Biological rhythms are governed by intricate interactions among oscillatory subsystems, yet how they balance functional demands and energy efficiency remains unclear. We present a bimodal coupling optimization strategy where physiological systems dynamically alternate between synchronized (energy-saving) and desynchronized (function-priority) coupling modes. By employing the water-filling principle developed in communications engineering, we prove synchronized heart rate(HR)-respiration oscillations maximize energy efficiency (oxygen uptake per cardiac work). Then, system modeling confirms task/stress-induced oxygen demands enhance oxygen uptake at the cost of desynchronization and reduced efficiency. Experiments reveal a 70.36% decrease in HR-respiration synchronization during arithmetic versus relaxation, enabling 4.43% higher oxygen uptake but with 11.38% lower energy efficiency. This bimodal coupling optimization strategy is also evident in pancreatic islets, with their insulin/glucagon oscillator alternating between in-phase (energy-saving) and anti-phase (rapid glucose reduction) coupling. This framework, integrating engineering and life sciences, reveals a universal regulatory principle for biological oscillatory systems.

To estimate physical parameters in a grey-box model with linear regressions, a two-step approach with much reduced computational complexity is developed. First, the parameters of the linear regression model are estimated via the simple linear least square method, before they are fed into a nonlinear optimization problem of a much reduced dimension. It is discovered that the right formulation of the optimization criterion depends on the input-output data, and can be expressed in terms of the singular value decomposition of the data matrix. It is also found that the estimated physical parameters can be fed back to improve the parameters of the linear regression model. This improvement is a consequence of exploiting the structural information of the system contained in the grey-box model, and thus overfitting to the limited training data can be avoided. Numerical examples are presented to demonstrate the effectiveness of the approach.

For wireless networks with multiple sources, an omnidirectional relay scheme is developed, where each node can help relay multiple sources in different directions. This is accomplished by the decode-and-forward relay strategy, with each relay binning the multiple messages to be transmitted, in the same spirit of network coding. Specially for the all-source all-cast problem, where each node is an independent source to be transmitted to all the other nodes, this scheme completely eliminates interference in the whole network, and the signal transmitted by any node is used by any other node. For networks with regular topologies, assuming no beamforming is to be performed, this omnidirectional scheme is capable of achieving the maximum achievable rate.

In the development of large wireless networks, scaling law studies can provide fundamental insights. For example, is it possible to build an arbitrarily large wireless network without a wired infrastructure while maintaining a constant communication rate for each user? This is equivalent to asking if a linear scaling law is achievable for wireless networks. Whether too ambitious a goal or not, this question has attracted intensive research but still remains open. Among many proposals, the hierarchical scheme is impressive in exploiting the MIMO gain with a bootstrapping strategy. In this paper, a careful analysis of the hierarchical scheme exposes the potential influence of the pre-constant in deriving scaling laws. It is found that a modified hierarchical scheme can achieve a throughput up to an arbitrary factor higher than the original one, although it is still short of linear scaling. This study demonstrates the essential importance of the throughput formula itself, rather than the scaling laws consequently derived.

A new approach to information-theoretic converses is proposed based on Shannon’s original sphere-packing argument. Typical sequence arguments are hardened with decoding sets to include structured codewords. Each decoding set is shown to have a minimum volume of 2 nH(Y|X) typical y-sequences in the point-to-point discrete-memoryless channel if the probability of decoding error vanishes. Since a codebook of type p(x) generates at most 2 nH(Y) typical y-sequences, the error probability is non-vanishing when R > max p(x) I(X;Y). Kolmogorov’s zero-one law is applied to prove the error probability also goes to one, unifying the weak and strong converses. In preparation for the capacity of the relay channel, i.i.d codebooks are shown via the zero-one law and a sphere-absorption argument, to exhibit a clustering property where their orbits in the y-space asymptotically coincide or separate into clusters of indistinguishable codewords. The capacity of the relay channel is shown to be ${\max _{p\left({{x_s},{x_r}}\right)}}\min \left\{ {I\left({{X_s},{X_r};{Y_d}}\right),I\left({{X_s};{Y_r}{Y_d}\mid {X_r}}\right) - \delta } \right\}$ where $\delta : = \min \left\{ {{{\left| {I\left({{{\hat Y}_r};{Y_r}\mid {X_r}{Y_d}}\right) - {C_0}} \right|}^ + },I\left({{X_s};{Y_d}\mid {X_r}{Y_r}}\right)} \right\}$, C 0 := I(X r ;Y d ), and ${\hat Y_r}$ emulates X s in a virtual source-relay channel.

