Modulo-folding ADCs (MF-ADCs) offer a potential alternative to conventional ADCs by requiring fewer bits. However, the algorithms that follow an MF-ADC typically require an unfolding method, which demands significant oversampling. In this paper, we explore the problem of symbol detection in digital communication at a receiver using an MF-ADC. We demonstrate that, in certain noisy conditions, unfolding is not necessary for detection, allowing the MF-ADC to operate at a lower rate. Additionally, we show that any unfolding process may negate the benefits of fewer bits or reduced quantization error associated with MF-ADCs. We derive theoretical bounds and discuss optimal symbol design to achieve the best performance. The proposed approach, which eliminates the need for unfolding, can facilitate the development of low-rate MF-ADCs for various other applications.

With the increasing use of high frequency communication for high bandwidths, carrier synchronization becomes a significant bottleneck, since frequency drifts need to be tracked continuously. Even conventional systems including radar and satellite communication systems with high Dopplers require complex carrier tracking solutions. Thus, well performing frequency tracking is necessary, and typically implemented using multi-bit signal processors. Such systems require more computation and incur high energy costs. One approach to reduce the complexity is to employ one-bit quantized values for carrier synchronization at the receiver. While algorithms for tracking phase using one-bit samples are available, the theory and practice for tracking frequency deviation appears yet to be developed, and this is the main contribution of the current paper. Unlike past approaches in this domain, we employ Fourier Series sum based formulae to estimate phase and frequency from one-bit quantized samples, and propose efficient, low-complexity frequency discriminators. These Fourier-based discriminators convert the frequency estimation and tracking problems into a set of equations that can be solved efficiently and are shown to be performant as confirmed by appropriate simulations.

Compressive sensing (CS) techniques for estimating the direction-of-arrival (DoA) stand apart from traditional approaches due to their ability to derive DoA information from just a single snapshot, eliminating the need for a large number of snapshots. This research addresses the challenge of adaptively choosing sensors for each snapshot during DoA tracking. We have devised a greedy algorithm for sensor selection, incorporating a submodular cost function based on our proposed deterministic prior models for DoA. Notably, we show that this selection algorithm is equally efficient compared to the conventional greedy method that relies on exact knowledge of the DOAs. We also introduce a modified version of a conventional CS-reconstruction algorithm that takes advantage of prior information to reduce the required number of measurements and computational time. We demonstrate that the tracking accuracy is improved when using the deterministic priors for sensor selection and subsequent reconstruction.

This paper presents an efficient practical Zak-OTFS modulation implementation using time and frequency windowing methods. We present two general classes of delay-Doppler (DD) twisted convolution (TC) filters (Type-1 and Type-2), and show that they can be realized by time and frequency windowing functions. We then propose practical methods to generate time domain Zak-OTFS signals, for actual transmission, using the windowing functions. For Type-1, the signals are generated using an interpolation filter. For Type-2, they are generated using a form of precoded OFDM. We show that this allows a wide variety of pulse shapes to be implemented in practice for Zak-OTFS modulation. This was not previously possible. We also show that the Type-2 signals are more spectrally efficient than their Type-1 counterparts. Finally, we compare the channel predictability of the two implementations.

In the problem of sensor selection, observations from L out of N sensors are chosen for data estimation, with the objective of minimizing an error metric. It is well known that a greedy selection (GS) method can achieve an error metric which is not worse than 1/e from that of the optimal algorithm, for a wide class of sensor selection problems. However, GS does not scale well with the problem size. The available accelerators to GS also fail to solve large scale problems in any reasonable time. In this paper, we propose a clustering-based solution called clustered greedy selection (CGS) which not only reduces the problem size, but also achieves a similar performance to GS. CGS first clusters the sensors based on a similarity metric and then applies GS on this lower dimensional problem. The method seems particularly suitable when the number of sensors to be selected is much less than the total number of sensors. We provide bounds for the worst-case performance of CGS and experimentally validate our results on some linear models.

The transmitter in a cell-free massive multiple input multiple output (MIMO) system comprises several access points (APs) which can coordinate to serve multiple user equipments. In massive MIMO systems, precoders such as conjugate beam-forming (CB), zero forcing (ZF), minimum mean square error (MMSE) etc are used in conjunction with power optimization techniques at the transmitter, in order to efficiently exploit the available degrees of freedom. However, none of these precoding techniques achieve the sum capacity of the system for all signal to noise ratios (SNRs). We model a cell free massive MIMO system as a fading Gaussian broadcast channel (GBC) with a distributed transmitter (TX). We find optimal transmission policies, in single user as well as in multi-user case, that achieve the ergodic sum capacity, thus outperforming all other precoding schemes. In the process, we obtain a novel algorithm to determine the ergodic sum capacity of the GBC and multiple access channels (MAC) based on an alternating optimization technique.

