In LoRa (Long Range), when a collision occurs in the network, each end-device has to retransmit its colliding frame, which reduces the throughput, and increases the energy consumption of the end-devices and the delay of the frames. In this paper, we propose an algorithm to decode colliding synchronized LoRa signals and thus improve the overall performance of the network. Indeed, we use successive transmissions of bitmaps by the end-devices to determine the correct symbols of each colliding frame, instead of retransmitting the whole frames. Simulation results show that our algorithm is able to significantly improve the overall throughput of LoRaWAN, and to decrease the energy consumption and the delay of the transmitters.
In @cite_8 , the authors have proposed two algorithms to decode some cases of collisions of LoRa signals. The first algorithm is used when superposed signals are slightly desynchronized, and the second algorithm is used when superposed signals are completely synchronized. Authors observe that the first algorithm is able to significantly increase the throughput, by decoding many collisions of two signals, and some collisions of three signals. On the other side, the second algorithm has improved the throughput, by decoding both signals when exactly two signals are colliding. The second algorithm requests any of the two colliding transmitters to retransmit its frame. Hence, when one frame is retransmitted, the algorithm is able to decode it, and to deduce the other colliding frame by elimination. The authors have considered the case of only two synchronized signals. In this paper, we are improving this by reducing the amount of data retransmitted, and by considering two or more synchronized signals.
@cite_8
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Long-range low-power wireless communications, such as LoRa, are used in many IoT and environmental monitoring applications. They typically increase the communication range to several kilometers, at the cost of reducing the bitrate to a few bits per seconds. Collisions further reduce the performance of these communications. In this paper, we propose two algorithms to decode colliding signals: one algorithm requires the transmitters to be slightly desynchronized, and the other requires the transmitters to be synchronized. To do so, we use the timing information to match the correct symbols to the correct transmitters. We show that our algorithms are able to significantly improve the overall throughput of LoRa. Polar codes, as the first provable capacity-achieving error-correcting codes, have received much attention in recent years. However, the decoding performance of polar codes with traditional successive-cancellation (SC) algorithm cannot match that of the low-density parity-check or Turbo codes. Because SC list (SCL) decoding algorithm can significantly improve the error-correcting performance of polar codes, design of SCL decoders is important for polar codes to be deployed in practical applications. However, because the prior latency reduction approaches for SC decoders are not applicable for SCL decoders, these list decoders suffer from the long-latency bottleneck. In this paper, we propose a multibit-decision approach that can significantly reduce latency of SCL decoders. First, we present a reformulated SCL algorithm that can perform intermediate decoding of 2 b together. The proposed approach, referred as 2-bit reformulated SCL ( 2b-rSCL ) algorithm , can reduce the latency of SCL decoder from ( @math ) to ( @math ) clock cycles without any performance loss. Then, we extend the idea of 2-b-decision to general case, and propose a general decoding scheme that can perform intermediate decoding of any @math bits simultaneously. This general approach, referred as @math -bit reformulated SCL ( @math b-rSCL ) algorithm , can reduce the overall decoding latency to as short as @math cycles. Furthermore, on the basis of the proposed algorithms, very large-scale integration architectures for 2b-rSCL and 4b-rSCL decoders are synthesized. Compared with a prior SCL decoder, the proposed (1024, 512) 2b-rSCL and 4b-rSCL decoders can achieve 21 and 60 reduction in latency, 1.66 and 2.77 times increase in coded throughput with list size 2, and 2.11 and 3.23 times increase in coded throughput with list size 4, respectively. This paper provides new results on computing simultaneous sparse approximations of multichannel signals over redundant dictionaries using two greedy algorithms. The first one, p-thresholding, selects the S atoms that have the largest p-correlation while the second one, p-simultaneous matching pursuit (p-SOMP), is a generalisation of an algorithm studied by Tropp in (Signal Process. 