The noisy shuffling channel models the conditions encountered in DNA storage systems, where transmitted data segments experience random permutation and substitution errors. Reliable communication over this channel requires effective indexing and channel coding strategies for segment order restoration and error correction. This paper introduces a concatenated coding approach for communication over the noisy shuffling channel using Reed-Solomon (RS) codes as outer codes and polar codes as inner codes. A coset-based indexing method, derived from polar codes, is proposed. A joint decoder is designed to detect the permutation pattern and perform polar decoding simultaneously. An upper bound on the frame error rate (FER) is derived when minimum distance decoding is employed for decoding. Also, an approximate analysis of the FER using random coding is conducted. A mapping between the cosets of the polar code and subsets of its frozen bits is established to design cosets achieving lower FERs compared to a commonly used explicit indexing method. Furthermore, a low-complexity decoding approach is devised, providing a trade-off between the computational complexity of the joint decoder and its performance.

Noisy shuffling channels capture the main characteristics of DNA storage systems where distinct segments of data are received out of order, after being corrupted by substitution errors. For realistic schemes with short-length segments, practical indexing and channel coding strategies are required to restore the order and combat the channel noise. In this paper, we develop a finite-length concatenated coding scheme that employs Reed-Solomon (RS) codes as outer codes and polar codes as inner codes, and utilizes an implicit indexing method based on cosets of the polar code. We propose a matched decoding method along with a metric for detecting the index that successfully restores the order, and correct channel errors at the receiver. Residual errors that are not corrected by the matched decoder are then corrected by the outer RS code. We derive analytical approximations for the frame error rate of the proposed scheme, and also evaluate its performance through simulations to demonstrate that the proposed implicit indexing method outperforms explicit indexing.

We analyze the performance of Coded Slotted ALOHA (CSA) protocols in scenarios where users are equipped with limited batteries that are recharged through Energy Harvesting (EH). First, we assume a Perfect Packet Recovery Scenario (PPRS) for which the received packets are decoded with no errors when there is no interference. We introduce Battery Outage Probability (BOP) as an extra performance metric; and, we derive the optimal EH-CSA transmission policies, which offer the maximum attainable traffic load while maintaining an asymptotically negligible Packet Loss Ratio (PLR), under specific rate and BOP constraints. We extend our study to Imperfect Packet Recovery Scenario (IPRS) where impairments at the physical layer, including channel estimation and channel decoding errors, will distort messages being passed through the iterative Successive Interference Cancellation (SIC) process. The distorted messages being passed through the SIC process potentially lead to error propagation. In order to track the error propagation process, we define the concept ofAccumulated Noise plus Interference Power(ANIP), and analytically track the evolution of its probability distribution. We employ our results to evaluate the bit error rates for different transmission policies for the case of IPRS. We also demonstrate the advantages of the optimal transmission policies through numerical examples for both PPRS and IPRS. Our results show that the optimal EH-CSA policies outperform the policies optimized for standard CSA without EH considerations, and the schemes that are optimal for PPRS are not necessarily optimal for the IPRS case. Furthermore, the EH-CSA optimal policies strictly outperform standard CRDSA when the system is required to support higher traffic loads.

We present a decentralized feedback-aided Irregular Repetition Slotted ALOHA (IRSA) scheme that improves energy efficiency. The scheme divides the IRSA MAC frame into several sub-frames, performs tentative decoding after each sub-frame, and uses limited feedback for users to detect whether their packet has been decoded at the receiver. Once a user detects that its packet is decoded, it stops transmitting its remaining replicas, resulting in a decrease in the expected number of transmitted packet replicas and an increase in energy efficiency. For analysis, we employ a graph-based representation of the successive interference cancellation decoding of IRSA. We prove several results for a fixed graph, and extend our analysis to a randomly selected graph to derive the efficiency of the proposed scheme. Numerical results show that the proposed feedback-aided IRSA solution outperforms standard IRSA and performs similarly to the best known Coded Slotted ALOHA (CSA) schemes. Also, the proposed scheme achieves efficiencies significantly larger than the threshold of 0.5 which is an upper bound for standard IRSA.

