Seamless Rate Adaptation Research Articles

Seamless rate adaptation for wide SNR range in FSO systems based on rate compatible modulation.

We propose and experimentally demonstrate a large-range and seamless rate-adaptive free-space optical (FSO) scheme based on rate compatible modulation (RCM). It utilizes a mapping method through weighted summation, enabling an even increase of bit energy. This allows the system to seamlessly adjust the throughput. In addition, the handover mechanism based on rate check and acknowledgment signals ensures a wide coverage of signal-to-noise ratio (SNR) fluctuation. It is worth noting that this scheme does not require any changes to the mapping matrix and decoding algorithm, making it easier to implement in real-world systems. Simulation proves the superiority of the proposed approach in terms of coding rate coverage by applying a fixed mapping matrix compared to a traditional adaptive modulation and coding scheme. Experimental demonstration over a 50 m FSO link verifies that the SNR dynamic range of this scheme is >15 dB with a seamless rate adjustment between 6.7 Gb/s and 53.6 Gb/s and the operability of RCM is not limited by the time scale of turbulence, revealing huge potential for the massive connections in future smart cities.

Rate Compatible Gaussian Modulation

This letter proposes a rate compatible Gaussian modulation (RCGM) for wireless channels. RCGM constructs a high-density non-uniformly spaced Gaussian constellation, and optimizes the mapping schemes by maximizing the Harmonic mean. The information bits are randomly sampled by a sparse matrix whose weights are all powers of 2, then the sampled bits are modulated to the high-density constellation following the mapping scheme. The number of the modulated symbols can be adjusted at a fine granularity according to the channel condition so as to achieve seamless rate adaptation. Both numerical and simulation results show that RCGM outperforms other rate adaptive modulations, such as rate compatible modulation and analog fountain code, in terms of error probability and throughput. RCGM can provide a capacity approaching modulation scheme in a wide range of signal-to-noise ratio even without channel coding.

Seamless rate adaptation for indoor visible light communication without CSI at the transmitter

Visible light communication (VLC), which uses LEDs for both lighting and communication purposes, has recently emerged as an alternative or support system to radio frequency (RF) communication. VLC channel between LEDs and photo-diodes is highly variable and pretty much depends on optical line of sight (LOS) and field of view (FOV). Therefore, received signal strength may vary dramatically, especially for mobile receivers. To ensure spectral efficient communication, it is essential to use rate adaptation methods. In this study, we propose rateless codes based rate adaptation method that adjusts transmission rate at receiver side and does not require channel state information (CSI) at the transmitter side. The spectral efficiency of the proposed method is analyzed and compared with adaptive modulation and coding (AMC) rate adaptation method widely used in literature. For analyses, we consider a realistic communication system model by using commercially available LEDs and also fulfilling illumination requirements. In order to minimize the multi-path effects, optical orthogonal frequency-division multiplexing (OFDM) is utilized. Numerical results indicate that proposed rate adaptive method can achieve seamless rate adaptation and has higher spectral efficiency compared to AMC in the presence of significant signal quality variations caused by mobility.

Compressive Spinal Codes

It is well known that spinal codes can essentially approach channel capacity for equiprobable source, and achieve seamless rate adaptation at the same time. But how to design an optimal spinal codes for non-equiprobable source is still an open problem. This paper proposes a novel Sparse Spinal (S-Spinal) codes design, which can essentially approach channel capacity for non-equiprobable and compressible source, and its seamless rate adaptation is also preserved. The main idea is to utilize source distribution information at the spinal decoder. The sparser the source is, the less symbols the receiver needs. The rateless feature of spinal codes can provide fine-grain compression ratio. The different decoder designs for Binary Symmetrical Channel (BSC) and Additive White Gaussian Noise (AWGN) channel are proposed, since their priori information is different. We also analyze and prove the S-Spinal codes with a practical pruning decoder can essentially achieve the channel capacity. The experimental results show that the proposed S-Spinal is very close to channel capacity of both BSC and AWGN channel for non-equiprobable source, and can achieve seamless rate adaption in practical system.

