This paper presents a blockage prediction system based on artificial intelligence/machine learning (AI/ML) for millimeter wave (mmWave) communications, with a particular focus on vehicular scenarios. Although numerous related works on ML-based blockage prediction, these works focus on simple scenarios, where the trajectory of the moving object is either fixed or deterministic. In order to make the blockage prediction system more practical, this paper assumes a nondeterministic situation such as both communication terminals and blockers are moving. Furthermore, we have formulated a practical AI/ML-based blockage prediction procedure that is operable in 3GPP compliant systems with taking its beam measurement reports and frame structure. The proposed prediction method uses the measurement report for beam management as input of a neural network. The computer simulation is conducted to show prediction performance of proposed method.

This paper proposes a practical multi-panel beam selection for higher rank line-of-sight (LoS) multiple input multiple output (MIMO) with joint phase-time array (JPTA). In multi-panel millimeter wave (mmWave) LoS-MIMO, spatial correlation varies significantly depending on the link distance between the transmitter (TX) and receiver (RX). Therefore, if a beam aligned with the LoS path is consistently selected across all panels, it can lead to a significant degradation in channel capacity (CC). To alleviate this issue, we have proposed a per-panel beamforming scheme that selects beams directing towards reflective paths at some panels, while the remaining panels select the direction of the direct path. However, it takes larger training overhead (OH) since beam switching is required to calculate the CC for possible beam pairs of panels. Our proposed solution in this paper involves leveraging JPTA that can form frequency dependent beam, a set of multiple beams for each frequency subband, in one orthogonal frequency division multiplexing (OFDM) symbol, leading to reduction of the training OH. Furthermore, a practical beam selection algorithm for each panel with JPTA is concretized for operating in 3GPP compliant systems with taking its reference signals, beam measurement reports and frame structure into account for the first time. The effectiveness of the proposed method is demonstrated through computer simulations, which show that the beam training (BT) algorithm utilizing JPTA can achieve high CC performance while significantly reducing the training OH.

Accurate path loss (PL) modeling is essential for evaluating and optimizing cell-free massive MIMO systems, especially in dense urban environments where traditional models fail to capture the complexity of real-world propagation. This paper introduces CUNEC (Cell-free massive MIMO for Urban Non-stationary Environments with Correlations), a novel PL model that accounts for spatial non-stationarity, inter-access point (AP)/user equipment (UE) correlations, and urban-specific propagation phenomena such as corner diffraction and street canyon waveguiding. CUNEC segments AP-UE paths by street order, models PL as a stochastic function of urban geometry, and integrates spatially correlated shadowing. The parameters are derived from large-scale ray tracing and validated against both additional ray tracing in New York, NY and real-world channel measurements in Los Angeles, CA. Compared to the conventional α–β model, CUNEC significantly improves accuracy in the considered urban propagation scenarios. An open-source dataset comprising over 30,000 AP locations and 128 UE positions is also released to support reproducible research and future system development.

This paper proposes a fast beam training algorithm utilizing true-time-delay (TTD) arrays suitably for high-rank line-of-sight (LoS) multiple input multiple output (MIMO) systems. In millimter wave bands, analog beamforming is generally utilized to compensate the effect of severe pathloss, and a beam training (BT) is typically operated to select beams which can obtain higher received signal power. However, it is difficult to secure the higher rank transmission in LoS-MIMO link with multiple sub-array panels, due to spatial correlation depending on a distance among Tx and Rx when selecting beam direction along LoS path commonly in all the panels. To alleviate this issue, we have proposed beamforming scheme, that select beams directing reflective paths at some panels, whereas the remaining panels select the direction of the direct path. However, it takes longer training time since BT is required to be operated in all the panels to select independent directions. Our proposed solution in this paper involves leveraging TTD arrays that can form a raibow beam, a set of multiple beams for each frequency, in one OFDM symbol, leading to reduction of the training time. Furthermore, we conduct effective angle-of-departure (AoD) and angle-of-arrival (AoA) estimation using TTD arrays to enhance the robustness of channel capacity for distance-dependent LoS-MIMO systems. The effectiveness of the proposed method is demonstrated through computer simulations. They show that the overhead of the proposed BT algorithm is smaller than that of BT algorithm using phased array (PA) while maintaining channel capacity.

