Cross-Link Interference Mitigation With Over-the-Air Pilot Forwarding for Dynamic TDD

Dynamic heterogeneous networks (HetNets) in which small cells operate in dynamic time division duplex (D-TDD) systems enable the instantaneous traffic load to be handled with greater spectral efficiency than conventional HetNets. However, combining HetNets and D-TDD systems causes cross-link interference (CLI) both within the same tier and across tiers, negatively impacting system performance. In this paper, we point out that small cells are underprivileged in terms of interference mitigation under the conventional spectrum partitioning usage. Accordingly, we propose employing a novel spectrum partitioning scheme that brings up interference scenarios where we can take advantage of multiple-input multiple-output (MIMO) compatibility from macro base stations. Simulation results show that the proposed spectrum usage in combination with general MIMO schemes achieves significant performance enhancement in terms of small cell capacity at the cost of a relatively small degradation in macro cell capacity.

Dynamic time division duplexing (TDD) is envisioned as a vital transmission technology of the 5G new radio, due to its reciprocal propagation characteristics. However, the potential cross-link interference (CLI) imposes a fundamental limitation against the feasibility of the ultra-reliable and low latency communications (URLLC) in dynamic-TDD systems. In this work, we propose a near-optimal and complexity-efficient CLI suppression scheme using orthogonal spatial projection, while the signaling overhead is limited to B-bit, over the back-haul links. Compared to the state-of-the-art dynamic-TDD studies, proposed solution offers a significant improvement of the URLLC outage latency, e.g., $\sim -199$% reduction, while boosting the achievable capacity per the URLLC packet by $\sim +156$%.

We analyze spectral efficiency of dynamic time division duplex in a fixed wireless cellular network. Conventionally, spectral efficiency has been analyzed for static and fully loaded systems. In this paper, we investigate how asymmetric and dynamic traffic affect the spectral efficiency of Time-Division-Duplex (TDD) systems. Recently, dynamic TDD (D-TDD) has gained much attention as an efficient duplex scheme for high-speed data communications, because adaptive switching ability enables the system to obtain statistical multiplexing gain by exploiting dynamic and asymmetric data traffic. However, it has been noted that a rather strong co-channel interference can be present due to adaptive switching in a cellular network that uses frequency reuse. Thus, benefits of dynamic TDD may not be justifiable unless a proper countermeasure is employed. To suppress the effect of strong co-channel interference, we employ time slot allocation (TSA) strategy along with sector antenna layouts, as proposed in our previous work Jeong and Kavehrad (IEEE and Transactions on communication, Vol. 50, no.10 pp. 1627–1636, 2002). We note that higher spectral efficiency is obtained in D-TDD systems by employing TSA strategy. We also evaluated spectral efficiency of D-TDD system employing adaptive modulation, assuming that traffic is delay tolerant. It is observed that five times higher spectral efficiency can be obtained by employing adaptive modulation. The effect of variance of ratio of offered load on uplink and downlink is also evaluated. Our computer simulation results show that spectral efficiency of D-TDD system with time slot allocation algorithm is more than that of static TDD (S-TDD) over a large range of traffic variation. In conclusion, D-TDD system can take advantage of statistical multiplexing gain of dynamic traffic by adaptively positioning the boundary to the varying traffic bandwidth in its two-way transmissions when TSA strategy is employed for suppression of strong co-channel interference.

