú.

Polarization-adjusted convolutional (PAC) codes can approach the normal approximation (NA) bound using Fano decoding. However, when the received information is unreliable, the decoding may linger over the decoding tree, resulting in both a high decoding complexity and latency. This paper proposes the modified PAC (MPAC) codes and their hybrid Fano-SC (HFSC) decoding that prevents an impractical decoding. For the MPAC codes, only a subset of the information bits undergo the convolutional transform. Its output then concatenates the remaining information bits for the inner polar transform. Consequently, the Fano decoding and the successive cancellation (SC) decoding are deployed to recover the information bits that have undergone the convolutional transform and the remaining information bits, respectively. Both the MPAC code design and the HFSC decoding insight are studied. Our simulation results show that with a limited complexity, HFSC decoding of MPAC codes can yield a better performance-complexity tradeoff than Fano decoding of PAC codes and SC list (SCL) decoding of cyclic redundancy check (CRC)-polar codes.

Pre-transformation with an upper-triangular matrix (including cyclic redundancy check (CRC), parity-check (PC) and polarization-adjusted convolutional (PAC) codes) improves the weight spectrum of Reed-Muller (RM) codes and polar codes significantly. However, a theoretical analysis to quantify the improvement is missing. In this paper, we provide asymptotic analysis on the number of low-weight codewords of the original and pre-transformed RM codes respectively, and prove that pre-transformation significantly reduces low-weight codewords, even in the order sense. For polar codes, we prove that the average number of minimum-weight codewords does not increase after pre-transformation. Both results confirm the advantages of pre-transformation.

Targeting high-throughput and low-power communications, we implement two successive cancellation (SC) decoders for polar codes. Converted to 16nm ASIC technology, the area efficiency and energy efficiency are 4Tbps/mm2and 0.63pJ/bit, respectively, for the unrolled decoder, and 561Gbps/mm2and 1.21pJ/bit, respectively, for the recursive decoder. To achieve such a high throughput, a novel code construction, coined as fast polar codes, is proposed and jointly optimized with a highly-parallel SC decoding architecture. First, we reuse existing modules to fast decode more outer code blocks, and then modify code construction to facilitate faster decoding for all outer code blocks up to a degree of parallelism of 16. Furthermore, parallel comparison circuits and bit quantization schemes are customized for hardware implementation. Collectively, they contribute to an 2.66× area efficiency improvement and 33% energy saving over the state of the art.

The rapid development of Internet of Things (IoT) requires new massive random access protocols to support the massive devices. Recently, Polyanskiy [1] established an information-theoretic formulation of unsourced multiple access (uMAC) problem and proposed theoretical finite-length achievability and converse bounds. In this paper, we further study the tradeoff between the number of active users and the energy-per-bit of random Gaussian codebook under maximum likelihood decoding and use two methods to improve the finite-length achievability bounds when the number of users is large and small, respectively. Our new results improve the finite-length achievability bound by more than 0.15 dB when per-user probability of error (PUPE) is 10−1, and more than 0.25 dB when PUPE is 10−3.

U-UV codes were recently proposed as a competent short-to-medium length coding scheme. With well designed component codes, U-UV codes can outperform similar rate cyclic redundancy check (CRC)-polar codes with the successive cancellation (SC) and the SC list (SCL) decoding. In order to improve the coded transmission spectral efficiency, this paper proposes the bit-interleaved coded modulation (BICM) scheme with U-UV codes as the channel codes. A bit interleaver structure is proposed to facilitate the component code rate allocation based on the polarized subchannel capacities. Under the BICM paradigm, the component code rates can be allocated by first estimating the modulation subchannel capacities, then adjusting based on the finite length rates and the equal error probability rule. Theoretical performance bounds and their approximations on decoding error rates are further analyzed. It provides the theoretical benchmarks for our simulations and guides the optimized design of the coded modulation scheme. Finally, simulation results of the U-UV coded BICM scheme are provided to demonstrate its error-correction competency. It can outperform the relevant bit-interleaved polar coded modulation (BIPCM) scheme.