This paper proposes an efficient spatial beam pre-diction method for millimeter-wave communication in multi-Transmission Reception Point (TRP) environments. We developed an AI-based framework leveraging beam measurements from multiple TRPs for beam management in 3GPP systems. We compare three architectural models: Single-TRP Input Single-TRP Output (STISTO), Multi-TRP Input Single-TRP Output (MTISTO), and Multi-TRP Input Multi-TRP Output (MTIMTO). The MTISTO approach achieves superior performance with an average 3.78% improvement in beam selection precision across all TRPs. Using ray-tracing simulations in realistic environments, our approach demonstrates significant beam prediction performance, corresponding to a 1.24 dB loss in received signal power compared to full beam search, while reducing measurement overhead by 93.75%. Additionally, MTISTO improves received signal power by an average of 0.37 dB (and up to 0.43 dB in challenging scenarios) compared to STISTO. This approach particularly excels in boundary regions between TRPs and in areas with building shadowing, demonstrating the feasibility of efficient spatial beam coordination for 5G-Advanced and beyond.

This research introduces a simplified beam training algorithm designed for line-of-sight (LoS) multi-input and multi-output (MIMO) systems, incorporating subarray-based beamforming capabilities at both the transmitter (Tx) and receiver (Rx), Typically, a conventional beam training algorithm is employed to maximize the signal power of each pair of Tx and Rx subarrays, leading to a scenario where each pair of subarrays directs its beam towards each other. However, in LoS-MIMO scenarios, the selected beams may not always be sufficient to achieve higher channel capacity due to spatial correlation dependency on the distance between Tx and Rx. To address this limitation, we have proposed leveraging reflection paths, such as ground reflection paths, and presented an effective beam training algorithm to implement this concept in real scenarios without requiring knowledge of the distance. In this paper, we propose a simplied beam training algorithm where the reflective direction determined by one subarray is shared with the other subarray. Experimental evaluation demonstrates for the first time that higher spatial multiplexing gain can be achieved without high dependency on the distance between TX and RX by controlling analog beam of each subarray appropriately under the existence of reflective paths.

Cell-free massive MIMO (CF-mMIMO), where each user equipment (UE) is connected to multiple access points (APs), is emerging as an important component for fifth-generation (5G) and sixth-generation (6G) cellular systems. Accurate channel models based on measurements are required to optimize the design and deployment of such systems. This paper presents an extensive measurement campaign for CF-mMIMO in an urban environment. A new “virtual AP” technique measures channels between 80 UE locations and more than 20, 000 possible micro-cellular AP locations. Measurements are done at 3.5 GHz carrier frequency with 350 MHz bandwidth (BW). The paper describes the measurement setup and data processing, shows sample results and their physical interpretation, and provides statistics for key quantities such as pathloss, shadowing, delay spread (DS), and delay window. We find pathloss coefficients of 2.9 and 10.4 for line-of-sight (LOS) and non line-of-sight (NLOS), respectively, where the high LOS coefficient is mainly because larger distance leads to more grazing angle of incidence and thus lower antenna gain in our setup. Shadowing standard deviations are 5.1/16.6 dB, and root mean squared (RMS) DSs of -80.6/-72.6 dBs, where dBs is defined as$t_{\mathrm { dBs}}=10\log _{10}(t_{\mathrm { sec}})$. The measurements can also be used for parameterizing a certain type of channel model, namely Cell-free massive MIMO for Urban Non-stationary Environment with Correlations (CUNEC), which will be reported in future work.