During cell devision, maintaining the epigenetic information encoded in histone modification patterns is crucial for survival and identity of cells. The faithful inheritance of the histone marks from the parental to the daughter strands is a puzzle, given that each strand gets only half of the parental nucleosomes. Mapping DNA replication and reconstruction of modifications to equivalent problems in communication of information, we ask how well enzymes can recover the parental modifications, if they were ideal computing machines. Studying a parameter regime where realistic enzymes can function, our analysis predicts that enzymes may implement a critical threshold filling algorithm which fills unmodified regions of length at most k. This algorithm, motivated from communication theory, is derived from the maximum à posteriori probability (MAP) decoding which identifies the most probable modification sequence based on available observations. Simulations using our method produce modification patterns similar to what has been observed in recent experiments. We also show that our results can be naturally extended to explain inheritance of spatially distinct antagonistic modifications.

We study the problem of simulating a two-user multiple-access channel (MAC) over a multiple access network of noiseless links. Two encoders observe independent and identically distributed (i.i.d.) copies of a source random variable each, while a decoder observes i.i.d. copies of a side-information random variable. There are rate-limited noiseless communication links between each encoder and the decoder, and there is independent pairwise shared randomness between all the three possible pairs of nodes. The decoder has to output approximately i.i.d. copies of another random variable jointly distributed with the two sources and the side information. We are interested in the rate tuples which permit this simulation. This setting can be thought of as a multi-terminal generalization of the point-to-point channel simulation problem studied by Bennett et al. (2002) and Cuff (2013). When the pairwise shared randomness between the encoders is absent, the setting reduces to a special case of MAC simulation using another MAC studied by Haddadpour et al. (2013). We establish that the presence of encoder shared randomness can strictly improve the communication rate requirements. We first show that the inner bound derived from Haddadpour et al. (2013) is tight when the sources at the encoders are conditionally independent given the side-information at the decoder. This result recovers the existing results on point-to-point channel simulation and function computation over such multi-terminal networks. We then explicitly compute the communication rate regions for an example both with and without the encoder shared randomness and demonstrate that its presence strictly reduces the communication rates. Inner and outer bounds for the general case are also obtained.

We study the problem of simulating a multiple access channel over a network of noiseless links. Two encoders observe independent and identically distributed (i.i.d.) copies of a source random variable each, while a decoder observes i.i.d. copies of a side-information random variable. There are rate-limited noiseless communication links and independent pairwise shared randomness resources between each encoder and the decoder. The decoder has to output approximately i.i.d. copies of another random variable jointly distributed with the observed random variables. This setting can be thought of as a multi-terminal generalization of the point-to-point channel simulation problem studied by Bennett et al. (2002) and Cuff (2013). General inner and outer bounds on the rate region are derived. For the special case when the sources at the encoders are conditionally independent given the side-information at the decoder, we completely characterize the rate region. Our bounds recover the existing results on deterministic function computation over such multi-terminal networks. We then show through an example that an additional independent source of shared randomness between the encoders that is not available to the decoder strictly improves the communication rates.

The capacity region of several multiuser models in information theory can be enlarged by utilizing feedback of the received symbols. This is in contradiction to the discrete memoryless case, where feedback is known not to change the capacity. In this paper, we consider two broadcast models with noisy feedback from both the receivers. The models are derived from a standard memoryless scalar GBC, where two intermediate passive nodes are assumed to be observing the transmissions via separate noisy links corrupted by independent AWGN. In our first model, the scalar output from each intermediate node is passed through two additional independent AWGN links, called feedback and forward links. The output of the feedback link is observed by the transmitter as feedback, whereas only the forward link is observed by the corresponding decoder. We derive conditions that are both necessary and sufficient for feedback to enlarge the capacity region. In the second model, the two outputs of a standard GBC are observed by the respective decoders, but the transmitter observes the sum of the symbols at the receivers using causal feedback. We show that such a feedback has no effect on the capacity region.

