User-selective collaboration maximizing gain in sum rate

Interference mitigation by applying Joint Processing (JP) in cooperative cellular networks boasts cell-edge performance compared to non-cooperative systems. However, downlink JP requires knowledge of Channel State Information (CSI) at all collaborating Base Stations (BSs), where interference received at the User Equipments (UEs) is markedly affected by the level of CSI quality influenced amongst others by the method of distributing CSI to the BSs. In this contribution, we compare CSI Distribution over the Air (CD-A) with CSI Distribution over the Backhaul (CD-B) considering a Frequency Devision Duplex (FDD) system. For CD-A, CSI is directly fed back from the UEs to the collaborating BSs via the uplink channel (assuming link adaptation is performed according to one master BS to which a UE has been assigned). Adopting CD-B, CSI is foremost transmitted only to the master BS and then forwarded to the other BSs using backhaul connections. This method typically introduces additional latency due to routing issues. In this contribution, we show that CD-A outperforms CD-B in the largest part of the cooperation area, even when pedestrian user velocities and small backhaul latencies are considered.

In order to realize high precision comparison between the time of remote user and UTC (NTSC), the National Time Service Center has built a set of high accuracy remote time common-view system based on the satellite common view time transfer method, the underlying system comprised with a base station and some calibration stations and a data processing center. Among them, the base station provides a reference measurement value for the whole system, namely the bias of UTC (NTSC) and the satellite clock. The calibration stations are used to provide a measurement value of the measured stations, namely the bias of local time of calibration station and the satellite clock. The data processing center is responsible for completing the time deviation between the local time of calibration stations and UTC (NTSC). In view of the importance of base station in the system, this paper has designed two kinds of configuration scheme, the single base station and multiple base stations. According to this a large number of tests has been analyzed. The results of experiment describe the single base station is easy to use, its timing accuracy is about 10ns, multi-base stations with higher availability and reliability, and its timing accuracy is 3 ∼ 5ns.

Today more and more bus companies are providing real-time bus locations to their riders to improve passenger experience and increase ridership. Most of the existing bus localization systems rely on the Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS). However, it is costly to install GNSS receivers and retrofit existing buses to power them, which prevents them to be adopted by those bus operators with tight budgets. There has been increasing interest in developing GPS-free localization schemes that leverage the wireless signals transmitted by the buses to localize them. Such schemes often require the received signal strength (RSS) measured at multiple base stations and therefore are not applicable to a small transportation service with a single base station, such as the shuttle service for a university campus. This paper presents a novel approach that leverages the LoRa link characteristics measured by a single base station and deep learning to localize a campus shuttle when it approaches a stop. Experimental results show that our solution provides a detection accuracy of no less than 92.07% and significantly outperforms all baselines without requiring new hardware and introducing additional communication overhead.

We consider the uplink three-cell single-user cellular systems with limited feedback interference alignment (IA). Considering the exchange of channel state information (CSI) between base stations (BSs), IA solution can be done at BSs. Then the indices of the quantized precoding vectors are fed back to users. A joint quantization strategy, which quantizes the precoding vectors for different users jointly, is proposed. The proposed quantization scheme enlarges the codebooks and quantizes precoding vectors jointly through the exchange of codebook information between BSs. The quantized precoding vectors for different users are set to have a same index. Only one of the three BSs is appointed to broadcast the same index so that the total feedback cost would not be increased. With the proposed quantization scheme, the quantization errors are mitigated and the system sum rate is increased. A quantization criterion, which we refer to as the maximum sum rate criterion, is also proposed. Compared with the general minimum chordal distance criterion, the proposed quantization criterion can achieve a higher sum rate. Performance improvement of the proposed scheme is analyzed. Simulations also confirm its effectiveness.

This paper studies feedback resource allocation in the downlink of a Frequency Division Duplex (FDD) multichannel wireless system. We consider a downlink network with a single base station, L shared channels and N mobile users. Throughput optimal algorithms like the MaxWeight scheduling in general require the complete channel state information (with N × L link states) for scheduling, which could be unaffordably expensive when the number of users is large. In this paper, we consider the scenario where the base station allocates only limited uplink resource for acquiring channel state information. We first show that to support a fraction (1 - ε) of the full throughput region (the throughput region with full channel state information), the base station needs to acquire at least Θ(L) link states at each time slot. We then propose a Weight Based Feedback allocation, named WBF, and show that WBF together with the MaxWeight scheduling achieves a fraction (1 - ε) of the full throughput region by acquiring at most Θ(L log 1 ⁄ ε) link states per time slot.

This paper addresses channel state information (CSI) estimation in a wireless relay network, which consists of a base station (BS), a relay station (RS), and a mobile station (MS). We propose a method for the BS to obtain the instantaneous CSIs of the three links (BS-RS, BS-MS, and RS-MS links) with minimized overhead. The CSI availability plays an important role for the BS to adapt its transmission strategies to the given channel conditions. Our proposed method exploits the fact that the link condition between fixed BS and fixed RS tends to be stable, and the frequent CSI update is not necessary for this link. In order for the BS to obtain the CSI of the distant link (i.e. RS-MS link), RS amplifies a pilot signal received from MS with a pre-defined amplification factor, and forwards it to BS. This enables the BS to obtain the CSI of the RS-MS link based on the received pilot signal and pre-knowledge on the CSI of BS-RS link. We show by computer simulation that our proposed method can reduce the required overhead for CSI estimation with little degradation on the accuracy of the obtained CSI.