Layered Decoding Algorithm and Two-Level Quasi-Cyclic Matrix Construction for Rate Compatible Modulation

Rate-compatible modulation (RCM) achieves seamless rate adaptation under time-varying channels by varying the number of transmitted symbols at a small granularity. The implementation of effective decoders based on the belief propagation (BP) algorithm suffers from the varying number of symbols and highly complex symbol nodes. Considering these RCM-specific characteristics, we present a top–down design methodology, including decoding algorithm, mapping matrix construction, and hardware implementation. The layered BP algorithm provides the potential to implement the decoders that fine-tune the decoding throughput according to the number of symbols. To mitigate the impact of inter-layer data dependency on decoding throughput and facilitate the implementation of low parallelism decoders, a construction and optimization algorithm is proposed to obtain the two-level quasi-cyclic matrices with small sub-matrix size. The proposed algorithm and matrix are applied to a partially parallel RCM decoder for a ${5544 \times 5544}$ matrix on a field-programmable gate-array (FPGA) device. A memory access structure that requires only one barrel shifter is used to improve area efficiency. The post-route results show that the decoder achieves a throughput of 377 M ~ 1100 Mbit/s at six iterations with a clock frequency of 218 MHz.

Fully-Parallel Stochastic Decoder for Rate Compatible Modulation

Rate compatible modulations (RCMs) are attractive for achieving seamless and blind rate adaptation under time varying channel. Although the decoding of RCM is inherently parallel, the highly complex processing nodes, and routing congestion have prohibited the implementation of fully-parallel decoders for high throughput. In this paper, we propose a new stochastic decoding algorithm for RCM to achieve desired parallel decoding. This algorithm provides even much better decoding performance than the floating-point belief propagation decoding algorithm with 20 iterations. To evaluate the effectiveness of the proposed algorithm, we apply it for the implementation of a field-programmable gate-array fully-parallel stochastic decoder that decodes a $ {400 \times 400}$ mapping matrix. Several novel structure techniques, including the RAM-based channel stream generator and the configurable variable node, are exploited to reduce the logical resources consumption. The implemented decoder achieves a clock frequency of 220 MHz and provides a maximum throughput about 136Mb/s at SNR = 10 dB and the number of symbols is 400. To the best of our knowledge, this decoder is the first reported fully-parallel RCM decoder which achieves the highest decoding throughput. This research validates the potential of stochastic RCM decoding as a practical approach for area-efficient and high throughput decoders.

Arithmetic-BICM for Seamless Rate Adaptation for Wireless Communication Systems

Bit-interleaved coded modulation (BICM) has been widely used in capacity-achieving communication systems. In this paper, we propose an enhanced BICM, namely, arithmetic-BICM (A-BICM), which is able to achieve high channel throughput and seamless rate adaptation in time-varying wireless channels. The A-BICM generates modulation symbols through arithmetic weighted sum operations, and it adapts to data rate via adjusting the number of transmitted symbols. Our contributions in the design of A-BICM are twofold. First, we use an extrinsic information transfer chart to analyze a demapping procedure, and this facilitates the selection of the weighting multiset, which is a key design parameter in A-BICM mapping. Second, we employ a systematic channel code in A-BICM and design an adaptation scheme to strike a balance between the performances in both high- and low-SNR regions. Simulation results show that the A-BICM outperforms the conventional BICM with length-864 low-density parity check coding in both additive white Gaussian noise and fading channels. The experiments in real WLAN environments reveal that A-BICM achieves 42.1% and 20.9% throughput gain over conventional BICM and hybrid automatic repeat request rate adaptation schemes. Furthermore, the channel coding rate and its constellation in A-BICM do not need to change within a fairly wide SNR region from 1 to 30 dB to reduce communication overhead.

Efficient coding modulation and seamless rate adaptation for visible light communications

The past decade has witnessed a rapid development of VLC technologies. However, the spectral efficiency has to be improved and the robustness enhanced, especially in dynamic lighting conditions. This article proposes an analog rateless code (ARC) modulation scheme for VLC in a hybrid VLC-RF system. The ARC uses a variable-length low-density random projection matrix to map a block of bits into a virtually unlimited number of modulation symbols. The sender progressively transmits the symbols, which are jointly decoded at the receiver, until an acknowledgment of successful decoding is received. Both the protocol design and coding matrix generation are discussed. Simulations validate that our proposed scheme not only achieves the desired seamless rate adaptation, but significantly outperforms a traditional OFDM VLC system in spectrum efficiency. Furthermore, experimental results demonstrate the feasibility of the proposed ARC scheme in a VLC system.