86:572–588, 2006). We first provide exact recovery conditions as well as worst case analyses of all algorithms. The results, expressed using the standard cumulative coherence, are very reminiscent of the single channel case and, in particular, impose stringent restrictions on the dictionary. A new viewpoint for adopting the lattice reduction in communication over multiple-input multiple-output (MIMO) broadcast channels is introduced. Lattice basis reduction helps us to reduce the average transmitted energy by modifying the region which includes the constellation points. The new viewpoint helps us to generalize the idea of lattice-reduction-aided (LRA) preceding for the case of unequal-rate transmission, and obtain analytic results for the asymptotic behavior (signal-to-noise ratio (SNR) rarr infin) of the symbol error rate for the LRA precoding and the perturbation technique. Also, the outage probability for both cases of fixed-rate users and fixed sum rate is analyzed. It is shown that the LRA method, using the Lenstra-Lenstra-Lovasz (LLL) algorithm, achieves the optimum asymptotic slope of symbol error rate (called the precoding diversity). This paper introduces a novel technique for access by a cognitive Secondary User (SU) to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique allows the SU to perform selective retransmissions of previously corrupted SU data packets. The temporal redundancy introduced by the primary ARQ protocol and by the selective SU retransmission process can be exploited by the SU receiver to perform Interference Cancellation (IC) over the entire interference pattern, thus creating a "clean" channel for the decoding of the concurrent message. The chain decoding technique, initiated by a successful decoding operation of a SU or PU message, consists in the iterative application of IC, as previously corrupted messages become decodable. Based on this scheme, we design an optimal policy that maximizes the SU throughput under a constraint on the average long-term PU throughput degradation. We show that the optimal policy can be found by first optimizing the SU access policy using a Markov Decision Process formulation, and then applying a chain decoding protocol defined by five basic rules. Such an approach enables a compact state representation of the protocol, and its efficient numerical optimization. Finally, we show by numerical results the throughput benefit of the proposed technique. Motivated by cognitive radio applications, we consider mitigating the effect of interference by exploiting known properties about its signal structure. Specifically, we analyze communication between a source and destination with an interferer that induces random variations in the source-destination channel. The interferer transmits a sequence chosen uniformly from a randomly generated codebook, which has an i.i.d. structure, or a superposition structure. It is assumed that both the encoder and decoder know the interferer's codebook. We first provide a definition of capacity for these settings. When the encoder knows the interferer's message noncausally, it can use Gel'fand-Pinsker (GP) encoding to precode against interference. Alternatively, it can encode by taking into account that the interference is a codeword, to enable the decoder to decode both messages. It is demonstrated by an example that the latter can outperform GP encoding. Two upper bounds to the performance of this channel are then presented. Next, a more realistic scenario is considered in which the interference is learned at the cognitive encoder causally through a noisy channel. It is shown that for the case of i.i.d. generated interference, this information has no value. In contrast, when the interference is a codeword from an i.i.d. generated codebook, this fact can be exploited to obtain higher rates between the cognitive pair. We study the approximate message-passing decoder for sparse superposition coding on the additive white Gaussian noise channel and extend our preliminary work. We use heuristic statistical-physics-based tools, such as the cavity and the replica methods, for the statistical analysis of the scheme. While superposition codes asymptotically reach the Shannon capacity, we show that our iterative decoder is limited by a phase transition similar to the one that happens in low density parity check codes. We consider two solutions to this problem, that both allow to reach the Shannon capacity: 1) a power allocation strategy and 2) the use of spatial coupling, a novelty for these codes that appears to be promising. We present, in particular, simulations, suggesting that spatial coupling is more robust and allows for better reconstruction at finite code lengths. Finally, we show empirically that the use of a fast Hadamard-based operator allows for an efficient reconstruction, both in terms of computational time and memory, and the ability to deal with very large messages. Recently, it has been shown that carrier-sense multiple access (CSMA)-type random access algorithms can achieve the maximum possible throughput in ad hoc wireless networks. However, these algorithms assume an idealized continuous-time CSMA protocol where collisions can never occur. In addition, simulation results indicate that the delay performance of these algorithms can be quite bad. On the other hand, although some simple heuristics (such as greedy maximal scheduling) can yield much better delay performance for a large set of arrival rates, in general they may only achieve a fraction of the capacity region. In this paper, we propose a discrete-time version of the CSMA algorithm. Central to our results is a discrete-time distributed randomized algorithm that is based on a generalization of the so-called Glauber dynamics from statistical physics, where multiple links are allowed to update their states in a single timeslot. The algorithm generates collision-free transmission schedules while explicitly taking collisions into account during the control phase of the protocol, thus relaxing the perfect CSMA assumption. More importantly, the algorithm allows us to incorporate heuristics that lead to very good delay performance while retaining the throughput-optimality property. We present an algorithm that synchronizes two short video sequences where an object undergoes ballistic motion against stationary scene points. The object’s motion