What makes biological systems different from man-made systems? One distinction is explored in this paper: Biological systems achieve reliable functions through randomness, i.e., by both mitigating and exploiting the effects of randomness. The fundamental reason for biological systems to take such a random approach is the randomness of the microscopic world, which is dramatically different from the macroscopic world we are familiar with. To substantiate the idea, bacterial chemotaxis is used as an example.

A general network is studied in which messages are relayed from multiple sources to multiple destinations according to a certain hierarchical order. The framework of flow decomposition is used to show the class of regular-order decode-forward index-coding schemes is computable. A shifting algorithm finds encoding/decoding schemes in P(|N|)EXP(|S|) time that achieve desired rate-vectors, where N is the set of nodes and S ⊆ N is the subset of source nodes in the channel.

The framework of flow decomposition is proposed to unify regular encoding/decoding schemes in multisource multi-relay multi-cast channels; flows describe encoding schemes and layered partitions describe decoding schemes. Flow decomposition reveals a fundamental duality between compress-forward and decode-forward schemes with broader implications for combinatorial optimization over submodular functions. The main result proves that regular decoding schemes collectively achieve the regional cut-set extension of the one-relay decode-forward rate. The proof mimics interior-point methods in convex optimization. A shifting algorithm is used to construct a sequence of layered partitions that converges to a target rate-vector, where the number of shifts is linear in the network size. Flow decomposition inherits the benefits of regular decoding without the drawbacks of backward decoding: linear (as opposed to exponential) encoding delays and unrestricted (as opposed to strictly hierarchical) flow.

A framework based on the idea of flow decomposition is proposed to characterize the decode-forward region for general multi-source, multi-relay, all-cast channels with independent input distributions. The region is difficult to characterize directly when deadlocks occur between two relay nodes, in which both relays benefit by decoding after each other. Rate-vectors in the decode-forward region depend ambiguously on the outcomes of all deadlocks in the channel. The region is characterized indirectly in two phases. The first phase assumes relays can operate non-causally. It is shown that every rate-vector in the decode-forward region corresponds to a set of flow decompositions, which describe the messages decoded at each node with respect to the messages forwarded by all the other nodes. The second phase imposes causal restrictions on the relays. Given an arbitrary set of (possibly non-causal) flow decompositions, necessary and sufficient conditions are derived for the existence of an equivalent set of causal flow decompositions that achieves the same rate-vector region.

The decode-forward achievable region is studied for general networks. The region is subject to a fundamental tension in which nodes individually benefit at the expense of others. The complexity of the region depends on all the ways of resolving this tension. Two sets of constraints define an outer-bound on the decode-forward region: first, the conventional mutual-information inequalities implied by the one-relay channel, and second, causality constraints that ensure nodes only forward messages they have already decoded. The framework of flow decomposition is introduced to show these constraints are also sufficient. Flow decomposition provides a way of manipulating regular decode-forward schemes without the long encoding delays and restrictions on bidirectional communication of backward decoding. The two structures that define a flow decomposition are flows and layerings. Flows specify the nodes which encode messages from each source (i.e., the routes) and the encoding delays. Layerings specify the messages decoded at a specific node in the channel. We focus on two types of flow: hierarchical flow, with tree-like routes, and all-cast flow, where each route covers all nodes. For arbitrary flows of either type and any rate-vector satisfying the mutual-information constraints at a specific node, we prove there are equivalent flows and a layering that satisfy both the mutual-information and causality constraints. In separate work, we show that only the mutual-information constraints are active in channels with hierarchical flow, which implies the achievable region has minimal complexity. In channels with all-cast flow, the achievable region is computable.

