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Characterizing the fundamental tradeoffs for maximizing energy efficiency (EE) versus spectrum efficiency (SE) is a key problem in wireless communication. In this paper, we address this problem for a point-to-point additive white Gaussian noise (AWGN) channel with the transmitter powered solely via energy harvesting from the environment. In addition, we assume a practical on-off transmitter model with non-ideal circuit power, i.e., when the transmitter is on, its consumed power is the sum of the transmit power and a constant circuit power. Under this setup, we study the optimal transmit power allocation to maximize the average throughput over a finite horizon, subject to the time-varying energy constraint and the non-ideal circuit power consumption. First, we consider the off-line optimization under the assumption that the energy arrival time and amount are a priori known at the transmitter. Although this problem is non-convex due to the non-ideal circuit power, we show an efficient optimal solution that in general corresponds to a two-phase transmission: the first phase with an EE-maximizing on-off power allocation, and the second phase with a SE-maximizing power allocation that is non-decreasing over time, thus revealing an interesting result that both the EE and SE optimizations are unified in an energy harvesting communication system. We then extend the optimal off-line algorithm to the case with multiple parallel AWGN channels, based on the principle of nested optimization. Finally, inspired by the off-line optimal solution, we propose a new online algorithm under the practical setup with only the past and present energy state information (ESI) known at the transmitter.
With recent developments of wireless communication technologies, malicious users can use them to commit crimes or launch terror attacks, thus imposing new threats on the public security. To quickly respond to defend these attacks, authorized parities (e.g., the National Security Agency of the USA) need to intervene in the malicious communication links over the air. This paper investigates this emerging wireless communication intervention problem at the physical layer. Unlike prior studies using jamming to disrupt or disable the targeted wireless communications, we propose a new physical-layer spoofing approach to change their communicated information. Consider a fundamental three-node system over additive white Gaussian noise (AWGN) channels, in which an intermediary legitimate spoofer aims to spoof a malicious communication link from Alice to Bob, such that the received message at Bob is changed from Alice's originally sent message to the one desired by the spoofer. We propose a new symbol-level spoofing scheme, where the spoofer designs the spoofing signal via exploiting the symbol-level relationship between each original constellation point of Alice and the desirable one of the spoofer. In particular, the spoofer aims to minimize the average spoofing-symbol-error-rate (SSER), which is defined as the average probability that the symbols decoded by Bob fail to be changed or spoofed, by designing its spoofing signals over symbols subject to the average transmit power constraint. By considering two cases when Alice employs the widely-used binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulations, we obtain the respective optimal solutions to the two average SSER minimization problems. Numerical results show that the symbol-level spoofing scheme with optimized transmission achieves a much lower average SSER, as compared to other benchmark schemes.
A new universal algorithm of modulation and demodulation is presented to process most signals of digital phase-related modulation schemes such as M-DPSK, MSK and even some M-FSK with low modulation level. It can easily deal with modulation and demodulation of the signals with different modulation schemes and data rates by only setting a few input arguments of the versatile software modules. The computational complexity of the algorithm is far less than that of conventional methods. The average processing capacity of the algorithm is about 15 instructions per symbol when processing DQPSK signals. It can be applied to software radios.
Random beamforming (RBF) is a practically favorable transmission scheme for multiuser multi-antenna downlink systems. This paper studies the asymptotic rates achievable with RBF in a multi-cell system subject to the inter-cell interference, by assuming that the number of users in each cell scales in a given order with the signal-to-noise ratio (SNR). In particular, we investigate the achievable degrees of freedom (DoF) for the sum-rate in each cell when the SNR goes to infinity, and characterize the achievable DoF region for all the cells. Our results show that to achieve the DoF-optimal transmission in a multi-cell system with RBF, the numbers of transmit beams in all the cells need to be assigned in a collaborative manner based on the user densities.
This paper studies the wireless two-way relay channel (TWRC), where two source nodes, S1 and S2, exchange information through an assisting relay node, R. It is assumed that R receives the sum signal from S1 and S2 in one time-slot, and then amplifies and forwards the received signal to both S1 and S2 in the next time-slot. By applying the principle of analogue network (ANC), each of S1 and S2 cancels the so-called "self-interference" in the received signal from R and then decodes the desired message. Assuming that S1 and S2 are each equipped with a single antenna and R with multi-antennas, this paper analyzes the capacity region of an ANC-based TWRC with linear processing (beamforming) at R. The capacity region contains all the achievable bidirectional rate-pairs of S1 and S2 under the given transmit power constraints at S1, S2, and R. We present the optimal relay beamforming structure as well as an efficient algorithm to compute the optimal beamforming matrix based on convex optimization techniques. Low-complexity suboptimal relay beamforming schemes are also presented, and their achievable rates are compared against the capacity with the optimal scheme.