and epipolar geometry are exploited to guide the algorithm to the correct synchronization in an iterative manner. Our algorithm accurately synchronizes videos recorded at different frame rates, and takes few iterations to converge to sub-frame accuracy. We use synthetic data to analyze our algorithm’s accuracy under the influence of noise. We demonstrate that it accurately synchronizes real video sequences, and evaluate its performance against manual synchronization. This paper presents a new desynchronization algorithm aimed at providing collision-free transmission scheduling for single-hop and acyclic multi-hop wireless sensor networks. The desynchronization approach is resilient to the hidden terminal problem and topology changes. Each node distributively converges upon a single collision-free transmission slot, utilizing only minimal neighbor information. In addition, we propose two strategies which facilitate increased convergence time. We evaluate the proposed algorithm via simulations over a range of network densities on both single-hop and acyclic multi- hop networks. Convergence and throughput comparison are performed against two previously proposed desynchronization algorithms. Finally, using an experimental tested of TelosB motes we verify the performance, practicality, and correctness of the desynchronization algorithm on varying network topologies. In 2010, Tao Xie and Dengguo Feng [XF10] constructed the rst single-block collision for MD5 consisting of two 64-byte messages that have the same MD5 hash. Details of their attack, developed using what they call an evolutionary approach, has not been disclosed security reasons". Instead they have posted a challenge to the cryptology community to nd a new dierent single-block collision attack for MD5. This paper answers that challenge by presenting a single-block collision attack based on other message dierences together with an example colliding message pair. The attack is based on a new collision nding algorithm that exploits the low number of bitconditions in the rst round. It uses a new way to choose message blocks that satisfy bitconditions up to step 22 and additionally uses three known tunnels to correct bitconditions up to step 25. The attack has an average runtime complexity equivalent to 2 49:8 calls to MD5’s compression function. Two new schemes are proposed for collision resolution in slotted ALOHA networks based on multi-user physical-layer network coding (MU PHY NC). In the proposed random access schemes, a collision of a generic number of packets can be recovered decoding the XOR of the original messages, such that the signal resulting from the collision is exploited rather than being discarded. Two different schemes that differ in terms of the amount of control information that needs to be transmitted from the access point, are studied. In this paper we introduce a novel linear precoding technique. The approach used for the design of the precoding matrix is general and the resulting algorithm can address several optimization criteria with an arbitrary number of antennas at the user terminals. We have achieved this by designing the precoding matrices in two steps. In the first step we minimize the overlap of the row spaces spanned by the effective channel matrices of different users using a new cost function. In the next step, we optimize the system performance with respect to specific optimization criteria assuming a set of parallel single- user MIMO channels. By combining the closed form solution with Tomlinson-Harashima precoding we reach the maximum sum-rate capacity when the total number of antennas at the user terminals is less or equal to the number of antennas at the base station. By iterating the closed form solution with appropriate power loading we are able to extract the full diversity in the system and reach the maximum sum-rate capacity in case of high multi-user interference. Joint processing over a group of multi-user MIMO channels in different frequency and time slots yields maximum diversity regardless of the level of multi-user interference. We present a randomized distributed algorithm that in radio networks with collision detection broadcasts a single message in O(D + log6 n) rounds, with high probability. This time complexity is most interesting because of its optimal additive dependence on the network diameter D. It improves over the currently best known O(Dlogn D + log2 n) algorithms, due to Czumaj and Rytter [FOCS 2003], and Kowalski and Pelc [PODC 2003]. These algorithms where designed for the model without collision detection and are optimal in that model. However, as explicitly stated by Peleg in his 2007 survey on broadcast in radio networks, it had remained an open question whether the bound can be improved with collision detection. We also study distributed algorithms for broadcasting k messages from a single source to all nodes. This problem is a natural and important generalization of the single-message broadcast problem, but is in fact considerably more challenging and less understood. We show the following results: If the network topology is known to all nodes, then a k-message broadcast can be performed in O(D + klog n + log2 n) rounds, with high probability. If the topology is not known, but collision detection is available, then a k-message broadcast can be performed in O(D + klog n + log6 n) rounds, with high probability. The first bound is optimal and the second is optimal modulo the additive O(log6 n) term. This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base