Co-operative Alien Noise Cancellation in Upstream VDSL: A New Decision Directed Approach

Alien noise in the vectored very-high-speed digital subscriber line (VDSL) system is part of the additive noise at the receiver and exhibits strong correlation among users. We present a per-tone co-operative alien noise cancellation (CoMAC) algorithm for the upstream (US) VDSL that can be applied subsequent to any self far-end-crosstalk (FEXT) mitigation strategy. CoMAC operates by predicting the noise seen by a given user based on the error samples from the remaining users. These errors are conveniently obtained after slicing the self-FEXT canceled signal of all the vectored users. We show that if the estimation of these errors is accurate, the proposed alien canceler achieves the Cramer-Rao lower bound (CRLB). In practice, the seamless rate adaptation (SRA) operation, which enables increased bit rate by increasing the bit-loading per-tone, can cause decision errors in any decision directed strategy. We also analyze the impact of these decision errors - an issue not addressed in the literature. We propose a strategy for bit-loading during the SRA operation by formulating a max-min optimization problem and demonstrate a possibility of a guaranteed (minimum) improvement in the per-user rate. Simulations indicate that performance of the algorithm can exceed the minimum value significantly in practical situations.

Compressive Coded Modulation for Seamless Rate Adaptation

This paper presents a novel compressive coded modulation (CCM) which simultaneously achieves joint source-channel coding and seamless rate adaptation. The embedding of source compression into modulation brings significant throughput gain when the physical layer data contain non-negligible redundancy. The kernel of CCM is a new random projection (RP) code inspired by the compressive sensing (CS) theory. The RP code generates multilevel symbols from source binaries through weighted sum operations. Then, the generated RP symbols are mapped into a dense constellation for transmission. The receiver performs joint decoding based on received symbols. As the number of RP symbols can be adjusted in fine granularity, the rate adaptation becomes seamless. Two key design issues in the proposed CCM are addressed in this paper. First, we consider the RP code design for sources with different redundancies. Three principles are established and a concrete implementation is given. Second, we devise a linear-time decoding algorithm for the proposed RP code. In this belief propagation (BP) algorithm, we find that computing convolution in time domain is more efficient than that in frequency domain for binary variable nodes. Moreover, we invent a ZigZag deconvolution to further reduce the complexity. Analysis show that the proposed decoding algorithm is nearly 20 times faster than the state-of-the-art BP algorithm for CS called CS-BP. Emulations on traced data show that CCM achieves significant throughput gain, up to 33% and 70%, respectively, over the Hybrid ARQ with compression and BICM with compression, under practical time-varying wireless channels.

Fast Decoding and Hardware Design for Binary-Input Compressive Sensing

Binary-input compressive sensing (BiCS) has recently been applied to wireless communications as a modulated coding scheme for seamless rate adaptation. Different from conventional channel codes which generate binary symbols with logical-OR (XOR) operations, BiCS generates multilevel symbols through weighted sum operation. Although BiCS can be decoded by message passing, it needs to compute the convolution of probability functions in each iteration. The high decoding complexity has prevented the technique from being applied to practical use. In this paper, we propose a fast BiCS decoding algorithm and its corresponding partial-parallel hardware design. In this algorithm, we first build lookup tables to solve the computationally intensive problem of convolution. Through these tables, we successfully convert the convolution of probabilities into the polynomial of some exponential terms. This key step allows us to use log-likelihood ratio as message in message passing decoding and a fast algorithm is developed by approximate computing. We further design a partial-parallel hardware decoder. To avoid memory collision, we propose a multilevel cyclic-shift approach to generate the CS measurement matrix. We design horizontal unit processors with the proposed tables for iterative computing. Our analyses show that the proposed fast algorithm can reduce multiplications by nearly 90%. The decoding speed of our field-programmable gate array design reaches the range of communication rate in modern wireless networks.