It is well known that the capacity region of an average transmit power constrained Gaussian Broadcast Channel (GBC) with independent noise realizations at the receivers is enlarged by the presence of causal noiseless feedback. When the noise variances at the receivers are identical, even passive feedback via independent memoryless Gaussian links can lead to a capacity region enlargement. The last fact remains true even when the feedback noise variance is very high, and available only from one of the receivers. While such capacity enlargements are feasible for several other feedback models in the Gaussian BC setting, it is also known that feedback does not change the capacity region for physically degraded broadcast channels. In this paper, we consider a two user GBC with independent noise realizations at the receivers, where the feedback links from the receivers are corrupted by independent additive Gaussian noise processes. We investigate the set of four noise variances, two forward and two feedback, for which no capacity enlargement is possible. A sharp characterization of this region is derived, i.e., any quadruple outside the presented region will lead to a capacity enlargement, whereas quadruples inside will leave the capacity region unchanged. Our results lead to the conclusion that when the forward noise variances are different, too noisy a feedback from one of the receivers alone is not always beneficial for enlarging the capacity region, be it from the stronger user or the weaker one, in sharp contrast to the case of equal forward noise variances.

We consider a strong coordination setup, where two nodes must produce a joint distribution on their actions that is close in total variation distance to independent and identical copies from a given joint probability distribution. The first node, which we call the encoder, observes an independent and identically distributed (i.i.d.) source. In order to coordinate the source with the reconstructed outputs of the second node (the decoder), they have access to a noiseless rate limited link and common randomness. The decoder also has additional side information. The reconstruction at the decoder is to be coordinated with the source process as well as the available side information. We allow the side information to be driven by another encoding process, which does not share common randomness with the two nodes. General inner and outer bounds on the rate-coordination region for this set up are derived, and our bounds match for an important special case. We also show an example with no encoding of the side information, where coordination of the source and reconstruction can be obtained as a union of three way coordination regions involving the side information as well.

Feedback is known to enlarge the capacity region of a Gaussian Broadcast Channel (GBC) with independent noise realizations at the receivers, and an average power constraint at the transmitter. The capacity enlargement may occur even when there is noisy feedback from only one of the two receivers. However, recent results show the existence of a feedback noise threshold, beyond which one-sided feedback from only the stronger receiver is futile in enlarging the capacity region. The current paper presents a tight characterization of the feedback noise threshold, which separates the regimes where feedback from only the stronger receiver enlarges the capacity or leaves it unchanged. The scheme used to prove this result also leads to some interesting observations on noisy feedback from only the weak receiver.

The mesosphere-stratosphere-troposphere (MST) radar is a very high frequency (VHF) pulsed coherent Doppler radar used for ground-based remote sensing. In an MST radar, the backscattered pulses due to fluctuations in atmospheric refractive index are observed at the receiver. Echoes from higher altitudes are typically weaker, resulting in a low signal-to-noise ratio (SNR). They could also be contaminated with clutter and interference, making conventional spectral estimation techniques in the Fourier domain unreliable. In this article, we propose an algorithm to estimate the atmospheric wind profile using a dynamic programming approach employing the Viterbi data association (VDA) algorithm. Progressively for each range bin, the proposed algorithm chooses a set of probable wind speeds as nodes in a trellis. A branch metric incorporating the kinematic parameters inclusive of the differential wind shear is then constructed for state transitions within the trellis, from nodes in one range bin to the subsequent ones. A dynamic programming approach then chooses the optimal wind profile across altitudes. The proposed algorithm is experimented on simulated as well as real MST radar data sets to verify its robustness. Our experiments show that the suggested method has improved performance when compared to previously established techniques.

In an action dependent state channel (ADSC), there are two encoders, viz. an action encoder and a channel encoder. We consider a Gaussian ADSC (GADSC) setup where the state process is generated by passing the action symbols through an AWGN channel. The receiver observes the superposition of the channel encoder symbols, state process, and independent AWGN.In our model, in addition to decoding the messages, the receiver is required to produce an estimate of the state process within some prescribed mean-squared error distortion. While joint state estimation and communication for a discrete memoryless ADSC with strictly causal state information has been solved, its non-causal counterpart remains open. We resolve this for the GADSC under average power constraints at the two encoders. Furthermore, we allow an additional independent message stream at the channel encoder and characterize the optimal distortion-rate trade-off region. Interestingly, our results also characterize the capacity region of a state-dependent Gaussian multiple access channel (MAC) with degraded message sets and state estimation constraints.

In a coherent time-varying MIMO multiple access channel (MAC), the ergodic sum-capacity can be achieved by suitable power control, which adapts the transmit covariance matrices based on the available channel state information (CSI). We consider a MIMO-MAC model where each transmitter is only aware of its own fading realization. For this distributed MAC, we propose decentralized power control schemes. First, some key structural properties on the optimal power control laws are derived, followed by an upper bound on the sum-capacity. Numerical results show that the proposed power control schemes closely characterize the sum-capacity in several regimes of interest.