In this paper, we present a novel approach to analog beam selection in cell-free massive multi-input multi-output (CF-mMIMO) systems employing hybrid beamforming (BF) over millimeter-wave (mmWave) channels. In conventional CF-mMIMO systems with hybrid BF, an analog beam is selected by a central processing unit (CPU) from a predefined codebook. This selection is based on the received power at each access point (AP) and the fairness among user equipment (UE). However, this conventional method disregards the potential impact of interference and heavily relies on digital BF to mitigate it. Consequently, when utilizing low-complexity digital BF techniques such as maximum ratio transmission (MRT), the system's performance experiences degradation. Our proposed beam selection method takes into account both the received power and the interference power, effectively mitigating the influence of interference while maintaining an adequate level of received power. The numerical simulations validate the efficacy of our proposed approach.

This paper proposes an antenna design suitable for a mobile analog repeater with frequency-to-spatial multiplexing do-main conversion (FSMDC) among access and backhaul link. The relaying scheme with FSMDC, which we had proposed, converts wider band frequency multiplexing in access link into spatial multiplexing for the backhaul link only with analog circuits, and it achieves low latency and high capacity in millimeter wave spectrum. However, the typical scenario where the millimeter wave repeater is operated is LoS environment, and spatial multiplexing (i.e. LoS-MIMO) gain is not secured due to its dependency to the communication distance. For the robustness, the proposed antenna design is optimized by applying cost functions, that can achieve better channel capacity of FSMDC at any communication distance, as the fitness in genetic algorithm. The simulation results show its robustness to the communication distance of the LoS MIMO Links.

This paper proposes a low-complexity digital post-distortion (DPoD) technique using a frequency-domain attention-based neural network (FDA-NN) for compensating nonlinear distortions in power amplifiers supporting extremely wideband signals in the terahertz (THz) band. The proposed method efficiently captures the frequency-dependent memory effects of the power amplifier by applying a discrete Fourier transform (DFT) to the input signal and generating an attention signal that weights the frequency components according to their relevance for nonlinear distortion compensation. The performance of the proposed method is evaluated through experiments using a wideband power amplifier operating in the THz band and an orthogonal frequency-division multiplexing (OFDM) signal with a bandwidth of 4.8 GHz. The results demonstrate that the FDA-NN achieves comparable performance to more complex models, such as deep neural networks (DNN) and long short-term memory (LSTM) networks, while reducing the computational complexity by 48.9% and 78.7% in terms of floating-point operations (FLOPs), respectively. The proposed FDA-NN presents a promising solution for efficient nonlinear distortion compensation in future extremely wideband THz communication systems.

This paper investigates multi-user grouping (MUG) to improve energy efficiency (EE) in cell-free massive multiple-input multiple-output systems with access point (AP) switch on/off (ASO). ASO puts those APs into sleep mode which have little contribution with any spatially-multiplexed user equipments (UEs). However, in general, ASO can be less effective especially when spatially-multiplexed UEs are widely distributed in the area. Therefore, it is crucial to systematically integrate ASO and MUG, where UEs are divided into groups for spatial multiplexing. As a concrete procedure, we propose a practical MUG-ASO algorithm utilizing the criteria based on correlations of large-scale fading among UEs for effective EE improvement. The proposed algorithm can effectively select closely locating UEs into the same group for spatial multiplexing, and then it can put comparatively large number of APs into sleep mode at each time slot. Numerical example simulations show the effectiveness of the integration for EE improvement; in a typical case, the proposed algorithm can improve EE by 29.4% at 50-percentile compared to the conventional ASO algorithms with random MUG without severe degradation of spectral efficiency.

The design of cell-free massive MIMO (CF-mMIMO) systems requires accurate, measurement-based channel models. This paper provides the first results from the by far most extensive outdoor measurement campaign for CF-mMIMO channels in an urban environment. We measured impulse responses between over 20, 000 potential access point (AP) locations and 80 user equipments (sUEs) at 3.5 GHz with 350 MHz bandwidth (BW). Measurements use a “virtual array” approach at the AP and a hybrid switched/virtual approach at the UE. This paper describes the sounder design, measurement environment, data processing, and sample results, particularly the evolution of the power-delay profiles (sPDPs) as a function of the AP locations, and its relation to the propagation environment.