Irregular Repetition Slotted ALOHA (IRSA) achieves load thresholds very close to 1 at the expense of reduced energy efficiency compared to its competitor, Coded Slotted ALOHA (CSA). The efficiency is related to the expected number of transmitted replicas, and is upper-bounded by 0.5 in the case of IRSA. In this paper, we present a feedback-aided IRSA scheme, analyze its efficiency, and show that utilizing a very limited feedback will offer considerable improvements. Remarkably, the feedback-aided scheme enables IRSA to achieve efficiencies greater than 0.5, and in some cases, perform very close to the more complex CSA schemes.

Fluctuating two-ray (FTR) channel model was shown to effectively characterize millimeter wave (mmWave) communication channels. In this article, we adopt FTR to investigate amplify-and-Forward (AF) mmWave relaying system. Two communications scenarios are considered corresponding to the presence and absence of a direct link between the transmitter and receiver. Outage probability and symbol error rate (SER) are then analytically obtained as performance metrics. The results are further compared with the corresponding metrics obtained based on conventional channel models including Nakagami-m and two-wave with diffuse power (TWDP). Especially, for the high-SNR regime, our analyses indicate that performance evaluations based on the conventional models significantly deviate from that of based on the FTR model. Our results provide quantitative insights on the importance of model selection in design and performance evaluations of relay-based mmWave systems. Moreover, for the high-SNR regime, we carry out asymptotic analysis and obtain a low-complexity expression for the achieved AF relaying gain. Such an expression provides a quantitative measure on whether or not AF relaying outperforms no-relaying in a given setting. Extensive numerical and simulation results are provided to confirm the accuracy of the analysis and investigate system performance in different settings.

In this paper, we investigate the performance of a relay selection scheme in a buffer-aided multi-relay network with coded transmissions. We consider: (1) two different coding schemes, i.e., Turbo codes and distributed Turbo codes (DTC) and (2) quasi-static Rayleigh fading channels, where all the links are independent but asymmetric. For each of the aforementioned schemes, we analyze the performance of the system in terms of the average throughput and average delay. In addition, simple and explicit approximations of the asymptotic throughput for infinite buffer size and the high signal-to-noise ratio (SNR) regime are obtained. Our analysis of the asymptotic throughput provides the maximum achievable diversity gain of the system, and the accuracy of the derived analytical framework is assessed when compared to the Monte-Carlo simulations.

Massive multiple‐input multiple‐output (MIMO) technology employs hundreds of antennas at the base station (BS) of a cellular system. Activating hundreds of antennas requires hundreds of radio‐frequency (RF) chains that lead to high cost and high energy consumption. Antenna selection techniques reduce the number of RF chains while curtailing the resulting performance loss. In this study, the authors consider massive‐MIMO downlink with zero‐forcing (ZF) precoding in a single cell. In ZF precoding with antenna selection, the number of users must always be less than or equal to the number of selected antennas, which may require adopting user selection (scheduling) algorithms. They propose a joint user and antenna selection algorithm to increase BS's energy efficiency (EE) and show that its performance is very close to that of the optimum exhaustive‐search based scheme. Using an analytical approximation for EE of the proposed scheme in asymptotically large numbers of users region, they find the optimum number of antennas to be selected.

Incremental relaying is a spectral-efficient relaying approach which introduces itself as one of the possible solutions to the spectrum scarcity problem for future generations of cellular communications systems. In this paper, we study threshold amplify-and-forward incremental relaying for correlated fading channels, i.e., fading channels for which the channel gain amplitude varies during the time interval of a block transmission; however, the variation occurs in a correlating fashion. From this aspect, these channels are different from both block-fading and fast-fading channels. Due to the correlating nature of the channels, it is possible to compress channel state information (CSI) sequences and employ the compressed CSI sequences as side information to carefully select the bits transmitted by the relay. This idea is already employed in previous works to increase the spectral efficiency; however, those works consider block-fading channels and also assume maximal ratio combining (MRC) at receiver. In case of MRC, explicit expressions for the end-to-end signal-to-noise ratio (SNR) exist, which enable packet error rate analysis by aid of Moment Generating Functions of the SNR. However, to apply MRC, the receiver requires all channel gains (including source-relay channel gain). In case of correlated channels, communicating all real-valued channel gains, requires a large overhead. This fact renders applicability of MRC for correlated channels. We apply the more practical Equal-Gain Combining method. We derive series approximations for symbol error rate, given M-QAM and MPSK modulations, and assuming Nakagami-m fading channels. We also analyze the spectral efficiency by assuming Markov models for the channels. We compare the spectral efficiency in case of correlated and fast-fading channels and show that, unlike fast-fading channels, correlated fading channels allow for a spectrally efficient scheme.