A hybrid communication network with a common analog source signal and independent digital data streams at the transmitters of a multiple access network is considered. The receiver has to estimate the analog signal samples with a given fidelity, and decode the digital streams with a low error probability. The main goal of this paper is to characterize the optimal tradeoff between the mean-squared error distortion in source estimation and the data rates available to each user. To this end, we consider a Gaussian multiple access channel (GMAC) setup with additive state, where the state is nothing but a scaled version of the source process itself. The state process is assumed to be non-causally available to all the transmitting nodes. The problem now becomes that of the joint state estimation and message communication in a GMAC with state. We provide a complete characterization of the optimal distortion-rate tradeoff for an N - sender GMAC. Our results show that, similar to the single-user results, it is optimal to amplify the state using uncoded transmissions, whereas the digital streams are superposed using appropriate Gaussian codebooks in conjunction with dirty paper coding (DPC). Since the variance of the additive state is controlled by a scaling factor in our model, we also recover the results for communicating a common source and independent messages over a GMAC without state as a special case.

A common reconstruction (CR) problem, where the common additive state to a Gaussian broadcast channel (BC) is to be estimated at two receivers, is considered. The state process is assumed to be IID Gaussian, and known non-causally at the encoder. Each receiver has to make separate estimates of the state-process, with the CR constraints that the individual receiver's estimate should match a corresponding estimate at the transmitter. We study the trade-offs between the two distortions and a private rate to the strong receiver. We compute inner and outer bounds which are numerically shown to characterize the optimal performance in several regimes of interest. Interestingly, it is observed that allowing the weak user to decode part of the private message to the stronger user helps the distortion trade-offs, even though the objective concerns only a private rate to the strong user. Also, as a special case of our BC results, we show that Gaussian auxiliaries are optimal for a single user Gaussian CR problem.

A time-slotted multiple access system with bursty data arrivals to the terminals is considered, where variable sized packets independently arrive in each slot at every transmitter. Each packet is required to be delivered to a common receiver within a certain number of slots specified by a maximum delay constraint. The terminals know only their own packet arrival process, i.e., the arrivals at the rest of the terminals are unknown to each transmitter, except for their probability distributions. For this interesting distributed multiple access model, we design novel online communication schemes which transport the arriving data without any outage, while respecting the delay constraint. In particular, the users choose their respective transmit powers in a distributed manner, ensuring at the same time that the joint power vector is sufficient to support the distributed choice of data rates employed in that slot. The proposed schemes are not only optimal in minimizing the average transmit sum power, but they also considerably outperform conventional orthogonal multiple access techniques like time-division multiple access. An optimal scheme for a multiple access channel with arrivals and time-varying fading is also presented, under a unit slot delay constraint.

A two-way half-duplex communication model is considered, where two nodes want to exchange a fixed number of bits with each other, and both nodes are powered by energy harvesting (EH) sources. The problem of minimizing the sum of the time required to send the required bits in both the directions is considered. The model also includes the processing cost at each node, that models the power needed for nodes to stay powered on during transmission. In the offline setting, where the EH arrival profile is known non-causally, an iterative algorithm based on alternating maximization is shown to be optimal. In the more realistic setting of causal knowledge of the EH arrival profile, an online algorithm is shown to be optimal in terms of the competitive ratio and the optimal competitive ratio is shown to be 2.

It is well known that, in general, feedback may enlarge the capacity region of Gaussian broadcast channels. This has been demonstrated even when the feedback is noisy (or partial-but-perfect) and only from one of the receivers. The only case known where feedback has been shown not to enlarge the capacity region is when the channel is physically degraded. In this paper, we show that for a class of two-user Gaussian broadcast channels (not necessarily physically degraded), passively feeding back the stronger user's signal over a link corrupted by Gaussian noise does not enlarge the capacity region if the variance of feedback noise is above a certain threshold.

The problem of finding an optimal sensing schedule for a mobile device that encounters an intermittent WiFi access opportunity is considered. At any given time, the WiFi is in any of the two modes, ON or OFF, and the mobile's incentive is to connect to the WiFi in the ON mode as soon as possible, while spending as little sensing energy. We introduce a dynamic programming framework which enables the characterization of an explicit solution for several models, particularly suitable when the OFF periods are exponentially distributed. While the problem for non-exponential OFF periods is ill-posed in general, a usual workaround in literature is to make the mobile device aware if one ON period is completely missed. In this restricted setting, using the DP framework, the deterministic nature of the optimal sensing policy is established, and value iterations are shown to converge to the optimal solution. Finally, we address the blind situation where the distributions of ON and OFF periods are unknown. A continuous bandit based learning algorithm that has vanishing regret (loss compared to the optimal strategy with the knowledge of distributions) is presented, and comparisons with the optimal schemes are provided for exponential ON and OFF periods.