We consider a user-centric cell-free massive MIMO wireless network withLremote radio units, each withMantennas, servingKsingle-antenna user devices (UEs). Most of the current literature considers the regimeLM≫K, where theKUEs are active on each time-frequency slot, and evaluates the system performance in terms ofergodic rates. In this paper, we take a quite different viewpoint. We observe that the regime ofLM≫Kcorresponds to a lightly loaded system with low sum spectral efficiency (SE). In contrast, in most relevant scenarios, the number of UEs is much larger than the total number of antennas (think of a sport event withK~ 10, 000 users andML~ 200 antennas). To achieve high sum SE and handleK≫ML, users must be scheduled over the time-frequency resource. The number of active usersKact⩽Kmust be carefully chosen such that: 1) the network operates close to its maximum SE; 2) the active user set must be chosen dynamically over time in order to enforce fairness in terms of per-user time-averagedthroughput rates. The fairness scheduling problem is canonically formulated as the maximization of a suitable concave componentwise non-decreasingnetwork utility functionof the per-user rates. The intermitted user transmission due to scheduling imposes slot-by-slot coding/decoding, which in turn prevents the achievability of ergodic rates. Hence, we model the per-slot service rates using information outage probability. In order to obtain a tractable problem, we make a “decoupling” assumption on the CDF of the instantaneous mutual information seen at each UEkreceiver. We approximately enforce this condition by introducing a conflict graph that prevents the simultaneous scheduling of users with large pilot contamination conflict and propose an adaptive scheme for instantaneous service rate scheduling based on locally estimating the mutual information CDF at each UE. Overall, the proposed dynamic scheduling is the first to address such system dimensions with tens of thousand users in a scalable way, is robust to system model uncertainties, and can be easily implemented in practice.

Distributed antenna systems (DAS) have attracted significant attention for next-generation wireless systems. This largely motivated by the inherent macrodiversity, i.e., the fact that shadowing of the links to the different access points (APs) is different, which plays a major role in the reliability and capacity of such systems. However, shadowing correlation might reduce the benefits. In this paper, we provide the first stochastic geometry-based analysis of the impact of correlated shadowing on the uplink performance of DAS. Since the statistics of the SNR are determined by the second moment, we use Fenton-Wilkinson (F-W) moment matching and the second moment measure, to formulate it as triple integral. From this, we further construct two closed-form approximations with different degrees of accuracy and simplicity. Extensive Monte Carlo simulations validate the theoretical inferences and approximation accuracy. The results show that a large decorrelation distance will increase the variance of uplink SNR, and a small path-loss exponent leads to a stronger dependence of the second moment on the decorrelation distance. The results can also serve as the basis for future investigations of cell-free massive MIMO systems.

We consider fairness scheduling in a user-centric cell-free massive MIMO network, where L remote radio units, each with M antennas, serve $K \approx LM$ user equipments (UEs). Recent results show that the maximum network sum throughput is achieved where ${K_{{\text{act}}}} \approx \frac{{LM}}{2}$ UEs are simultaneously active in any given time-frequency slots. However, the number of users K in the network is usually much larger. This requires that users are scheduled over the time-frequency resource and achieve a certain throughput rate as an average over the slots. We impose throughput fairness among UEs with a scheduling approach aiming to maximize a concave component-wise non-decreasing network utility function of the per-user throughput rates. In cell-free user-centric networks, the pilot and cluster assignment is usually done for a given set of active users. Combined with fairness scheduling, this requires pilot and cluster reassignment at each scheduling slot, involving an enormous overhead of control signaling exchange between network entities. We propose a fixed pilot and cluster assignment scheme (independent of the scheduling decisions), which outperforms the baseline method in terms of UE throughput, while requiring much less control information exchange between network entities.