The decode-forward region is characterized for general multi-source, multi-relay, all-cast channels. This region depends on the sequences of nodes that forward each message, the encoding delays in each sequence, and the messages decoded at each node. The assumption of causality creates necessary conditions on the encoding delays since nodes cannot forward messages they have not decoded. Flow decompositions are introduced to describe these dependencies. Every rate vector in the decode-forward region corresponds to a set of flow decompositions. The conditions derived from causality impose some structure on otherwise arbitrary sets of flow decompositions. Given this structure, there exists an equivalent set of flow decompositions that recovers the same region in a causal manner.

We consider the discrete memoryless symmetric primitive relay channel, where, a source X wants to send information to a destination Y with the help of a relay Z and the relay can communicate to the destination via an error-free digital link of rate R-0, while Y and Z are conditionally independent and identically distributed given X. We develop two new upper bounds on the capacity of this channel that are tighter than existing bounds, including the celebrated cut-set bound. Our approach significantly deviates from the standard information-theoretic approach for proving upper bounds on the capacity of multi-user channels. We build on the blowing-up lemma to analyze the probabilistic geometric relations between the typical sets of the n-letter random variables associated with a reliable code for communicating over this channel. These relations translate to new entropy inequalities between the n-letter random variables involved. As an application of our bounds, we study an open question posed by (Cover, 1987), namely, what is the minimum rate R-0* needed for the Z-Y link in order for the capacity of the relay channel to be equal to that of the broadcast cut. We consider the special case when the X-Y and X-Z links are both binary symmetric channels. Our tighter bounds on the capacity of the relay channel immediately translate to tighter lower bounds for R-0*. More interestingly, we show that when p -> 1/2, R-0* >= 0.1803; even though the broadcast channel becomes completely noisy as p -> 1/2 and its capacity, and therefore the capacity of the relay channel, goes to zero, a strictly positive rate R-0 is required for the relay channel capacity to be equal to the broadcast bound. Existing upper bounds on the capacity of the relay channel, and the cut-set bound in particular, would rather imply R-0* -> 0, while achievability schemes require R-0* -> 1. We conjecture that R-0* -> 1 as p -> 1/2.

An achievable rate-region for the two-way multiple-relay channel is proposed using decode-forward block Markovian coding. We identify a fundamental tension between the information flow in both directions that leads to an intractable number of decode-forward schemes and achievable rate regions, none of which are universally better than the others. We introduce a new concept in decode-forward coding called ranking, and discover that each of these rate regions are different realizations of a single expression that depends on the rank assignment. This discovery makes it possible to characterize the complete achievable rate region that includes all of the interesting decode-forward schemes and corresponding rate regions.

This paper studies the achievable rate and power allocation to improve the uplink (UL) spectrum efficiency in a Long-Term Evolution Advanced (LTE-A) cooperative cellular network with the deployment of Type-II in-band decode-and-forward (DF) relay stations (RSs). The physical-layer UL transmission technology is based on single-carrier frequency-division multiple access (SC-FDMA) with frequency-domain equalization (FDE). Different from the downlink (DL) orthogonal FDMA system, signals on all subcarriers in the SC-FDMA system are transmitted sequentially rather than in parallel; thus, the user's achievable rate is not simply the summation of the rates on all allocated subcarriers. Moreover, each user equipment (UE) device has its own transmission power constraint instead of a total power constraint at the base station in the DL case. Therefore, the UL resource allocation problem in the LTE-A system is more challenging. To this end, we first derive the achievable rates of the SC-FDMA system with two commonly used FDE techniques, namely, zero-forcing (ZF) equalization and minimum-mean-square-error (MMSE) equalization, based on the joint superposition coding for cooperative relaying. We then propose optimal power allocation schemes among subcarriers at both the UE and RS to maximize the overall throughput of the system. Both theoretical analysis and numerical results demonstrate that our proposed power allocation schemes can drastically improve system throughput.