Traditionally, the capacity region of a coherent fading multiple access channel (MAC) is analyzed in two popular contexts. In the first, a centralized system with full channel state information at the transmitters (CSITs) is assumed, and the transmit power and data-rate can be jointly chosen for every fading vector realization. On the other hand, in fast-fading links with distributed CSIT, the lack of full CSI is compensated by performing ergodic averaging over sufficiently many channel realizations. Notice that the distributed CSI may necessitate decentralized power-control for optimal data-transfer. Apart from these two models, the case of slow-fading links and distributed CSIT, though relevant to many systems, has received much less attention. In this paper, a block-fading additive white Gaussian noise MAC with full CSI at the receiver and distributed CSI at the transmitters is considered. The links undergo independent fading, but otherwise have arbitrary fading distributions. The channel statistics and respective long-term average transmit powers are known to all parties. We first consider the case where each encoder has knowledge only of its own link quality, and not of others. For this model, we compute the adaptive capacity region, i.e., the collection of average rate-tuples under blockwise coding/decoding such that the rate-tuple for every fading realization is inside the instantaneous MAC capacity region. The key step in our solution is an optimal rate allocation function for any given set of distributed power control laws at the transmitters. This also allows us to structurally characterize the optimal power control for a wide class of fading models. Further extensions are also proposed for the case where each encoder has additional partial CSI about the other links.

We consider a multiple access system (MAC) with bursty arrivals. The transmissions are grouped into slots and the users are frame-synchronized. At the start of each time slot, variable sized packets independently arrive at each of the transmitting terminals. The packets are to be delivered to a common receiver by the end of the slot. Each terminal knows only its own arrival process, i.e. the packet-sizes at the rest of the terminals are unknown to each transmitter. The respective link gains from the transmitters to the receiver are assumed to be fixed and known to all. In this random access system, we propose schemes which will deliver the arriving data without any outage, under strict delay constraints. Our schemes are optimal in minimizing the total average power spent in data transport.

We consider a block-fading Gaussian MAC under a local CSI model where the transmitters have access to their own fading states. The system requires that the joint transmission rate-vector should not be in outage in any block. The average rate-tuples that can be achieved in fading MAC under such local distributed CSI and outage-free transmission belong to the so called adaptive capacity region. We present the adaptive capacity region of MACs under fairly general fading distributions and local CSI. Our results considerably generalize the known sum-capacity solutions in literature, by evaluating the full capacity region. Our results also provide the adaptive capacity region for arbitrary given power allocation functions. We further extend our results to more general CSI models where each user has additional quantized CSI of the other links.

We consider a distributed MAC setting with block-wise flat fading links and full receiver CSI (channel state information). Of the L transmitters, a subset is assumed to have knowledge of the global CSI vector in each block, whereas the remaining users have access only to their respective link qualities, i.e. each one in the latter set is unaware of the quality of other links. Outage is not allowed in any communication block. We propose efficient power-allocation and rate-adaptation strategies which are sum-rate optimal when users in each subset observe identical fading distributions chosen from a class, which includes the popular Rayleigh, Ricean etc.

We consider a two-user block-fading MAC with distributed channel state information (CSI), where each user has access to only its own fading coefficients. The average rate-pairs of communication while employing within-block coding is known as the adaptive capacity region, where each user adapts the rate based on its perceived link gain. We evaluate the adaptive sum-capacity of MAC channels with general fading distributions, for discrete as well as continuous valued ones.

We consider the problem of finding optimal, fair and distributed power-rate strategies to achieve the sum capacity of the Gaussian multiple-access block-fading channel. The transmitters have access to only their own fading coefficients, while the receiver has access to all of the fading coefficients. We propose a distributed strategy called the `midpoint' strategy which is optimal when the system cannot tolerate outage. In addition, we demonstrate a successive decoding scheme that can achieve this maximal sum-rate. In presence of outage, we show that the strategies based on a single threshold are suboptimal.

We consider a multiplicative multiple access channel in the presence of additive white Gaussian noise. Under individual average power constraints at each of the transmitters, we characterize the capacity region of this channel. The structure of the region reveals some fundamental characteristics related to timesharing, power constraints and the auxiliary random variables present in the converse theorems. As an example, it is shown that to achieve the capacity region of a two user multiplicative MAC, time-sharing of 3 strategies/rate-pairs are required, as opposed to the sufficiency of time-sharing between at most 2 rate-pairs in a discrete memoryless MAC.