stations (BSs) are connected to a cloud-computing-based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users’ messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in the C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Furthermore, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution. We study the problem of synchronization of two remotely located data sources, which are mis-synchronized due to deletions and insertions. This is an important problem since a small number of synchronization errors can induce a large Hamming distance between the two sources. The goal is to effect synchronization with the rate-efficient use of lossless bidirectional links between the two sources. In this work, we focus on the following model. A binary sequence X of length n is edited to generate the sequence at the remote end, say Y, where the editing involves random deletions and insertions, possibly in small bursts. The problem is to synchronize Y with X with minimal exchange of information (in terms of both the average communication rate and the average number of interactive rounds of communication). We focus here on the case where the number of edits is much smaller than n, and propose an interactive algorithm which is computationally simple and has near-optimal communication complexity. Our algorithm works by efficiently splitting the source sequence into pieces containing either just a single deletion insertion or a single burst deletion insertion. Each of these pieces is then synchronized using an optimal one-way synchronization code, based on the single-deletion correcting channel codes of Varshamov and Tenengolts (VT codes). We consider the 2 2 2 multiple-input multipleoutput interference channel where two source-destination pairs wish to communicate with the aid of two intermediate relays. In this paper, we propose a novel lattice strategy called Aligned Precoded Compute-and-Forward (PCoF). This scheme consists of two phases: 1) Using the CoF framework based on signal alignment we transform the Gaussian network into a deterministic finite field network. 2) Using linear precoding (over finite field) we eliminate the end-to-end interference in the finite field domain. Further, we exploit the algebraic structure of lattices to enhance the performance at finite SNR, such that beyond a degree of freedom result (also achievable by other means). We can also show that Aligned PCoF outperforms time sharing in a range of reasonably moderate SNR, with increasing gain as SNR increases. Abstract This paper advocates that for some multimodal tasks involving more than one stream of data representing the same sequence of events, it might sometimes be a good idea to be able to desynchronize the streams in order to maximize their joint likelihood. We thus present a novel Hidden Markov Model architecture to model the joint probability of pairs of asynchronous sequences describing the same sequence of events. An Expectation–Maximization algorithm to train the model is presented, as well as a Viterbi decoding algorithm, which can be used to obtain the optimal state sequence as well as the alignment between the two sequences. The model was tested on two audio–visual speech processing tasks, namely speech recognition and text-dependent speaker verification, both using the M2VTS database. Robust performances under various noise conditions were obtained in both cases. We study the problem of synchronizing two files X and Y at two distant nodes A and B that are connected through a two-way communication channel. We assume that file Y at node B is obtained from file X at node A by inserting and deleting a small fraction of symbols in X. More specifically, we consider the case where X is a non-binary non-uniform string, and deletions and insertions happen uniformly with rates βd and βi, respectively. We propose a synchronization protocol between node A and node B that needs to transmit O(q H2(βd+βi)n log 1 βd+βi) bits (where n is the length of X, q is the alphabet size and H2 is the collision entropy of X) and reconstructs X at node B with error probability exponentially low in n. This protocol readily generalizes the recent result by Tabatabaei Yazdi and Dolecek that dealt with synchronization from binary uniform source and under only deletion errors. This paper investigates the iterative decoding for spectrally efficient two-way relay (TWR) communications in which two end nodes exchange information through a relay (R). With the use of physical-layer network coding (PNC), information exchange throughput can be significantly improved as only two orthogonal time slots (TS) are required to realize both the multiple access (MAC) and the broadcast (BC) transmissions. During the MAC stage, transmitted symbols from the end nodes are superimposed at R which decodes the exclusive-or (XOR) information of the two end nodes. However, with a high order modulation, symbol superposition will cause demapping ambiguity. This paper proposes a TWR communication system that employs the bit-interleaved coded modulation (BICM). The iterative decoding, namely, the BICM-ID, is introduced to dissolve the demapping ambiguity. With iterative decoding, the transmitted symbol pair probabilities can be better estimated so that the demapper can provide better coded bit information for the decoder. The proposed work generalizes the iterative demapping-decoding approach for TWR channels. Our simulation results demonstrate significant iterative decoding gains can be achieved with a tendency of approaching the XOR-decoding bound.