In this paper, we propose a novel antenna architecture called movable antenna (MA) to improve the performance of wireless communication systems. Different from conventional fixed-position antennas (FPAs) that undergo random wireless channel variation, the MAs with the capability of flexible movement can be deployed at positions with more favorable channel conditions to achieve higher spatial diversity gains. To characterize the general multi-path channel in a given region or field where the MAs are deployed, a field-response model is developed by leveraging the amplitude, phase, and angle of arrival/angle of departure (AoA/AoD) information on each of the multiple channel paths under the far-field condition. Based on this model, we then analyze the maximum channel gain achieved by a single receive MA as compared to its FPA counterpart in both deterministic and stochastic channels. First, in the deterministic channel case, we show the periodic behavior of the multi-path channel gain in a given spatial field, which can be exploited for analyzing the maximum channel gain of the MA. Next, in the case of stochastic channels, the expected value of an upper bound on the maximum channel gain of the MA in an infinitely large receive region is derived for different numbers of channel paths. The approximate cumulative distribution function (CDF) for the maximum channel gain is also obtained in closed form, which is useful to evaluate the outage probability of the MA system. Moreover, our results reveal that higher performance gains by the MA over the FPA can be acquired when the number of channel paths increases due to more pronounced small-scale fading effects in the spatial domain. Numerical examples are presented which validate our analytical results and demonstrate that the MA system can reap considerable performance gains over the conventional FPA systems with/without antenna selection (AS).
Mobile data services are becoming the main driver of a wireless service provider's (WSPs) revenue growth, and two-part tariff data plans (each including a lump-sum fee and a per-unit charge) are usually provided to wireless users. Some users can easily use up their monthly data quota and may pay for costly data over-usage. Motivated by users' diverse usage behavior (more or less than the subscribed data quotas), this paper proposes a new type of user-initiated network for cellular users to trade data plans by leveraging personal hotspots (PHs) with users' smartphones. A user with data surplus can set up a PH and share the cellular data connection to another user with data deficit in the vicinity. Due to users' randomness in data usage, incentive to trade, and user mobility to enter or leave the PH connection range, the analysis on the secondary trading market is challenging. To overcome these issues, we propose a PH-market for users with diverse data usage behaviors and random user mobility to directly trade data as sellers and buyers, by designing a market-clearing price. It is shown that the PH-market greatly saves all users' expected costs when the existence condition of the PH-market is met. Finally, as this PH-market will challenge the WSP's revenue collection (especially the surcharge from users' data over-usage), we analyze the WSP's response to the PH-market and propose two effective countermeasure strategies by either reducing the selling users' data quota in their data plans (the PH-market's supply) or increasing the buying users' data quota (the PH-market's demand). When we have more than one WSP and they are competitive, we show that one WSP can take advantage of the PH-market by indirectly selling more data to the other WSP's users.
This work introduces two complementary techniques to improve the packet loss resilience of scalable video coding systems. First, a "switch per-pixel" error concealment (SPEC) scheme is proposed, which allows the decoder to exploit information from both the current base layer and previous enhancement-layer frame for the reconstruction of missing enhancement-layer blocks. Based on the packet loss history and the quantized base-layer data, the algorithm switches per pixel between the two information sources. SPEC is shown to consistently outperform standard concealment methods. The second main contribution is concerned with encoder decision optimization. Enhancement layer prediction modes are selected so as to minimize the overall decoder reconstruction distortion, which is due to quantization, packet loss and error propagation. The distortion computation uses a recursive optimal per-pixel estimate (ROPE) to accurately account for the effects of error concealment as well as spatial and temporal error propagation. Simulation results show that ROPE-based mode selection substantially outperforms conventional prediction mode selection schemes. Finally, the combination of SPEC at the decoder and ROPE-based mode selection at the encoder is shown to achieve significant additional performance gains.
Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main paradigms in cellular UAV communications, i.e., cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location, during which it needs to maintain reliable communication with the cellular network by associating with one GBS at each time instant. We aim to minimize the UAV's mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target. To tackle this challenging non-convex problem, we first propose a graph connectivity based method to verify its feasibility. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging graph theory and convex optimization techniques. The proposed methods are analytically shown to be capable of achieving a flexible trade-off between complexity and performance, and yielding a solution that is arbitrarily close to the optimal solution in polynomial time. Finally, we make concluding remarks and point out some promising directions for future work.
Intelligent reflecting surface (IRS) is an emerging technique to enhance the wireless communication spectral efficiency with low hardware and energy cost. In this letter, we consider the integration of IRS to an orthogonal frequency division multiple access (OFDMA) based multiuser downlink communication system, and study the pertinent joint optimization of the IRS reflection coefficients and OFDMA time-frequency resource block as well as power allocations to maximize the users' common (minimum) rate. Specifically, due to the lack of frequency-selective passive beamforming capability at the IRS, only one set of reflection coefficients can be designed for adapting to a large number of channels of multiple users over different frequency sub-bands. To tackle this difficulty, we propose a novel dynamic passive beamforming scheme where the IRS reflection coefficients are dynamically adjusted over different time slots within each channel coherence block to create artificial time-varying channels and select only a subset of the users to be simultaneously served in each time slot, thus achieving a higher passive beamforming gain. Although the formulated optimization problem is non-convex, we propose an efficient algorithm to obtain a suboptimal solution to it. Numerical results show that the proposed scheme significantly improves the system common rate over the setup without IRS and that with random IRS reflection coefficients. Moreover, our proposed dynamic passive beamforming outperforms the fixed passive beamforming which employs a common set of reflection coefficients in each channel coherence block, by more flexibly balancing between passive beamforming and multiuser diversity gains.
No abstract is provided for this article.
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.
Poly(ADP-ribosyl)ation (PARylation) is a post-translational modification by which poly ADP-ribose (PAR) polymers are covalently added to proteins through a PAR polymerase (PARP). Here, we identify that the transcriptional regulator, OVOL2, is a novel substrate for PARP1 and can be PARylated at residues Lysine 145, Lysine 176, and Lysine 212, within the C2H2 zinc-fingers domain. PARylated OVOL2 acts as a transcriptional suppressor directly inhibiting downstream expression of the E3 ligase, Skp2, which promotes ubiquitin-dependent degradation of Cyclin E, thus leading to an accumulation of Cyclin E during cell cycle progression. As a result, overexpression of OVOL2, but not the non-PARylated OVOL2-3K/A mutant, induces aberrant centrosome amplification and chromosome instability, resulting in aneuploidy and subsequent cell death. Overexpression of PARylated OVOL2 in vivo resulted in a significant reduction in tumor progression, supporting the function of OVOL2 as a tumor suppressor, which is highly regulated by PARylation. Support or Funding Information This work was supported in part by Grants R-154-000-637-511 from the Ministry of Education (MOE), National Medical Research Council (NMRC), Singapore to Y.C.L. Schematic diagram illustrating the role of PARylated OVOL2 in maintaining the stability of Cyclin E. Accumulated Cyclin E2 leads to aberrant amplification of centromeres and chromosome instability, resulting in tumor cell death. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.