We propose a relay subset selection method for two-hop Wireless Sensor Networks (WSNs) where the source-relay links are modeled as time-varying fading channels. Assuming perfect channel estimation at relays, the channel state is quantized to binary levels, called as Good and Bad states, and is modeled by a Gilbert-Elliott channel. The relays compress their quantized channel state information and transmit to a fusion centre. The fusion centre selects the smallest possible subset such that each transmitted source bit is received in a Good channel state by at least one of the relays in that subset. We show through simulations that selecting this subset for relaying the information to the source, reduces the transmission power compared to a conventional all-relay transmit scheme, while maintaining the end-to-end bit error rate below a desired threshold.

We propose a power-efficient transmission scheme for wireless sensor networks. In this scheme, the sensor nodes compress their corresponding source-relay channel state information (CSI) and transmit this compressed CSI sequence along with a selected subset of their received bits from the source, which we refer to as the good bits. We assume slowly varying fading channels between source and relays and analytically derive the compression rate of the CSI sequence. We then study the relay-fusion centre link and find an optimal threshold for our proposed scheme, based on the target bit error rate and the packet delivery ratio of the network. Combining the threshold optimization and the reliable bit transmission schemes, we study the total number of transmitted bits for our proposed system. We show that for slowly varying fading channels, our proposed scheme considerably reduces the number of transmitted bits, and consequently the transmission power of the relay nodes compared with a conventional scheme where all bits are transmitted to the fusion center. We also compare the performance of our proposed scheme with a special decode and forward (SDF) scheme previously introduced in the literature. In order to have a fair comparison, we modify the SDF scheme, such that the modified scheme includes the originally proposed SDF scheme as a special case. We provide detailed comparisons and discussions on the achieved bit error rate, energy efficiency, and feasibility of the proposed and the modified SDF schemes.

We consider a wireless sensor network (WSN) with identically distributed nodes, and a two phase cooperative protocol where the source transmits and is overheard by multiple relays which in turn transmit to the destination or fusion center (FC). We introduce a selection scheme that will pick a subset of the relays that overhear the message and transmit to the FC. This scheme will aim at making the least number of relays active while minimizing the outage probability and sending the least amount of information enough to reconstruct the message at the FC. The reduced amount of information being transmitted through the network along with an even distribution of active relays leads to a more energy efficient system.

We consider a specific type of data gathering sensor networks that can be modeled by a binary chief executive officer problem. We apply turbo codes to encode sensors observations and transmit them to a fusion center over independent binary symmetric channels. It is shown in the literature that the fusion center can exploit the correlation between sensors observations to design a soft-input soft-output (SISO) global decoder. Then the fusion center iterates extrinsic information between the global decoder and the SISO decoder of the applied error correcting code to jointly estimate the source. Since we consider turbo codes, the joint decoding problem is generalized to the problem of exchanging extrinsic information between three SISO modules. In this paper, we first apply the sum-product algorithm to derive the rules that update extrinsic information for the global decoder. Then, we apply extended-serial decoding that is the best known structure for decoders consisting of three concatenated SISO modules. We compare the bit error rate achieved by extended-serial decoding with the one achieved by a separate decoding strategy, where the fusion center separately decodes each sensor's observation and then decides based on the majority of the outputs. Our simulations show that extended-serial decoding performs significantly better than separate decoding.

We consider the problem of joint decoding and data-fusion in data gathering sensor networks modeled by the Chief Executive Officer (CEO) problem. Correlation between sensorspsila data is known at the fusion center and is employed to update extrinsic information received from soft-in soft-out (SISO) decoders. It is shown in the literature that this scheme has a lower bit error rate compared with the schemes that separately decode data received from each sensor and then estimate the value of the source. Previous works consider correlated Gaussian sources and apply a single SISO decoder. We consider the binary CEO problem, where all sensors observe the same binary source corrupted by independent binary noises, and apply turbo codes to encode and transmit them to the fusion center. We show how extrinsic information is passed between SISO decoders and the vertical-decoding unit that updates extrinsic information using channel correlations. We illustrate the performance of the joint decoder for different correlations and rates. Simulation results show promising improvements compared with the separate decoding scheme. We also compare the bit error rates achieved by turbo codes with the ones achieved by convolutional codes and discuss the results.