In this paper, we design a polar decoding platform for diverse application scenarios that require low-cost and low-power communications. Specifically, prevalent polar decoders such as successive cancellation (SC), SC-list (SCL) and Fano decoders are all supported under the same architecture. Unlike high-throughput or low-latency decoders that promote parallelism, this architecture promotes serialization by repeatedly calling a “sub-process” that is executed by a core module. Surprisingly, the resulting serial SCL-8 decoder is only 3 times the size of an SC decoder, much smaller than the predicted 8 times. Cost and power are minimized through resource sharing and adaptive decoding techniques, etc. We carried out performance simulation and hardware implementation to evaluate the actual chip area and energy consumption.

Automorphism ensemble (AE) decoding for polar codes was proposed by decoding permuted codewords with successive cancellation (SC) decoders in parallel and hence has lower latency compared to that of successive cancellation list (SCL) decoding. However, some automorphisms are SC-invariant, thus are redundant in AE decoding. In this paper, we find a necessary and sufficient condition related to the block lower-triangular structure of transformation matrices to identify SC-invariant automorphisms. Furthermore, we provide an algorithm to determine the complete SC-invariant affine automorphisms under a specific polar code construction.

Identification capacities of randomized and deterministic identification were proved to exceed channel capacity for Gaussian channels with channel side information (CSI). In this work, we extend deterministic identification to the block fading channels without CSI by applying identification codes for both channel estimation and user identification. We prove that identification capacity is asymptotically higher than transmission capacity even in the absence of CSI. And we also analyze the finite-length performance theoretically and numerically. The simulation results verify the feasibility of the proposed blind deterministic identification in finite blocklength regime.

Recently, a permutation-based successive cancellation (PSC) decoding framework for polar codes attracts much attention. It decodes several permuted codewords with indepen-dent successive cancellation (SC) decoders. Its latency thus can be reduced to that of SC decoding. However, the PSC framework is ineffective for permutations falling into the lower-triangular affine (LTA) automorphism group, as they are invariant under SC decoding. As such, a larger block lower-triangular affine (BLTA) group that contains SC-variant permutations was discovered for decreasing polar codes. But it was unknown whether BLTA equals the complete automorphism group. In this paper, we prove that BLTA equals the complete automorphisms of decreasing polar codes that can be formulated as affine transformations.

Polar codes are the first class of channel codes achieving the symmetric capacity of the binary-input discrete memoryless channels (B-DMC) with efficient encoding and decoding algorithms. But the weight spectrum of polar codes is relatively poor compared to Reed-Muller (RM) codes, which degrades their maximum-likehood (ML) performance. Pre-transformation with an upper-triangular matrix (including cyclic redundancy check (CRC), parity-check (PC) and polarization-adjusted convolutional (PAC) codes), improves weight spectrum while retaining polarization. In this paper, the weight spectrum of upper-triangular pre-transformed polar codes is mathematically analyzed. In particular, we focus on calculating the number of low-weight codewords due to their impact on error-correction performance. Simulation results verify the accuracy of the analysis.

Recently, a parallel decoding algorithm of GN-coset codes was proposed. The algorithm exploits two equivalent decoding graphs. For each graph, the inner code part, which consists of independent component codes, is decoded in parallel. The extrinsic information of the code bits is obtained and iteratively exchanged between the graphs until convergence. This algorithm enjoys a higher decoding parallelism than the previous successive cancellation algorithms, due to the avoidance of serial outer code processing. In this work, we present a hardware implementation of the parallel decoding algorithm, it can support maximum N = 16384. We complete the decoder's physical layout in TSMC 16nm process and the size is 999.936μm×999.936μm, ≈ 1.00mm2. The decoder's area efficiency and power consumption are evaluated for the cases of N = 16384, K = 13225 and N = 16384, K = 14161. Scaled to 7nm process, the decoder's throughput is higher than 477Gbps/mm2and 533Gbps/mm2with five iterations.