This paper investigates a precoding method in downlink multiuser multiple-input multiple-output (MIMO) transmission with multiple base station (BS) cooperation, where each user device basically feeds back the instantaneous channel state information (CSI) to only the nearest BS, but the users near the cell edge additionally feedback the instantaneous CSI to the second nearest BS among the cooperating BSs. Our precoding method is categorized as a form of multi-cell processing (MCP) [5], in which the transmission information to a user is shared by the cooperating BSs in order to utilize fully the degrees of freedom of the spatial channel, and is based on block diagonalization of the channel matrix. However, since some elements of the channel matrix are unknown, we allow partially non-orthogonal transmission. More specifically, we allow inter-user interference to users with limited instantaneous CSI feedback from the channel where the instantaneous CSIs of those users are not obtained at the BSs. The other sources of inter-user interference are set to zero based on the block diagonalization of the channel matrix. The proposed method more efficiently utilizes the degrees of freedom of the spatial channel compared to the case with full orthogonal transmission at the cost of increased inter-user interference. Simulation results show the effectiveness of the proposed method compared to the conventional approaches, which can accommodate the partial CSI feedback scenario, from the viewpoints of the required transmission power and achievable throughput.

Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently. Most of the relevant literature is focused on the single cell setting where a single IRS is deployed and perfect channel state information (CSI) is assumed. In this work, we develop a novel methodology for multi-IRS-assisted multi-cell networks in the uplink. We consider the scenario in which (i) channels are dynamic and (ii) only partial CSI is available at each base station (BS); specifically, scalar effective channel powers from only a subset of user equipments (UE). We formulate the sum-rate maximization problem aiming to jointly optimize the IRS reflect beamformers, BS combiners, and UE transmit powers. In casting this as a sequential decision making problem, we propose a multi-agent deep reinforcement learning algorithm to solve it, where each BS acts as an independent agent in charge of tuning the local UE transmit powers, the local IRS reflect beamformer, and its combiners. We introduce an efficient information-sharing scheme that requires limited information exchange among neighboring BSs to cope with the non-stationarity caused by the coupling of actions taken by multiple BSs. Our numerical results show that our method obtains substantial improvement in average data rate compared to baseline approaches, e.g., fixed UE transmit power and maximum ratio combining.

We consider a precoding method in downlink multiuser multiple-input multiple-output (MIMO) transmission with multiple base station (BS) cooperation where some of the instantaneous channel state information (CSI) is not available at the BSs. The proposed precoding method is based on block diagonalization of the channel matrix. However, since some elements of the channel matrix are unknown, we allow a partially non-orthogonal transmission. More specifically, we allow inter-user interference to users with limited instantaneous CSI feedback from the channel where the instantaneous CSI of that user is not obtained at the BSs. The other inter-user interferences are set to zero based on the block diagonalization of the channel matrix. The proposed method efficiently uses the degree of freedom of the spatial channel compared to the case with full orthogonal transmission at the cost of increased inter-user interference to the users with limited instantaneous CSI feedback. Simulation results show that the proposed method increases the capacity for a given transmission power or decreases the transmission power for a given capacity requirement compared to the case with full orthogonal transmission.

Introduction It is envisioned that the capacity demand in fifth generation (5G) wireless networks will increase by 1000 times before the year 2020, and this huge demand will be fueled by high-definition video and content streaming applications, which not only consume immense wireless bandwidth but also have stringent real-time quality of service (QoS) requirements. Small-cell dense wireless networks are regarded as a key candidate 5G technology to meet such aggressive demand. By dense deployment of small-cell base stations (BSs), the network becomes closer to mobile users, and thus the spectral efficiency per unit area can be significantly improved. Moreover, relay stations (RSs) without wired backhaul can also be deployed to enhance the coverage area and provide signal-to-noise ratio (SNR) gain. While there are many potential opportunities associated with dense wireless networks, the potential spectrum efficiency gain they can provide is significantly limited by increasingly severe interference due to the densification of BSs. There are several common schemes to mitigate interference. In the strong-interference case, channel orthogonalization, such as frequency division multiple access (FDMA) or time division multiple access (TDMA), is used to avoid interference [1], and advanced schemes, such as interference alignment (IA) [2, 3] and interference coordination [4, 5], have been proposed to increase the spectral efficiency of interference channels by jointly mitigating the interference using shared channel state information (CSI). However, these approaches may lead to inefficient use of channel resources because the transmitters do not share payload information and therefore are not fully cooperative. To improve the spectrum efficiency further, coordinated multipoint (CoMP) transmission has been proposed as one of the most important core technologies for Long Term Evolution-Advanced (LTE-A) and future 5G wireless networks [6]. By sharing both CSI and payload data among the BSs concerned, CoMP transmission can transform the wireless network from an unfavorable interference topology to a favorable broadcast topology, where the interference can be mitigated much more efficiently. However, the conventional CoMP scheme is quite costly because it requires a high-capacity backhaul for payload exchange between the BSs, and this poses a huge challenge for practical applications of CoMP, especially for small-cell dense wireless networks.