•Resin methods were employed to remove the proteins and pigments from polysaccharides of Gentiana scabra Bunge roots.•The weight-average molecular weights of purified polysaccharides (named GSP-2 and GSP-3) were analyzed by HPGPC.•Monosaccharide compositions of polysaccharide fractions were measured by HPAEC-PAD.•GSP-3 showed significant anticoagulant activity in APTT and TT assays.

This paper investigates quality of service (QoS) provisioning for Internet of Things (IoT) in long-term evolution advanced (LTE-A) heterogeneous networks (HetNets) with partial spectrum usage (PSU). In HetNets, the IoT users with ubiquitous mobility support or low-rate services requirement can connect with macrocells (MCells), while femtocells (FCells) with PSU mechanism can be deployed to serve the IoT users requiring high-data-rate transmissions within small coverage. Despite the great potentials of HetNets in supporting various IoT applications, the following challenges exist: 1) how to depict the unplanned random behaviors of the IoT-oriented FCells and cope with the randomness in user QoS provisioning and 2) how to model the interplay of resource allocation (RA) between MCells and FCells under PSU mechanism. In this work, the stochastic geometry (SG) theory is first exploited to statistically analyze how the unplanned random behaviors of the IoT-oriented FCells impact the user performance, considering the user QoS requirements and FCell PSU policy. Particularly, to satisfy the QoS requirements of different IoT user types, the concept of effective bandwidth (EB) is leveraged to provide the users with probabilistic QoS guarantee, and a heuristic algorithm named QA-EB algorithm is proposed to make the EB determination tractable. Then, the interplay of RA between the MCells and FCells is formulated into a two-level Stackelberg game, where the two parties try to maximize their own utilities through optimizing the macro-controlled interference price and the femto-controlled PSU policy. A backward induction method is proposed to achieve the Stackelberg equilibrium. Finally, extensive simulations are conducted to corroborate the derived SINR and ergodic throughput performance of different user types and demonstrate the Stackelberg equilibrium under varying user QoS requirements and spectrum aggregation capabilities.

Consider a symmetric primitive relay channel, where, the source X wants to send information to the destination Y with the help of a relay Z, the relay Z can communicate to the destination Y via an error-free digital link of rate R 0 , and Y, Z are conditionally independent and identically distributed given X. This paper presents two new upper bounds on the capacity of such relay channels, where the first one is a sharpened version of the recently proposed bound by (Xue, 2014), and the second one is novel. These two bounds are shown to be generally tighter than the cut-set bound, and as an example they are numerically evaluated for the case of binary symmetric channels.

In this paper, a new MAC protocol for LTE over unlicensed spectrum (LTE-U) is presented that allows friendly co-existence of LTE-U with other unlicensed wireless networks, including Wi-Fi. Specifically, in a time-slotted LTE-U system, LTE- U users can transmit continuously for a period after a successful channel reservation during the spectrum sensing period. Following each LTE transmission period, a certain duration is reserved for asynchronous Wi-Fi transmissions. By adaptively adjusting the periods of LTE transmissions, Wi-Fi transmissions, and spectrum sensing, different levels of Wi-Fi protection can be achieved. Based on the proposed MAC, an analytical model is developed to study the throughput performance of both LTE-U and Wi-Fi, considering the asynchronous transmission nature of Wi-Fi within the time-slotted MAC structure. Impacts of the protocol parameters, i.e., the periods of LTE/Wi-Fi transmissions and spectrum sensing, on the throughput performance of LTE-U and Wi-Fi are also investigated. Extensive simulation results are provided to validate the analysis.