Recently, a parallel decoding framework of GN-coset codes was proposed. High throughput is achieved by decoding the independent component polar codes in parallel. Various algorithms can be employed to decode these component codes, enabling a flexible throughput-performance tradeoff. In this work, we adopt successive cancellation (SC) as the component decoders to achieve the highest-throughput end of the tradeoff. The benefits over soft-output component decoders are reduced complexity and simpler (binary) interconnections among component decoders. To reduce performance degradation, we integrate an error detector and a log-likelihood ratio (LLR) generator into each component decoder. The LLR generator, specifically the damping factors therein, is designed by a genetic algorithm. This low-complexity design can achieve an area efficiency of 533Gbps/mm2under 7nm technology.

Polar codes has been selected as the channel coding scheme for 5G new radio (NR) control channel. Specifically, a special type of parity-check polar (PC-Polar) codes was adopted in uplink control information (UCI). In this paper, we propose a parity-check soft-cancellation (PC-SCAN) algorithm and its simplified version to decode PC-Polar codes. The potential benefits are two-fold. First, PC-SCAN can provide soft output for PCPolar codes, which is essential for advanced turbo receivers. Second, the decoding performance is better than that of successive cancellation (SC). This is due to the fact that paritycheck constraints can be exploited by PC-SCAN to enhance the reliability of other information bits over the iterations. Moreover, we describe a cyclic-shift-register (CSR) based implementation "CSR-SCAN" to reduce both hardware cost and latency with minimum performance loss.

In this work, we propose a type of GN-coset codes for parallel decoding. The parallel decoder exploits two equivalent decoding graphs of GN-coset codes. For each decoding graph, the inner code part is composed of independent component codes to be decoded in parallel. The extrinsic information of the code bits is obtained and iteratively exchanged between the two graphs until convergence. Accordingly, we explore a heuristic and flexible code construction method (information set selection) for various information lengths and coding rates. Compared to the previous successive cancellation algorithm, the parallel decoder avoids the serial outer code processing and enjoys a higher degree of parallelism. Furthermore, a flexible trade-off between performance and decoding latency can be achieved with three types of component decoders. Simulation results demonstrate that the proposed encoder-decoder framework achieves comparable error correction performance to polar codes with a much lower decoding latency.

In this paper, we investigate an artificial-intelligence (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).

In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environment with state, action, and reward is defined in the context of polar coding. Specifically, a state represents the construction of an (N, K) polar code, an action specifies its reduction to an (N, K - 1) subcode, and the reward is the decoding performance. A neural network architecture consisting of both policy and value networks is proposed to generate actions based on the observed states, aiming at maximizing the overall rewards. A loss function is defined to trade off between exploitation and exploration. To further improve learning efficiency and quality, an “integrated learning” paradigm is proposed. It first employs a genetic algorithm to generate a population of (sub-)optimal polar codes for each (N, K), and then uses them as prior knowledge to refine the policy of RL. Such a paradigm is shown to accelerate the training process, and converge at better performances. Simulation results show that the proposed learning-based polar constructions achieve comparable, or even better, performances than the state of the art under successive cancellation list (SCL) decoders, and meanwhile satisfies the nested property. Last but not least, the learning process does not exploit explicit expert knowledge from polar coding theory.

Accurate prediction of fading channel in the upcoming transmission frame is essential to realize adaptive transmission for transmitters, and receivers with the ability of channel prediction can also save some computations of channel estimation. However, due to the rapid channel variation and channel estimation error, reliable prediction is hard to realize. In this situation, an appropriate channel model should be selected, which can cover both the statistical model and small scale fading of channel, this reminds us the natural languages, which also have statistical word frequency and specific sentences. Accordingly, in this paper, we take wireless channel model as a language model, and the time-varying channel as talking in this language, while the realistic noisy estimated channel can be compared with mumbling. Furthermore, in order to utilize as much as possible the information a channel coefficient takes, we discard the conventional two features of absolute value and phase, replacing with hundreds of features which will be learned by our channel model, to do this, we use a vocabulary to map a complex channel coefficient into an ID, which is represented by a vector of real numbers. Recurrent neural networks technique is used as its good balance between memorization and generalization, moreover, we creatively introduce sequence-to-sequence (seq2seq) models in time series channel prediction, which can translate past channel into future channel. The results show that realistic channel prediction with superior performance relative to channel estimation is attainable.