Dynamic time-division duplexing (TDD) allows for flexible resource allocation for the varying downlink (DL) and uplink (UL) traffic demands in the 5G cellular network. It becomes more useful when the number of active users is highly limited, especially for the small cells in a ultra-dense network. However, traffic imbalance between DL and UL in adjacent cells incurs crosslink interference (CLI), which may seriously reduce the system throughput. In order to deal with CLI induced by dynamic TDD, full or partial information on slot assignment and/or mutual interference must be shared among all cells for coordinating dynamic slot assignment throughout the system. In this paper, we propose the fully decentralized slot assignment policies that can reduce the CLI without resorting to side information shared among the cells. It is based a bi-directional slot-ordered scheduling scheme in each subframe, which exploits implicit location-dependent information such as signal-to-interference-plus-noise ratio (SINR) and/or three-dimensional spatial angle for all UEs in each cell under a full-dimension multi-input and multi-output (FD-MIMO) operation. Our system-level performance evaluation in 5G system scenario has shown that the proposed approach can maintain total interference almost constant even subject to the CLI. Furthermore, it achieves the average system throughput as much as 95% and 91% of the performance that can be achieved by a centralized greedy search for DL and UL, respectively, without much compromising the lower 5% system throughput.

Dynamic Time Division Duplexing (DynTDD) is one of the key features that enable the dynamic or flexible uplink and downlink transmission and reception for a specific sub-frame in SG mobile networks. However, the advantages of the DynTDD system are difficult to fully utilize due to the cross-link interference (CLI) arising from neighboring cells using different transmission directions on the same or partially- overlapping time-frequency resources. There are two types of cross-link interference; between the Base Stations (BS), which is known as BS-to-BS or DL-to-UL interference, and between User Equipment (UE) which is known as UE-to-UE or UL-to-DL interference. Rank deficiency of channel matrices is an important aspect of Multi-Input Multi-Output (MIMO) wireless systems. Poor scattering and the presence of single or very few direct paths are some reasons for rank deficiency in wireless channels. While the implications of rank deficient channels are well understood for the single user (SU) point-to-point setting, less is known for interference networks. In this paper, we give a sufficient condition for a full rank MIMO interfering channel, that outperforms the current state of the art, we provide also tighter necessary conditions for Interference Alignment (IA) feasibility, that are very close to the necessary and sufficient condition, which gives all the feasible cases. Then we extend a MIMO Interference Alignment (IA) feasibility framework to rank deficient channels, by investigating our published theorem for the necessary and sufficient condition in this case.

Although in cellular networks full duplex and dynamic time-division duplexing promise increased spectrum efficiency, their potential is so far challenged by increased interference. While previous studies have shown that self-interference can be suppressed to a sufficient level, we show that the cross-link interference for both duplexing modes, especially from base station to base station, is the remaining challenge in multi-cell networks, restricting the uplink performance. Using beamforming techniques of low complexity, we show that this interference can be mitigated, and that full duplex and dynamic time-division duplexing can substantially increase the capacity of multi-cell networks. Our results suggest that if we can control the cross-link interference in full duplex, we can almost double the multi-cell network capacity as well as user throughput. Therefore, the techniques in this article have the potential to enable a smooth introduction of full duplex into cellular systems.

The Third Generation Partnership Project (3GPP) has been studying dynamic allocation of sub-frames to uplink (UL) or downlink (DL) in Time Division Duplex (TDD), called ‘Dynamic TDD,’ since the Long Term Evolution (LTE) Rel. 11 timeframe. At the same time, 3GPP is also standardizing Enhanced Local Area (eLA) small-cell heterogeneous architectures for inclusion in LTE-B (LTE Rel. 12) as a solution offering high data rate to user terminals (UEs) along with high system capacity through spatial reuse of spectrum. In this paper, we focus on a particular eLA architecture proposed by DOCOMO, called the Phantom Cell architecture, that has the option to support dynamic TDD. For an arbitrarily-located UE in an eLA cell network, we apply results from stochastic geometry to derive expressions for the distribution of DL signal to interference plus noise ratio (SINR) at an arbitrary UE and the distribution of UL SINR at its serving eLA base station (BS). We use these results to study aspects of eLA cell system design, and the sensitivity of SINR to the extent of coordination across eLA cells employing dynamic TDD.