Coordinated multi-point transmission/reception (CoMP), in which base stations cooperation during the downlink, has been considered as a attractive way of achieving higher performance and mitigating interference, especially for terminals at cell edges. In this paper, distributed precoding for multicell-MISO downlink CoMP is considered. Most previous work in the area is on the assumption that base stations have knowledge of both data symbols and full channel state information (CSI), which leads to heavy exchange of channel state information and signal information over backhaul links . In this paper, several distributed precoding strategies (Distributed MRT, distributed ZF, distributed LVSINR and DVSINR) for multicell-MISO downlink CoMP are analyzed, which supposes that each base station only has local CSI and the precoders can be devised at each BS in a distributed manner, and Monte Carlo simulations demonstrate the performance of such strategies.

A radio communications network design supporting airport surface traffic automation (ASTA) using global positioning system (GPS) data is considered. The emphasis is on spectrally efficient channel access schemes taking into account traffic characteristics. The bandwidth requirements of two time-domain-based multiple access schemes and code division multiple access (CDMA) are analyzed. In the case of CDMA, both a single base station and multiple base stations are considered. It is shown that the use of multiple base stations can significantly improve the total system capacity for a fixed bandwidth. To obtain specific results, it is assumed that the CDMA system employs convolutional forward error correction and operates in a Rayleigh fading channel.

Abstract. PPP-RTK has been widely investigated to take the advantages of both real-time kinematic (RTK) and precise point positioning (PPP) techniques. Prior to PPP-RTK, the conventional RTK based on the use of a single base station, on the one hand, has been extended to work within a regional network of multiple base stations, known as network RTK (NRTK). The RTK and NRTK enable fast ambiguity resolution over a short baseline or a local region. PPP, on the other hand, eliminates the need to establish any local network like NRTK, which is able to work in a single receiver mode but it suffers long convergence time in ambiguity resolution. PPP-RTK therefore can provide fast ambiguity resolution capability like RTK and NRTK. Current PPP-RTK techniques however still face challenges in supporting mass-market applications such as mobile devices and autonomous vehicles. Although PPP-RTK system (a combination of RTK and PPP technologies) can help expand the coverage of RTK and speed up the ambiguity resolution in PPP, the deployment and maintenance of a dense network of permanent base stations and a central data processing infrastructure for generation of SSR corrections increases not only the system cost but also the system complexity. This is particularly an obstacle for mass-market applications. In this paper, a new RTK approach is described. First it is based on a single base station state-space-representation (SSR) correction generation strategy to support fast ambiguity resolved PPP. Further it presents a new peer-to-peer propagation strategy to form a real-time dynamically generated network of base stations to support mass-market application users with unbounded coverage. As a result, the new approach eliminates the need to deploy and maintain a dense network of permanent base stations and central data processing infrastructure as required in a conventional PP-RTK system.

Beam alignment (BA), a core component of beamforming, is essential for achieving optimal directional gain in high-frequency communications. However, in single base station (BS) scenarios, BA may fail when the line-of-sight (LoS) path is obstructed. Multi-BS cooperation can effectively address this issue by leveraging the LoS paths from other BSs. However, applying traditional single-BS BA schemes to multi-BS cooperation scenarios leads to a substantial training overhead. To address this, we propose a sensing-aided simultaneous multi-BS BA (SSMBA) strategy in this paper. In the first phase, compressed sensing (CS) is utilized to enable simultaneous multi-beam training across multiple BSs, significantly reducing training overhead. In the second phase, leveraging integrated sensing and communication (ISAC) capabilities, a sensing-aided beam refinement technique is introduced to further increase BA accuracy and beamforming gain. The simulation results demonstrate that the proposed SSMBA strategy has advantages in both reducing the training overhead and increasing beamforming gain.

This chapter addresses distributed parameter coordination methods for wireless communication systems. We present two distributed algorithms for the problem of precoder selection. The first and simplest method is the greedy solution in which each communication node in the network acts selfishly. The second method and the focus of this chapter is based on a message-passing algorithm, namely min-sum algorithm, in factor graphs. Three kinds of precoding codebook are considered: transmit antenna selection, fixed-beam selection, and LTE precoder selection. Evaluations on the potential of such an approach in a wireless communication network are provided and its performance and convergence properties are compared with those of a baseline selfish/greedy approach. Simulation results are presented and discussed, which show that the graph-based technique generally obtains gain in sum rate over the greedy approach at the cost of a larger message size. For instance, the percentage gain in sum rate over the greedy is about 33 % within 5 iterations in a 7-cell network considering single-layer LTE precoders. Besides, the graph-based method usually reaches the global optimum in a efficient manner. Methods of improving the rate of convergence of graph-based distributed coordination technique and reducing its associated message size are therefore important topics for wireless communication networks.