The key to successive cancellation (SC) flip decoding of polar codes is to accurately identify the first error bit. The optimal flipping strategy is considered difficult due to lack of an analytical solution. Alternatively, we propose a deep learning aided SC flip algorithm. Specifically, before each SC decoding attempt, a long short-term memory (LSTM) network is exploited to either (i) locate the first error bit, or (ii) undo a previous "wrong" flip. In each SC attempt, the sequence of log likelihood ratios (LLRs) derived in the previous SC attempt is exploited to decide which action to take. Accordingly, a two-stage training method of the LSTM network is proposed, i.e., learn to locate first error bits in the first stage, and then to undo "wrong" flips in the second stage. Simulation results show that the proposed approach identifies error bits more accurately and achieves better block error rate performance than the state-of-the-art SC flip algorithms.

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 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 application-specific 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 easily-reproducible 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.

Polarization weight (PW) is a novel construction method for polar codes, which results in universal reliability ordering efficiently and yields imilar performance as other channel dependent construction methods. In this paper, we interpret PW method or algorithm from a rate allocation perspective of channel polarization to show its effectiveness. Take the AWGN channel as an example, we show that the rate allocation for information bits by PW method coincides with the capacities of channel polarization. Furthermore, some properties of PW algorithm, including the universal and fractal of the reliability ordering, are illustrated, which facilitate the implementation in practice. Comprehensive simulation results with various code rates, code lengths and list sizes are also shown to validate the effectiveness of PW method.

Polar codes are the first proven capacity-achieving codes. Recently, they are adopted as the channel coding scheme for 5G due to their superior performance. A polar code for encoding length-K information bits in length-N codeword could be specified by the polar code construction method. Most construction methods define a polar code related to channel parameter set, e.g. designed signal-to-noise ratio. Polarization weight (PW) is a channel-independent approximation method, which estimates the subchannel reliability as a function of its index. In this paper, we generalize the PW method by including higher-order bases or extended bases. The proposed methods have robust performance while preserving the computational and mathematical simplicity as PW.

With the prevalence of smart mobile devices and surveillance cameras, the traffic load within the Internet of Things (IoT) has shifted away from nonmultimedia data to multimedia traffics, particularly, the video content. However, the explosive demand for real-time video communication over wireless networks in IoT is constantly challenging both video coding and communication research communities. The state-of-the-art answer to this challenge is sliding-window-based delay-aware fountain (DAF) codes, which combine the channel-adaptive feature in rateless coding and the delay-aware feature in video coding. However, the high computational cost and large delay make it impractical for real-time streaming. To address this issue, we integrate the model predictive control (MPC) technique into DAF codes, so the complexity is lowered to an affordable level so that real-time video encoding is supported. Two schemes are developed in this paper: 1) DAF-S, the smallhorizon DAF codes and 2) DAF-O, the MPC-based DAF using video bit rate prediction. The advantages of both designs are validated through theoretical analysis and comprehensive experiments. The results of simulation experiments show that the decoding ratio of DAF-S is close to the global optimum in DAF codes, and higher than the other existing schemes; DAF-O outperforms the state-of-the-art real-time video communication algorithms.

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 bit-flipping 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).

We propose to use second order Reed-Muller (RM) sequence for user identification in 5G grant-free access. The benefits of RM sequences mainly lie in two folds, (i) support of much larger user space, hence lower collision probability and (ii) lower detection complexity. These two features are essential to meet the massive connectivity (107links/km2), ultra-reliable and low-latency requirements in 5G, e.g., one-shot transmission (≤ Ims) with ≤ 10-4packet error rate. However, the nonorthogonality introduced during sequence space expansion leads to worse detection performance. In this paper, we propose a noiseresilient detection algorithm along with a layered sequence construction to meet the harsh requirements. Link-level simulations in both narrow-band and OFDM-based scenarios show that RM sequences are suitable for 5G.

