We study the incentives behind double-spend attacks on Nakamoto-style Proof-of-Work cryptocurrencies. In these systems, miners are allowed to choose which transactions to reference within their block, and a common strategy for selecting transactions is to greedily choose those with the highest fees. This can be problematic if these transactions originate from an adversary with substantial computational power (even if it is less than 50% of the total), as high-value transactions are targets for double-spend attacks. The most common mechanism for deterring double-spend attacks is for the recipients of large transactions to wait for additional block confirmations beyond the number suggested by the protocol (effectively increasing the attack cost). We argue that this defense mechanism is not satisfactory, as the security of the system is contingent on the actions of its users . Instead, defending against double-spend attacks should be the responsibility of the miners ; to this end, we propose a protocol rule under which miners limit the amount of transaction value in a block (i.e., reduce the attack reward). To demonstrate the efficacy of our proposed rule, we model cryptocurrency mining as a mean-field game in which the standard mining reward function is augmented to simulate the presence of a rational, double-spending adversary. We design and implement an algorithm which characterizes the behavior of miners at equilibrium, and we show that miners who respond to the adversary-aware reward function accumulate more wealth than those who do not. Under this reward function, the optimal strategy for honest miners is to limit the value transferred in each block such that the adversary’s expected profit is 0. Additionally, we examine Bitcoin’s resilience to double-spend attacks under our model. Assuming a six-block confirmation time, we find that a Bitcoin miner with 26% of the network mining power expects to profit from a double-spend attack.

In this paper, we consider aK-user interference channel where interference among the users is neither too strong nor too weak, a scenario that is relatively underexplored in the literature. We propose a novel deep learning-based approach to design the encoder and decoder functions that aim to maximize the sumrate of the interference channel for discrete constellations. We first consider the MaxSINR algorithm, a state-of-the-art linear scheme for Gaussian inputs, as the baseline and then propose a modified version of the algorithm for discrete inputs. We then propose a neural network-based approach that learns a non-linear constellation mapping with the objective of maximizing the sumrate. We provide numerical results to show that the constellations learned by the neural network-based approach provide enhanced alignments, not just in beamforming directions but also in terms of the effective constellation at the receiver, thereby leading to improved sum-rate performance.

The optimal coding scheme for Additive White Gaussian noise (AWGN) channels with noisy output feedback has been unknown for several decades. The best-known linear scheme is by Chance and Love, where the coefficients of the linear scheme are numerically optimized based on unique observations. In this paper, we introduce a new class of linear coding schemes, calledsequential linear schemes, where the encoder sequentially updates a linear state process based on feedback. We then derive the optimal scheme within this class, in a closed form, by formulating a novel Markov decision process and solving it via dynamic programming. We demonstrate that our scheme outperforms the Chance-Love scheme for channels with noisy feedback and coincides with the Shalkwijk-Kailath scheme for channels with noiseless feedback. This problem is an instance of decentralized controlwithout any common informationand, to the best of our knowledge, the first such scenario where we can derive analytical solutions using dynamic programming.

In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TINYTURBO exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TINYTURBO strongly generalizes across different rate, blocklengths, and trellises. We verify the reliability and efficiency of TINYTURBO via over-the-air experiments.

In this paper, we develop a framework for an autoencoder based transmission strategy for achieving distributed interference alignment and optimal power allocation in a multiuser interference channel. The users in the interference channel have access to the local channel state information only. We compare the explicit schemes, such as MaxSINR [1], against the autoencoder schemes. We find that the MaxSINR schemes outperform the autoencoder networks which are either jointly or distributively trained from scratch. However, we find that the autoencoders which are pretrained with the beamforming vectors and the power allocation obtained from the explicit schemes outperform the explicit schemes when the interference gets stronger. The explicit schemes perform well as they are effective in choosing the set of users which are to be suppressed. The pretrained autoencoders benefit from this initialization, and also from the fact that end to end training can improve their performance even further. We showcase our performance comparison results for 5 user interference channels with different levels of interference.

In this paper, we consider a communication system where a sender sends messages over a memoryless Gaussian point-to-point channel to a receiver and receives the output feedback over another Gaussian channel with known variance and unit delay. The sender sequentially transmits the message over multiple times till a certain error performance is achieved. The aim of our work is to design a transmission strategy to process every transmission with the information that was received in the previous feedback and send a signal so that the estimation error drops as quickly as possible. The optimal code is unknown for channels with noisy output feedback when the block length is finite. Even within the family of linear codes, optimal codes are unknown in general. Bridging this gap, we propose a family of linear sequential codes and provide a dynamic programming (DP) algorithm to solve for a closed form expression for the optimal code within a class of sequential linear codes. The optimal code discovered via DP is a generalized version of which the Schalkwijk-Kailath (SK) scheme is one special case with noiseless feedback; our proposed code coincides with the celebrated SK scheme for noiseless feedback settings.

In this paper, the impact of nonlinear distortions created by coherent combination on the spur-free dynamic range (SFDR) and response stability of microwave photonic (MWP) systems is studied. High SFDR is one of the key requirements in demanding modern communication networks which is usually limited by the nonlinearity of active devices. However, we demonstrate that even purely passive devices can generate nonlinear distortion when modulated optical signals coherently combine on photonic integrated circuits (PIC) or in fiber- based systems. This type of combination is more frequent in PICs since the total signal path on the chip is usually shorter than the coherence length of the laser source. Two different major types of coherent interference, Fabry-Perot (FP) and Mach-Zehnder (MZ), are considered and the induced nonlinearity is simulated for a directly intensity modulated optical signal. Two-tone method is employed to characterize intermodulation (IMD) products added to the signal. Based on the simulation results in the case of FP interference, the dynamic range of a link with 30% optical modulation index (OMI) and reflections as low as - 20 dB can be limited to 90.32 dB.Hz1/2or 118.89 dB.Hz2/3, respectively, by SFDR2or SDFR3rather than the distortions of active devices. Also, the output signal can be dominated by nonlinear distortions even higher than the fundamental signal when MZ interference happens. Finally, the response instability and environmental dependency of the link is discussed based on the simulation results.