Dynamic time division duplexing (DynTDD) provides greater flexibility than static time division duplexing (TDD) by dynamically adjusting time slot allocation based on communication needs.However, flexibility may be limited by crosslink interference (CLI) from neighboring cells using different transmission directions on partially-overlapping time-frequency resources.To mitigate this interference, coordinated beamforming is critical.This study focuses on zero-forcing (ZF) transmit beamforming at initialisation, with and without water-filling, and the iterative weighted minimum mean-square error (WMMSE) algorithm to maximise the sum rate in a MIMO user equipment to user equipment (UE-to-UE) interference channel (IC).Additionally, the study explores the potential benefits of non-uniform degrees-of-freedom (DoF) at uplink (UL) and/or downlink (DL) UEs, increasing the sum of DoF, resulting in a higher sum rate at high SNR.

The future 5G cellular networks are expected to support several-fold increase in data traffic and number of devices, and provide a very low latency and gigabit-rate data services. millimeter Wave (mmWave) communication (30–300 GHz) is proposed to be an important part of the 5G cellular networks to fulfill these requirements. With most of the high data rate demands originating from indoor User Equipments (UEs), conventional wired backhaul links prove to be a major bottleneck. Hence, mmWave wireless backhaul links are explored at Femto Base Stations (FBSs). In order to derive maximum benefit of mmWave backhaul links, efficient resource (data slots) utilization is necessary. Proper scheduling of mmWave backhaul links will help to achieve the same. In this paper, we propose a novel FBS-to-FBS (F2F) communication scheme which not only helps in offloading the data traffic from the Micro Base Station (MiBS) but also can act as a relay link for the other FBSs for routing the backhaul traffic to the MiBS. We also propose a solution to concurrently schedule mmWave wireless backhaul links to increase the resource utilization considering the coexistence of both FBS-to-MiBS (F2M) and F2F links. Our proposed solution consists of two parts — first, it determines which backhaul links can be scheduled concurrently by finding the upper bound for the interfering distance based on the alignment of different lobes of the directional antennas. Second, the transmit power is throttled to match the Quality of Service (QoS) requirements of each link aiming to further control the interference and to increase the number of concurrent transmissions. The benefits of the proposed solution are studied and compared with the Time Division Multiple Access (TDMA) and random scheduling schemes using extensive simulations.

In this paper, multiple antenna based intercell interference mitigation techniques are studied in the downlink of an OFDM-based cellular radio network. At the base stations, multiple antennas are used for transmit beamforming in order to reduce the intercell interference spatially spread in the network. At the user terminals, multiple receive antennas facilitate implementation of interference rejecting baseband signal processing techniques, e.g. the well-known interference rejection combining (IRC). Evaluations are performed by means of system level simulations. The results show that both transmit beamforming by itself as well as terminal interference rejection by itself provide significant gains, both in terms of system throughput as well as cell edge user throughput. However, the real performance boost comes when the techniques are used together, they combine very well and the gains are almost additive. The reasons for this are two; first, the transmit beamforming reduces the intercell interference considerably. Second, when the user yet is exposed for interference, it consists most probably of one or few dominating sources, which is exactly the situation where interference rejection algorithms such as IRC are most efficient.

In recent years, drones have been used in a wide range of fields, such as agriculture, transportation of goods, and security. Drones equipped with communication facilities are expected to play an active role as base stations in areas where ground base stations are unavailable, such as disaster areas. In addition, asynchronous operation is being considered for local 5G, in order to support all kinds of use cases. In asynchronous operation, cross-link interference between base stations is an issue. This paper attempts to reduce the interference caused by the drone network by introducing circularly polarized antennas against the conventional system using linearly polarized antennas. Numerical analyses are conducted to validate the effectiveness of the proposed system, where Signal-to-Interference Ratios (SIRs) are shown to be improved significantly as the numerical evaluation results. Specifically, for the scenario of only access links, in the region where conventional antenna architecture can only achieve an SIR of less than 20 dB, our proposed system applying circularly polarized antennas can almost realize an SIR of more than 40 dB. Significant improvement can be also observed in the scenario with the existence of backhaul links, where the conventional system had difficulty achieving our system design goal SIR of 16.8 dB, while the proposed antenna architecture could easily attain this goal in most regions of our evaluation ranges.