Spreading a large file consisting of many packets over a mobile network is challenging due to the short meeting duration for each transmission. Moreover, two typical causes of inefficient file spreading are duplicate packet reception at the destination nodes and excessive overhead exchanges. We propose to employ fountain codes at the source node to jointly addresses the three issues: 1) each coded packet can be small enough to fit into the meeting duration; 2) duplicate packet reception is significantly reduced since each coded packet is innovative; and 3) overhead is greatly saved by using file-level ACK instead of packet-level ACK. We conduct performance analysis in terms of the source-to-destination file delay and source-to-destination file spreading time in both non-relaying and relaying scenarios. While packet duplication can be eliminated in the former scenario, there is still a non-trivial duplication probability if relaying is allowed. Therefore, we propose a fountain-coded two-hop relaying (FTTR) protocol to further reduce the packet duplication ratio so that the spreading performance does not degrade with network size. The file spreading time and packet duplication ratio of FTTR are derived in closed form and verified through simulations.

Joint FoUntain coding and Network coding (FUN) is proposed to boost information spreading over multi-hop lossy networks. The novelty of our FUN approach lies in combining the best features of fountain coding, intra-session network coding, and cross-next-hop network coding. This paper provides an in-depth study of FUN codes. First, we theoretically analyze the throughput of FUN codes. Second, we identify several practical issues that may undermine the actual performance, such as buffer overflow, and quantify the resulting throughput degradation. Finally, we propose a systematic design to overcome these issues. Simulation results in TDMA multi-hop networks show that our methods yield near-optimal throughput and are significantly better than fountain codes and existing network coding schemes.

An important application for modern large-scale networks is to spread the information efficiently to the largest audience. To better understand the theoretical underpinnings, a novel graph metric named mobile conductance was proposed in our previous work to evaluate the information spreading time of a connected mobile network. By capturing the details of both network structure and mobility pattern, this metric essentially determines the network bottleneck for conducting information flow under general network mobility. Despite major relaxation on node mobility, only slight relaxation on network connectivity was made in our previous work. In this paper, we make another major relaxation on the network connectivity by extending the mobile-conductance based analytical model to the sparse setting, hence offering a unified view. Interestingly, a penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. By jointly considering mobility and connectivity, we derive the mobile conductance for various mobility models with general connectivity. Using these analytical results, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to compensate for network connectivity deficiency.

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range, and therefore, accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the information propagation speed (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme, in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range and therefore accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the Information Propagation Speed (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.

In this paper, we address the power control problem in an interference network with multiple users transmitting simultaneously on the same channel. We aim at achieving the energy efficiency (EE) balance among difference users. First, a multi-objective optimization problem is formulated, which maximizes the EE of each individual user while guaranteeing their minimum data rate requirements. To find its solution, we adopt two different scalarization methods to combine multiple objectives into a single one, namely, the weighted-sum method and the weighted Tchebycheff method. The problem in the weighted-sum method turns out to be a non-concave sum of- ratios optimization and an effective algorithm is developed based on the concave-convex procedure (CCCP) method. On the other hand, the problem in the weighted Tchebycheff method becomes a generalized fractional programming and we utilize the Dinkelbach method and the CCCP method to solve it. Through numerical simulation, we find that both methods can effectively obtain the Pareto optimal solutions to the multiobjective optimization problem and achieve the EE balance among users as well.