In this paper, we study the channel estimation problem for the two-way wireless relay network (TWRN) where two terminals exchange their information through a relay node in a bi-directional manner. We derive the maximum likelihood (ML) channel estimator as well as a new estimator called the linear maximum signal-to-noise ratio (LMSNR) estimator. It is shown that our proposed methods give superior performance compared to the common channel estimators like the least-square (LS) and the linear minimum-mean-squared-error (LMMSE) in the TWRN scenario. The provided study is based on any given training sequence, while the optimal training sequence design will be presented in a separate work due to the lack of the space. Simulations are conducted to corroborate the proposed studies.
Cooperative MIMO (CMIMO) communications have transformed channel fading from a negative factor that should be mitigated into a positive factor that can be exploited in wireless networks to improve capacity. Meanwhile, CMIMO scheme has been proved to be an effective solution to improve the energy efficiency of a network. However, an inherent feature of wireless channels, unreliability caused by channel fading is neglected in related studies. In this paper, on the basis of an unreliable link model, the trade-off between the energy consumption and the transmission delay of CMIMO scheme is investigated from the viewpoint of cross-layer, and a lower bound of energy-delay tradeoff is provided which offers a deep insight into the benefits of CMIMO compared with classical multi-hop communications. Moreover, a cross-layer framework is introduced to optimize the parameters at the physical and protocol layers.
Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes.
Intelligent reflecting surface (IRS) is envisioned to play a significant role in future wireless communication systems thanks to its powerful capability of enabling smart and reconfigurable radio environment. In this paper, we study the multi-IRS aided downlink communication in a massive multiple-input multiple-output (MIMO) system, where a multi-antenna BS simultaneously serves multiple remote single-antenna users with orthogonal beams reflected by multiple IRSs. By exploiting the line-of-sight (LoS) link between each pair of selected IRSs, a multi-hop cascaded LoS link can be established between the BS and each user via their cooperative beam routing. Under this setup, we optimize the selected IRSs and their beam routing path for each user, along with the BS/IRS active/passive beamforming such that the minimum received signal power among all users is maximized, subject to a new multi-beam routing path separation constraint for avoiding the inter-user/route interference. To tackle this problem, we first derive the optimal BS/IRS active/passive beamforming in closed-form for any given beam routes and show the beam routing optimization is NP-complete by recasting it as an equivalent graph-optimization problem. To solve this challenging problem, we then propose an efficient recursive algorithm to partially enumerate the feasible solutions, which effectively balances the performance-complexity trade-off by tuning its design parameter. Numerical results demonstrate that the proposed algorithm can achieve near-optimal performance with low enumeration complexity and also outperform other benchmark schemes.
Cloud radio access network (C-RAN) has been recently proposed, in which densely deployed access points (APs) are empowered by cloud computing, to achieve enormous mobile data rates. However, close proximity of many active APs results in more severe interference and also inefficient energy consumption. To tackle this problem, we propose a joint downlink (DL) and uplink (UL) user-AP association and beamforming design in this paper to coordinate interference in the C-RAN for energy minimization. The design problem is shown to be NP hard, but exhibits an interesting "group-sparse" property. By establishing a virtual DL transmission for the original UL transmission based on the celebrated UL-DL duality result, we convert the problem to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively, and obtain an efficient solution through "group-sparse" optimization.
The use of unmanned aerial vehicles (UAVs) as aerial base stations (BSs) is of great practical significance in future wireless networks, especially for on-demand deployment during a temporary event and emergency situation. Although prior works have demonstrated the performance improvement brought by the UAV mobility, they mainly focus on the delay-tolerant applications such as file transfer and data collection. As such, it is unknown if the UAV mobility is able to provide performance gain for delay-constrained applications, such as video conferencing and online gaming. Motivated by this, we study in this paper a UAV-enabled downlink orthogonal division multiple access (OFDMA) network where a UAV is dispatched to serve two ground users within a given flight period. By taking into account the delay-specified minimum-rate-ratio constraints of the users, our goal is to maximize the minimum user throughput by jointly optimizing the UAV trajectory and communication resource allocation. We show that the max-min user throughput in general decreases as the minimum-rate-ratio constraints become more stringent, which reveals a fundamental tradeoff between the throughput gain by exploiting the UAV mobility and the user delay requirement. Simulation results verify our theoretical findings and also demonstrate the effectiveness of our proposed design.
In this paper, we study the path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the large-scale channel gain with each of its associated ground base stations (GBSs) during the flight. To this end, we propose the use of radio map that provides the information on the large-scale channel gains between each GBS and uniformly sampled locations on a three- dimensional (3D) grid over the region of interest, which are assumed to be time-invariant due to the generally static and large-size obstacles therein (e.g., buildings). Based on the given radio maps of the GBSs, we first obtain the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. To reduce the computation complexity of the optimal solution, we further propose a grid quantization method whereby the grid points in each GBS's radio map are more coarsely sampled by exploiting the spatial channel correlation over neighboring grids. Then, we solve the approximate SPP over the reduced-size radio map (graph) more efficiently. Numerical results show that the proposed solutions can effectively minimize the flying distance of the UAV subject to its communication quality constraint. Moreover, a flexible trade-off between performance and complexity can be achieved by adjusting the quantization ratio for the radio map.