The massive multiple-input-multiple-output (MIMO) technology relies on many antennas at base stations to offer high throughput to meet 5G network requirements. However, each base station requires accurate channel state information during channel estimation. Pilot contamination is one of the significant challenges that limit higher order MIMO deployment because it causes channel estimation error. In this paper, a Wiener predictor (WP) using the temporal-based prediction technique was first proposed and then extended as the coordinated WP (CoWP). The WP method is based on prediction using information that has been stored and processed at the base station from a modified subframe, whereas the extended CoWP method requires neighboring base station coordination to assign collection and prediction with other base stations. Results showed a reduction in channel estimation error due to pilot contamination after the Wiener-based prediction and coordinated base station techniques were used. The proposed CoWP technique can increase the performance of channel estimation error by up to 25 and 30 dB relative to the performance of other research technique and conventional MMSE channel estimation. This paper indicates that the pilot contamination effect can be minimized by exploiting the temporal dimension, and this approach increases the number of MIMO order for future 5G base stations, which currently limited due to pilot contamination.

Massive Multiple-Input Multiple-Output (Massive MIMO) serves as a foundational enabling technology for 5G and future communication systems, markedly boosting spectral and energy efficiency through the deployment of large-scale antenna arrays. However, the scaling-up of antenna arrays has led to a substantial increase in system power consumption and hardware costs, with high-precision analog-to-digital converters (ADCs) emerging as the dominant power consumption bottleneck in the radio frequency chain. To alleviate system complexity and power consumption, low-resolution ADCs (13 bits) have attracted extensive research interest in recent years. Such schemes can substantially curtail hardware costs and energy consumption while retaining satisfactory system performance. Nevertheless, the introduction of severe nonlinear distortion due to low-precision quantization disrupts the linear Gaussian model assumption upon which traditional receiver algorithms rely, resulting in compromised channel estimation and signal detection performance. Quantization errors demonstrate non-Gaussian and input-dependent characteristics, leading to the degradation of amplitude information and thus constraining the applicability of technologies such as high-order modulation and high-precision sensing. This paper presents a systematic review of low-precision quantization techniques for Massive MIMO. It first investigates the impacts of low-bit quantization on system models and signal statistical properties. Subsequently, it elaborates on transceiver architectures and key design challenges pertaining to low-precision ADCs/DACs. The paper highlights signal processing and algorithmic strategies to overcome quantization distortion, including Bussgang decomposition linearization methods, statistical inference techniques such as approximate message passing (AMP), model-driven deep learning frameworks, and quantization architectures endowed with noise-shaping capabilities. Finally, it discusses the challenges and future directions of this technology in emerging scenarios, including terahertz communications, intelligent reflecting surfaces, and integrated sensing and communication. This paper seeks to provide researchers with a systematic technical overview, clarifying the intrinsic connections and trade-offs among different methods, and offering valuable insights for the realization of high-energy-efficiency and low-cost Massive MIMO systems.

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.

In this paper, we consider the optimization of wireless capacity-limited back-haul links in future heterogeneous networks (HetNets). We assume that the HetNet is formed with one macro-cell base station (MBS), which is associated with multiple small-cell base stations (SBSs). It is also assumed both the MBS and the SBSs are equipped with massive arrays, while all mobiles users (macro-cell and small-cell users) have single antenna. For the back-haul links, we propose to use a capacity-aware beam-forming scheme at the SBSs and MRC at the MBS. Using particle swarm optimization (PSO), each SBS seeks the optimal transmit weight vectors that maximize the back-haul up-link capacity and the access up-links signal-to-interference plus noise ratio (SINR). The performance evaluation in terms of the symbol error rate (SER) and the ergodic system capacity shows that the proposed capacity-aware back-haul link scheme achieves similar or better performance than traditional wireless back-haul links and requires considerably less computational complexity.