In high mobility wireless communication scenarios, the most challenging issue is to cope with the extremely fast fading channel. Compared with its static counterpart, channel estimation in high mobility scenarios consumes excessive energy and spectrum to achieve similar performance. To address this issue, we exploit delay correlation with sequential antenna-hopping (AH) to convert the rapid fading channels to a virtual slow-fading channel. As a result, robust communication can be achieved with much less channel estimation overhead even under high mobility. Finally, two important performance metrics, namely, channel estimation mean square error (MSE) and transmission symbol error rate (SER), are re-examined for this virtual slow-fading channel. Numerical results verify the good performance of the proposed scheme and elucidate its effectiveness in high mobility communication scenarios.

A framework on decentralized interference coordination based on the pricing mechanism is developed for device-to-device (D2D) communication underlying cellular systems to guarantee quality of service (QoS) of both cellular users (CUs) and D2D links. We aim at coordinating two types of interference: inter-layer interference from D2D pairs to CUs and intra-layer interference among D2D pairs. The former is mitigated by the base station through setting a price on the channel being reused by D2D pairs while the latter is solved by a game-theoretic approach, in which the D2D pairs compete for the spectrum until a Nash Equilibrium (NE) is achieved. Finally, numerical results verify that the proposed distributed scheme is effective for the interference coordination and its performance is close to the centralized scheme.

A joint modulation and coding scheme is proposed based on Rateless Codes and Vector OFDM systems to combat fading over doubly selective channels. A rateless encoder can generate potentially unlimited coded symbol. Vector OFDM is a general transmission scheme, where OFDM and Single-Carrier systems can be seen as two extreme cases. The main focus of this paper is to design and analyze a Rateless Coded Vector OFDM scheme to obtain extra gain and improve the system performance. Firstly, we derive the lower bound of the decoding complexity of the block universal Raptor Codes and the upper bound of the probability to decode successfully in Binary Erasure Channels. Secondly, we analyze the joint scheme of Rateless Coded Vector OFDM with a novel proposition proposed, which reveals the impacts of the degree distribution on the system performance. According to the proposition, two practical Rateless Coded Vector OFDM methods are presented, which can achieve considerable joint Multipath-and-Doppler diversity gain and coded modulation gain to improve the reliability of the system. Finally, simulation results are demonstrated to validate the proposed scheme.

Provisioning reliable wireless services for railway passengers is becoming an increasingly critical problem to be addressed with the fast development of high speed trains (HST). In this paper, exploiting the linear mobility inherent to the HST communication scenario, we discover a new type of spatial-temporal correlation between the base station and moving antenna array on the roof top of the train. Capitalizing on the new spatial-temporal correlation structure and properties, an improved differential space-time modulation (DSTM) scheme is proposed. Analytical expressions are obtained for the pairwise error probability of the system. It is demonstrated that the proposed approach achieves superior error performance compared with the conventional DSTM scheme. In addition, an adaptive method, which dynamically adjusts the transmission block length is proposed to further enhance the system performance. Numerical results are provided to verify the performance of the proposed schemes.

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.

A pricing-based joint spectrum and power allocation framework is proposed for decentralized interference coordination among device-to-device (D2D) communications and cellular users (CUs), with the quality-of-service guarantee. The interlayer interference from D2D pairs to CUs is controlled by the base station through setting a price for each D2D channel usage. The intralayer interference among D2D pairs is mitigated distributively using a game-theoretic approach, where the D2D pairs compete for the spectrum until a Nash equilibrium is achieved. The effectiveness of the proposed strategy, including a practical scheme with limited signaling overhead, is demonstrated through comparing with a centralized scheme.

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-of-ratios 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.

In this paper, we analyze the ergodic sum rate in a multiuser multi-cell downlink. Coordinated beamforming is employed to mitigate the interference, including intra-cell and inter-cell interference. However, due to the limited capacity of the backhaul link, only partial channel state information (CSI) is obtained at the base stations (BSs), resulting in residual interference even with coordinated beamforming. By quantifying the impact of imperfect CSI, we derive closed-form ergodic sum rate results for a multiuser multi-cell downlink in terms of i. CSI accuracy, ii. transmit signal-to-noise ratio (SNR) and iii. channel condition. Furthermore, through asymptotic analysis of the performance loss induced by imperfect CSI, we obtain some clear insights on the performance. Finally, our theoretical claims are validated through extensive simulations.