Lossless turbo source coding employs an iterative encoding algorithm to search for the smallest codeword length that guarantees zero distortion. Although such encoder achieves promising compression rates, running the iterative algorithm for each individual message block imposes a large delay on the system. To reduce this delay, we propose a two-stage encoding algorithm for turbo source coding. We show that converging to zero distortion after a definite number of iterations, can be predicted from the earlier behavior of the distortion function. This will enable us to produce a quick, and yet sufficiently accurate, estimate of the codeword length in the first encoding stage. In the second stage, we iteratively increase this estimated codeword length until reaching zero distortion. Also, we show that employing an auxiliary distortion measure at the first stage of encoding may allow for better estimates and decrease the delay furthermore. Numerical results show that the proposed algorithm will decrease the encoding delay up to 19%. Although there are previous works in the literature on delay reduction of turbo source coding, those works achieve lower delays by reducing the message block length. However, the proposed algorithm achieves lower delays for the same block length and therefore the actual "per bit" encoding delay is decreased.

We propose a tree structured variable length random binning scheme for lossless compression of binary memoryless sources. Previously proposed source coding schemes based on nested error correcting codes can be regarded as practical implementations of this random binning scheme. For sufficiently large data blocks, we prove that the proposed scheme asymptotically achieves the entropy limit. We also derive the distribution of the compression rate achieved by the tree structured random binning scheme. Comparing this distribution with the distribution obtained using a library of random binning schemes, we show that a nested code can achieve rates close to a library of codes but with much lower encoding/decoding complexity

We propose a tree structured variable length random binning scheme that enables an error correcting code to act as a source code. The existing source coding schemes based on turbo codes, low density parity check codes, and repeat accumulate codes can be regarded as practical implementations of this random binning scheme. We investigate the performance of lossless turbo source coding relative to the proposed tree structured random binning scheme. Our numerical results show that the compression rate achieved by lossless turbo source coding is far from the tree structured random binning bound. In that, we suggest improvements to enable short block length turbo source codes to achieve compression rates close to the tree structured random binning bound.

Lossless turbo source coding with decremental redundancy is an effective approach for compressing binary sources. A large block length lossless turbo source encoder offers compression rates close to the source entropy but with large latency. In this letter, we propose a lossless compression technique for binary memoryless sources using short block length turbo codes. To achieve compression rates close to the source entropy, we modify different components of the encoder. We focus on the design of the parity interleaver for different compression rates. Also, we replace the square shape puncturing array with a rectangular shape array that allows finer puncturing and hence improved compression rates. Finally, instead of a single code, we employ many codes operating in parallel. Given these modifications, we evaluate the encoding complexity of the proposed code

Lossless turbo source coding with decremental redundancy is an effective approach for compressing binary sources. In this method, the message is encoded using a turbo code. Then the parities are heavily punctured using an iterative process and all non-punctured parities along with side information are sent to the decoder. To improve the performance, a single code can be replaced by a library of codes. The message is compressed using each code and the best result is sent to the decoder. The side information contains the number of punctured parities and the index of the applied code. Instead of transmitting the code index, we find a criterion to detect the code index using the transmitted parities, at the decoder. Compared to the case where the code index is transmitted, our method helps to achieve a better rate for short block length turbo source coders.

In this paper, we consider the application of low-density parity-check (LDPC) codes in multi-carrier frequency-hopping (MC-FH) CDMA systems. We evaluate the performance of the coded system in a slowly Rayleigh fading frequency-selective channel using different construction methods of regular LDPC codes. We then compare the results with those of the super-orthogonal coded system. Our simulation results show that the LDPC coded scheme significantly outperforms the uncoded and the super-orthogonal coded scheme. Finally, we propose a new semi-random construction of regular LDPC code and evaluate its performance in MC-FH-CDMA system. Our numerical results indicate that this new construction outperforms other regular constructions of LDPC codes.