Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio

Temporal and spatial combining for 5G mmWave small cells

This chapter proposes the combination of temporal processing through Rake combining based on direct sequence-spread spectrum (DS-SS), and multiple antenna beamforming or antenna spatial diversity as a possible physical layer access technique for fifth generation (5G) small cell base stations (SBS) operating in the millimetre wave (mmWave) frequencies. Unlike earlier works in the literature aimed at previous generation wireless, the use of the beamforming is presented as operating in the radio frequency (RF) domain, rather than the baseband domain, to minimise power expenditure as a more suitable method for 5G small cells. Some potential limitations associated with massive multiple input-multiple output (MIMO) for small cells are discussed relating to the likely limitation on available antennas and resultant beamwidth. Rather than relying, solely, on expensive and potentially power hungry massive MIMO (which in the case of a SBS for indoor use will be limited by a physically small form factor) the use of a limited number of antennas, complimented with Rake combining, or antenna diversity is given consideration for short distance indoor communications for both the SBS) and user equipment (UE). The proposal’s aim is twofold: to solve eroded path loss due to the effective antenna aperture reduction and to satisfy sensitivity to blockages and multipath dispersion in indoor, small coverage area base stations. Two candidate architectures are proposed. With higher data rates, more rigorous analysis of circuit power and its effect on energy efficiency (EE) is provided. A detailed investigation is provided into the likely design and signal processing requirements. Finally, the proposed architectures are compared to current fourth generation long term evolution (LTE) MIMO technologies for their anticipated power consumption and EE

Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication

The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa

Non-linear echo cancellation - a Bayesian approach

Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel

Enabling Technology and Algorithm Design for Location-Aware Communications

Location-awareness is emerging as a promising technique for future-generation wire­ less network to adaptively enhance and optimize its overall performance through location-enabled technologies such as location-assisted transceiver reconfiguration and routing. The availability of accurate location information of mobile users becomes the essential prerequisite for the design of such location-aware networks. Motivated by the low locationing accuracy of the Global Positioning System (GPS) in dense multipath environments, which is commonly used for acquiring location information in most of the existing wireless networks, wireless communication system-based po­sitioning systems have been investigated as alternatives to fill the gap of the GPS in coverage. Distance-based location techniques using time-of-arrival (TOA) mea­surements are commonly preferred by broadband wireless communications where the arrival time of the signal component of the First Arriving Path (FAP) can be con­verted to the distance between the receiver and the transmitter with known location. With at least three transmitters, the location of the receiver can be determined via trilatération method. However, identification of the FAP’s signal component in dense multipath scenarios is quite challenging due to the significantly weaker power of the FAP as compared with the Later Arriving Paths (LAPs) from scattering, reflection and refraction, and the superposition of these random arrival LAPs’ signal compo­ nents will become large interference to detect the FAP. In this thesis, a robust FAP detection scheme based on multipath interference cancellation is proposed to im­ prove the accuracy of location estimation in dense multipath environments. In the proposed algorithm, the signal components of LAPs is reconstructed based on the estimated channel and data with the assist of the communication receiver, and sub­ sequently removed from the received signal. Accurate FAP detection results are then achieved with the cross-correlation between the interference-suppressed signal and an augmented preamble which is the combination of the original preamble for com­ munications and the demodulated data sequences. Therefore, more precise distance estimation (hence location estimation) can be obtained with the proposed algorithm for further reliable network optimization strategy design. On the other hand, multiceli cooperative communication is another emerging technique to substantially improve the coverage and throughput of traditional cellular networks. Location-awareness also plays an important role in the design and imple­mentation of multiceli cooperation technique. With accurate location information of mobile users, the complexity of multiceli cooperation algorithm design can be dra­matically reduced by location-assisted applications, e.g., automatic cooperative base station (BS) determination and signal synchronization. Therefore, potential latency aroused by cooperative processing will be minimized. Furthermore, the cooperative BSs require the sharing of certain information, e.g., channel state information (CSI), user data and transmission parameters to perform coordination in their signaling strategies. The BSs need to have the capabilities to exchange available information with each other to follow up with the time-varying communication environment. As most of broadband wireless communication systems are already orthogonal frequency division multiplexing (OFDM)-based, a Multi-Layered OFDM System, which is spe­cially tailored for multiceli cooperation is investigated to provide parallel robust, efficient and flexible signaling links for BS coordination purposes. These layers are overlaid with data-carrying OFDM signals in both time and frequency domains and therefore, no dedicated radio resources are required for multiceli cooperative networks. In the final aspect of this thesis, an enhanced channel estimation through itera­ tive decision-directed method is investigated for OFDM system, which aims to provide more accurate estimation results with the aid of the demodulated OFDM data. The performance of traditional training sequence-based channel estimation is often lim­ ited by the length of the training. To achieve acceptable estimation performance, a long sequence has to be used which dramatically reduces the transmission efficiency of data communication. In this proposed method, the restriction of the training se­quence length can be removed and high channel estimation accuracy can be achieved with high transmission efficiency, and therefore it particular fits in multiceli coopera­tive networks. On the other hand, as the performance of the proposed FAP detection scheme also relies on the accuracy of channel estimation and data detection results, the proposed method can be combined with the FAP detection scheme to further optimize the accuracy of multipath interference cancellation and FAP detection