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel cluster-based cooperative information forwarding scheme, in which the vehicles opportunistically form virtual antenna arrays to boost one-hop transmission range and therefore accelerate information propagation along the highway. Both closed-form results of the transmission range gain and the improved Information Propagation Speed (IPS) are derived and verified by simulations. It is observed that the proposed scheme demonstrates the most significant IPS gain in moderate traffic scenarios, whereas too dense or too sparse vehicle density results in less gain. Moreover, it is also shown that increased mobility offers more contact opportunities and thus facilitates information propagation.

In this paper, we propose a novel energy-efficient resource allocation method to simultaneously improve both downlink and uplink energy efficiency (EE) for time division duplex (TDD) systems with carrier aggregation (CA). We aim at EE tradeoff between downlink and uplink by optimizing the power and bandwidth allocation on each carrier component (CC) for each user. The objective function is a sum of several fractional functions, therefore, a novel nonlinear sum-of-ratios programming technique is used to solve it. We first transform the problem into an equivalent and better tractable one and then propose an iterative algorithm to find the global optimum solution. Numerical results show that our method can converge with an acceptable number of iterations and achieve flexible EE tradeoff between downlink and uplink.

A class of recovering algorithms for 1-bit compressive sensing (CS) named Soft Consistency Reconstructions (SCRs) are proposed. Recognizing that CS recovery is essentially an optimization problem, we endeavor to improve the characteristics of the objective function under noisy environments. With a family of re-designed consistency criteria, SCRs achieve remarkable counter-noise performance gain over the existing counterparts, thus acquiring the desired robustness in many real-world applications. The benefits of soft decisions are exemplified through structural analysis of the objective function, with intuition described for better understanding. As expected, through comparisons with existing methods in simulations, SCRs demonstrate preferable robustness against noise in low signal-to-noise ratio (SNR) regime, while maintaining comparable performance in high SNR regime.

In this paper, we study the problem of physical layer security in a large-scale multiple-input multiple-output (LS-MIMO) relaying system. The advantage of LS-MIMO relaying systems is exploited to enhance both wireless security and spectral efficiency. In particular, the challenging issue incurred by short interception distance is well addressed. Under very practical assumptions, i.e., no eavesdropper's channel state information (CSI) and imperfect legitimate channel CSI, this paper gives a thorough investigation of the impact of imperfect CSI in two classic relaying systems, i.e., amplify-and-forward (AF) and decode-and-forward (DF) systems, and obtain explicit expressions of secrecy outage capacities for both cases. Finally, our theoretical claims are validated by the numerical results.

In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency.

In this paper, we propose a general analytical framework for information spreading in mobile networks based on a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. We derive a general result for the information spreading time in mobile networks in terms of this new metric, and instantiate it through several popular mobility models. Large scale network simulation is conducted to verify our analysis.

Opportunistically sharing the white spaces, or the temporarily unoccupied spectrum licensed to the primary user (PU), is a practical way to improve the spectrum utilization. In this paper, we consider the fundamental problem of rate regions achievable for multiple secondary users (SUs) which send their information to a common receiver over such a white space channel. In particular, the PU activities are treated as on/off side information, which can be obtained causally or non-causally by the SUs. The system is then modeled as a multi-switch channel and its achievable rate regions are characterized in some scenarios. Explicit forms of outer and inner bounds of the rate regions are derived by assuming additional side information, and they are shown to be tight in some special cases. An optimal rate and power allocation scheme that maximizes the sum rate is also proposed. The numerical results reveal the impacts of side information, channel correlation and PU activity on the achievable rates, and also verify the effectiveness of our rate and power allocation scheme. Our work may shed some light on the fundamental limit and design tradeoffs in practical cognitive radio systems.