This paper suggests and analyzes adaptive bit allocation for the quantization of uplink signals of a cell-free massive MIMO (CF-mMIMO) system under limited fronthaul capacity. Specifically, we consider a CF-mMIMO system with hybrid processing, where at each access point (AP) a singular-value decomposition (SVD) reduces the number of streams that need to hauled, each stream is quantized with an adaptive number of bits, and a central processing unit (CPU) decodes the uplink signals. The hybrid processing, which the authors previously proposed, had been shown its potential to reduce fronthaul load without severe degradation of the spectral efficiency. However, as the bandwidths of the wireless system increases, the fronthaul capacity becomes comparatively tight, and the quantization noise would degrade the spectral efficiency severely. In order to improve the performance under such a scenario, this paper proposes algorithms for adaptive bit allocation of the output streams, based on the optimization of the average SNR, or the sum capacity. In addition, appropriate selection of the number of streams of the hybrid processing in each AP is also discussed. Computer simulations verify the effectiveness of these proposed methods.

In this paper, we propose low-complexity digital predistortion (DPD) schemes based on FastGRNN to compensate for the nonlinearity of RF power amplifiers. Conventionally, high-precision recurrent neural network (RNN) models, such as long short-term memory (LSTM) and gated recurrent unit (GRU), have been used to model the behavior of amplifiers, and their excellent compensation performance has been shown in terms of error vector magnitude (EVM) and adjacent channel power ratio (ACPR) has been demonstrated. However, their complex structures result in high computational complexity. To solve this issue, the proposed method is designed to significantly reduce the complexity without significant performance degradation by appropriately applying the FastGRNN models to the DPD. Complexity analysis and experiments using a power amplifier in the 2.0 GHz frequency band showed that the proposed method achieved comparable EVM performance to LSTM with 29.2% floating point operations (FLOPs) and 27.1% trainable parameters.

The pilot contamination in cell-free massive multiple-input-multiple-output (CF-mMIMO) must be addressed for accommodating a large number of users. In previous works, we have investigated a decontamination method called subspace projection (SP). The SP separates interference from co-pilot users by using the orthogonality of the principal components of the users’ channel subspaces. For CF-mMIMO system with SP, non-overloaded pilot assignment (PA) and overloaded PA can be considered. Non-overloaded PA, where each radio unit (RU) does not assign the same pilot to different users, limits the number of associated RUs per each UE and this reduces the potential spectral efficiency (SE) of the system. On the other hand, non-overloaded PA reduces channel estimation error induced by contamination. This paper compares non-overloaded PA and overloaded PA, and introduces overloaded PA methods adjusted for the decontamination in order to improve the sum SE of CF systems. Numerical simulations show that the overloaded PA methods give higher SE than that of non-overloaded PA at a high user density scenario.

Cell-free massive multi-input multi-output (CF-mMIMO) systems are one of the key technologies for 6G. Currently, performance assessment of such systems is hampered by the fact that there are no specific path loss (PL) models for CF-mMIMO. Conventional PL models based on Euclidean distance and log-normal shadowing assuming spatial stationarity across coverage area are usually employed for simplicity but show significant deviations from reality particularly in urban environments, which are the main deployment scenario for CF-mMIMO. In this work, we provide the first realistic channel model for CF-mMIMO systems in urban environments, introducing non-isotropic, non-stationary behavior in different parts of street canyon locations and incorporating both correlations between access points, and between user equipments. Simulation results demonstrate the superior reproduction of typical PL values in urban street canyons.

This paper proposes a clustering scheme suitable for a centralized millimeter wave Cell-Free massive MIMO with hybrid beamforming (BF). Conventionally AP-wise clustering schemes have been proposed for a decentralized architecture, that suppresses multi-user interference at each access point (AP) with local digital precoding and analog BF of each radio frequency (RF) chain, in order to obtain better performance efficiently. However in the centralized architecture, designing the precoder and cluster at central processing unit (CPU) with considering all RF chains of all APs together, the AP-wise clustering could not fit well especially when a large number of UEs are multiplexed. Because the RF chains with high coupling loss (the sum of the path loss and BF gain of the analog beam) could be included in the APs of the cluster to each UE. The proposed method forms a cluster on a per-RF-chain basis, and can select RF chains with lower coupling loss for each UE at the CPU regardless of the AP by incorporating the analog BF gain of each RF chain to form a cluster for each UE. Through simulation evaluations, we show that the hybrid BF with the proposed clustering scheme can obtain the superior spectral efficiency while effectively reducing the complexity of the centralized digital precoding.

Cell-free massive multiple-input multiple-output systems are expected to provide faster and more robust connections to user equipments (UEs) by cooperation of a massive number of distributed access points (APs). Energy efficiency (EE) is becoming an important indicator to design and operate networks; to improve EE, use of sleep-mode of APs (SMA), also called AP switch on/off, for selected APs has been investigated. Although previous works analyze the performance of SMA in the presence of user-centric clustering (UCC), these two techniques are assumed to not affect each other. In this paper, we propose a new greedy combining algorithm (GCA), where SMA and UCC work alternately to obtain better performance, and show its superiority over a conventional algorithm. Example simulations show that GCA can achieve 44% higher total EE for 8 UEs and 59% for 16 UEs with 64 APs. Additionally, GCA also provides higher minimum spectral efficiency thanks to its structure of the algorithm.

This paper proposes hybrid signal processing schemes for the uplink cell-free massive MIMO; these schemes serve to reduce fronthaul loads to obtain a scalable centralized processing architecture. In this architecture, received signals of multiple receive antennas at the access points (APs) are compressed into fewer streams by local spatial signal processing and then the streams are forwarded to a central processing unit (CPU) via fronthaul, and the CPU performs scalable processing for channel estimation and signal detection based on partial minimum mean squared error (PMMSE). We propose two kinds of concrete local signal processing methods for this hybrid processing architecture: one is based on MMSE, and the other is based on principal component analysis (PCA) with eigenvalue decomposition (EVD). For the EVD, a local vector selection based EVD (LVS-EVD) that selects uniform number of eigenvectors for each AP in a standalone way, and a global vector selection based EVD (GVS-EVD) that determines the dimensions of the weight vector of each AP in the CPU, are further considered. Computer simulations verify the approaches and compare their effectiveness. In addition, we show that the GVS-EVD scheme can be operated with significantly reduced fronthaul loads without severe performance degradation.

Measurements of the propagation channels in real-world environments form the basis of all realistic system performance evaluations, as foundation of statistical channel models or to verify ray tracing. This is also true for the analysis of cell-free massive multi-input multi-output (CF-mMIMO) systems. However, such experimental data are difficult to obtain, due to the complexity and expense of deploying tens or hundreds of channel sounder nodes across the wide area a CF-mMIMO system is expected to cover, especially when different configurations and number of antennas are to be explored. In this paper, we provide a novel method to obtain channel data for CF-mMIMO systems using a channel sounder based on a drone, also known as a small unmanned aerial vehicle (UAV). Such a method is efficient, flexible, simple, and low-cost, capturing channel data from thousands of different access point (AP) locations within minutes. In addition, we provide sample 3.5 GHz measurement results analyzing deployment strategies for APs and make the data open source, so they may be used for various other studies. To our knowledge, our data are the first large-scale, real-world CF-mMIMO channel data.

Cell-free massive multiple-input multiple-output systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G. Recently, a measurement-based evaluation revealed that the performance of a semi-distributed deployment, where each AP has multiple antennas, is comparable to that of a fully-distributed deployment in terms of coverage in an indoor environment while reducing the number of APs. In this paper, we analyze the performance of various antenna distribution configurations, and show that semi-distributed deployments outperform fully-distributed deployment remarkably from both spectral and energy efficiency points of view. These characteristics of semi-distributed deployments enable us to construct more cost-effective networks, which is an important indicator to deploy the systems in real environment.