In this paper, we analyze the effect of mobility on information spreading in geometric networks through natural random walks. Specifically, our focus is on epidemic propagation via mobile gossip, a variation from its static counterpart. Our contributions are twofold. Firstly, we propose a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. Secondly, we utilize geometrical properties to explore this metric for several popular mobility models, and offer insights on the corresponding results. Large scale network simulation is conducted to verify our analysis.

Rateless coding is a new type of feed-forward incremental redundancy channel coding technique which can be incorporated with MIMO technology to exploit both the diversity and coding gain with possibly reduced channel feedback. In this paper, the benefits of limited feedback beamforming and rateless coding are investigated jointly in a multiple-input single-output (MISO) downlink system. Based on weight enumerator analysis and with the goal of improving the effective channel gain, we propose an adaptive bit allocation scheme by taking advantage of the inherent relationship between the feedback codebook size and the number of transmitted coded bits. Given the feedback codebook size, we derive the required minimum number of coded bits for a reliable data recovery. In addition, for the service with time delay constraint, the required feedback codebook size is also determined. Finally, numerical results are presented to validate our theoretical analysis.

A novel Distributed Spectrum-Aware Clustering (DSAC) scheme is proposed in the context of Cognitive Radio Sensor Networks (CRSN). DSAC aims at forming energy efficient clusters in a self-organized fashion while restricting interference to Primary User (PU) systems. The spectrum-aware clustered structure is presented where the communications consist of intra- cluster aggregation and inter-cluster relaying. In order to save communication power, the optimal number of clusters is derived and the idea of groupwise constrained clustering is introduced to minimize intra-cluster distance under spectrum-aware constraint. In terms of practical implementation, DSAC demonstrates preferable scalability and stability because of its low complexity and quick convergence under dynamic PU activity. Finally, simulation results are given to validate the proposed scheme.

A novel carrier sensing method with self-cancelation of the inter-carrier emission inevitably existent in OFDMA-based coexisting systems is proposed. Different from the conventional energy-based carrier detector, which directly compares the carrier energy with some predefined thresholds without considering such inter-carrier emissions, our proposed method can remove them before making decision, thus significantly reduces the false-alarm probability and provides more access opportunities. Firstly, the detection model considering the inter-carrier emission is presented, and then the distribution of inter-carrier energy emission is investigated. Secondly, the energy emission of the detected adjacent carriers got by conservative pre-decision are gradually removed from the carrier to be detected. Finally, the performance of the proposed method is evaluated in terms of detection probability and false-alarm probability.

A novel concept of Joint Source and Channel Sensing (JSCS) is introduced in the context of Cognitive Radio Sensor Networks(CRSN). Every sensor node has two basic tasks: application-oriented source sensing and ambient-oriented channel sensing. The former is to collect the application-specific source information and deliver it to the access point within some limit of distortion, while the latter is to find the vacant channels and provide spectrum access opportunities for the sensed source information. With in-depth exploration, we find that these two tasks are actually interrelated when taking into account the energy constraints. The main focus of this paper is to minimize the total power consumed by these two tasks while bounding the distortion of the application-specific source information. Firstly, we present a specific slotted sensing and transmission scheme, and establish the multi-task power consumption model. Secondly, we jointly analyze the interplay between these two sensing tasks, and then propose a proper sensing and power allocation scheme to minimize the total power consumption. Finally, simulation results are given to validate the proposed scheme.

This paper addresses the cross-layer design of multi-user opportunistic spectrum access (OSA) in cognitive radio networks under the framework of grouped cooperative sensing. We attempt to reveal the mutually conflict nature in the following two processes: a) multi-user collaboration during cooperative sensing in PHY layer; b) the contention emerged during competitive medium access in MAC layer. Specifically, higher reliability of cooperative sensing requires more engaged cognitive users (CUs), which, in turn, may cause more intense competition in access, thereby reducing the overall channel utilization efficiency. By exploiting the inherent interplay between these two processes, we tradeoff their performance with respect to the number of cooperating users in one group. The optimal number that maximizes the channel revenue is determined by a binary search method we propose. Finally, the analysis and performance are validated by extensive simulations.