Carrier aggregation (CA) has been adopted in order to ensure wide bandwidth for high-speed data communications in Long-Term Evolution (LTE)-Advanced. In most existing reports related to the CA technique, processing on the physical (PHY) layer is operated per component carrier (CC). This is because the independence of each CC in the PHY layer is assumed to maintain backward compatibility with the LTE system. In this case, the frequency diversity gain will be limited by the bandwidth of each CC. In this paper, we propose an effective precoding operation called cross-carrier precoding (CCP), in which the precoding operation is executed across CCs transmitted on different frequency bands for further enhancement of CA. By applying CCP, the modulated symbols are spread over different frequency bands resulting in a significant frequency diversity gain. The results of numeric computer simulations show that the required SNR for obtaining a PER of 1% is reduced at most by 3.5 dB in the vehicular A model and 2.5 dB in the typical urban model by applying CCP.

This paper presents an adaptive impedance control of antennas on user equipment (UE) for a Long Term Evolution (LTE) system that supports MIMO reception in downlink and single antenna transmission in uplink. The proposed control method balances the performances of both links, duplexing with a frequency division duplex (FDD), without additional information from eNode B (eNB). In addition, computer simulations verify the effectiveness of the proposed method in the usage environment, where a user holds the UE under multipath propagation channels.

To utilize wireless sensor networks (WSN) effectively, maximization of network lifetime and minimization of response time to data queries are very important issues. To enhance the functionality and availability of conventional WSNs, we propose the new concept of a Multi-Mode Wireless Sensor Network (MM-WSN) in which nodes with only limited battery power are equipped with different types of radio access technologies. Nodes in a MM-WSN can use both single-hop and multi-hop delivery of data and select the most suitable option according to the situation. In this paper, we show that our proposed method provides a radio access selection method that achieves energy-efficient and high-speed data reports. The effectiveness of our proposal is shown by means of computer simulations.

This paper proposes a new way of measuring signal to interference and noise ratio (SINR) at a low level of power consumption for vertical handover. In order to select the most suitable radio access networks (RAN) in vertical handover, the SINR of the alternative RAN should be measured at a certain interval while communicating with the existing RAN. In our proposal, the SINR measurement interval for the alternative RAN is controlled on the basis of SINR fluctuations in order to maintain high tracking ability and reduce power consumption during monitoring operations for vertical handover. In addition, a simple probability inequality is applied to detect SINR fluctuations with high precision and achieve low computational complexity. The effectiveness of the proposed monitoring method was verified through computer simulations and the results showed that the averaged SINR could be measured to an accuracy of about 1 dB while maintaining sleep mode at about 30%.

This paper presents an energy efficient radio access network (RAN) selection for vertical handover between heterogeneous networks. The proposed RAN selection switches evaluation bases by application and adaptively selects a RAN with low energy consumption. In addition, the selection employs a penalty function that avoids discarded vertical handovers to reduce handover overhead and network loading by reducing the integrations.

This paper proposes a new blind adaptive equalizer based on maximum-likelihood sequence estimation (MLSE) for frequency selective mobile radio channels. A conventional blind MLSE equalizer with a single fractionally spaced transversal filter can cope with timing offset, but it suffers from a performance degradation due to the ambiguity of blind channel estimation. To avoid this ambiguity, this paper proposes a different type of fractional sampling in which separate symbol-spaced channel estimations are performed in the respective fractional sampling phases. The estimation employs a recursive form using the Moore-Penrose generalized inverse matrix. The equalizer combines the separate channel estimation errors, and provides the sum for the Viterbi algorithm processor as the branch metric, which tremendously reduces the probability that the correct solution is turned into an ambiguous false solution. Computer simulation demonstrates the effectiveness of the proposed equalizer on frequency selective fading channels.