GB2466252A - Underwater multiple output multiple user acoustic communication system - Google Patents

Abstract

A underwater acoustic communication system between N acoustic transmitters and M remotely disposed acoustic signal receiver to form an underwater network comprises transmitters and receiver to share a common frequency band;ranking signals representing the power of at least two signal transmissions received by said receiver from said transmitter;observing a signal to determine if it has expanded or contracted within a predetermined time period;equalising Inter-Symbol Interference from multipath propagation and phase fluctuations; andcancelling Multiple Access Interference generated from receiving two or more signal transmissions.

Description

MULTIPE OUTPUT MULTIPLE USER METHODS AND/OR SYSTEMS OF UNDERWATER

ACOUSTIC COMMUNICATION

Field of the Invention

The invention relates to methods and/or systems of underwater acoustic communication as well as receivers, transmitters, and base units configured to carry out the methods.

Background and review of Art known to the Applicant The idea of several transmitters sending information simultaneously via a communication channel dates back to Thomas A. Edison in 1873, with the invention of the duplex [1].

Simultaneous transmission of two telegraphic messages travelling in the same direction through the same wire was enabled with this revolutionary system. The messages were encoded by changing the polarity of one, whilst a change of absolute value was performed for the other. In modern day context, multiple access communication exists in numerous situations. Mobile telephones transmitting to a base station, local area networks, packet-radio networks are just few of the examples of multiple access communication. The common feature in these communications is the use of a common channel through which transmissions take place.

The translation of the concept of multiple access communication to underwater communications, employing sound propagation, is an area that is an immense task beset by many underwater environmental problems.

The receiver usually observes a superposition of signals sent by the active transmitters, as shown in Figure 1.

Sound waves are the principal means for long-distance wireless communication in the ocean. Electromagnetic (EM) waves, carried by wires or fibres on the ocean bottom, offer high reliability and useful bandwidth. However in wireless mode, EM waves do not propagate over long distances underwater, except in the Extremely Low Frequency (ELF).

The history of underwater sound wave propagation can be traced back several centuries to the fundamental discoveries and accomplishments of scientists in many diverse fields.

Towards the end of the 15th century, Leonardo da Vinci wrote [4]: "If you cause your ship to stop, and place the head of a long tube in the water and place the outer extremity to your ears, you will hear ships at a great distance from you" In 1687, Sir Isaac Newton published the first treatment of the theory of sound, where he was able to relate the propagation of sound in fluids to measurable physical quantities such as density and elasticity [4]. In 1827, a Swiss physicist, Daniel Colladon, and a French mathematician, Charles Sturm, measured the speed of sound in water at Lake Geneva in Switzerland. Although they only used a simple light flash, coupled with the sounding of an underwater bell, to obtain the measurement, the value obtained was close to the accepted value today [5]. Lord Rayleigh established the basis for acoustic theory in 1877 [6]. His work covered the generation, propagation, and reception of sound in a rigorous manner.

The first practical application of underwater sound came into use in the late 19th century.

Ships employing a submarine bell and by timing the interval between the sound of the bell and a foghorn, sent off simultaneously in parallel, a second ship could then determine its position from the ship where the foghorn and bell were installed.

In the 2Oth century, there was considerable progress in improving underwater acoustic communication both for military and civilian purposes. An extensive historical review of the development of practical acoustic applications for both World Wars is well documented in [7] -[8]. Coverage of underwater acoustic communication prior to 1967 is recorded in [9]. A comprehensive review of acoustic telemetry prior to 1983 is referenced in [10].

Recent Advancement Recent techniques that have advanced the field of underwater acoustic communication are highlighted in this section. One pioneer for an underwater communication system, which did not employ diversity techniques was the Gertrude system [ii]. This system was used for communication with submarines. It used analogue amplitude modulation (AM), and with careful placement of the hydrophones, compensation for multipath propagation from distinct angles of arrival was achieved. The single-sideband Gertrude system is still in operation for diver communication systems [12]. These perform well for vertical or ultra-short horizontal links environments, with negligible multipath propagation. The development of digital systems, not employing diversity techniques, was reported as early as 1960 [13] -[14]. The Benthic digital system described in [15] allows 4800 bps transmission. Since no diversity technique was adopted, data transmission was performed vertically through the water column in order to minimise multipath propagation effects.

The above systems were not really suitable for horizontal shallow water channels which exhibit severely delayed spread due to muttipath propagation. Alternative systems that were based on non-coherent digital modulation, Frequency Shift Keying (FSK), had been traditionally accepted as the only alternative for shallow water channels which exhibit rapid phase variation. Although non-coherent detection eliminates the need for carrier phase tracking, it does not solve the problem of multipath propagation. In order to combat the problem of inter-symbol interference (ISI), the non-coherent FSK system had to employ guard times, which were inserted between each successive transmitted data, to ensure that the reverberation effects were kept minimal at the receiver. Non-coherent receiver systems are usually classified by their explicit diversity technique, where explicit diversity could be characterised by intentionally transmitting the data through distinct subchannels in time, frequency, geometric space, or waveform space. Due to the independence of the subchanne( fading processes, the channel error probability decreases exponentially in the number of transmissions, or diversity order [16]. The digital acoustic telemetry system described in [17] was one communication link that used explicit diversity technique. This system was designed to operate in frequency-selective multipath fading environments having extreme phase instability. Coded data using multiple FSK (M-FSK) was adopted for data transmissions operating in the 45-55 kHz band. A 30 kHz header tone was used for coarse, word synchronisation and a continuous 60 kHz pilot tone was used for Doppler tracking. n one of the implementations of a digital acoustic telemetry system, a 400 bps digital coded data stream was transmitted with an (8,4) Hamming code.

The Hamming codeword elements were selected from eight tones spanning at 2 kHz per each baud period. The receiver then performed an estimation of the Doppler shift using a phase-locked loop (PLL) whose output was used to adapt the down-conversion nominal carrier, which was at 50 kHz. The hopping pattern was then tracked in order to determine the frequency span for the current active word and inverse Fast Fourier Transform (FFT) was used to extract the squared magnitudes of the received gated tones. Retrieval of the 8-bit code word was achieved with non-coherent soft decision detection. Apart from this system, several commercial systems following the digital acoustic telemetry format that allowed reliable transmission through severe reverberant muttipath channels with low system complexity were reported in [181. Provided the PLL was successful in tracking the pilot tone, such systems could tolerate Doppler shift for up to 600 Hz. The Doppler compensation performance deteriorated to 20-25 Hz when the pilot PLL lost lock on the received signal. Another non-coherent M-FSK telemetry system operating in the 20-30 kHz band with maximum throughput of 5kbps was reported in [191. Here the frequency band was divided into 16 subbands, in each of these subbands a 4-FSK data signal was transmitted. The system reported successful implementation for telemetry over a 4 km horizontal shallow water channel and a 3 km deep ocean vertical path. In the case of a 700 m shallow water path, the error probabilities for the transmitted uncoded data were recorded in the order of 10-2 -i03.

With the aim of increasing the usage of the bandwidth-limited underwater channel, research focus shifted from non-coherent modulation technique to phase-coherent techniques such as Phase Shift-Keying (PSK) and Quadrature Amplitude Modulation (QAM). These methods encode data information into the phase as well as the amplitude of the transmitted signal.

One of the earliest phase-coherent systems for underwater acoustic communication was reported in [20] where ISI was compensated by a coherent echo cancellation technique.

Adaptive equalisation adopted for high-speed underwater acoustic communication was reported in the early 1990s [21}. In a short-range communication link of -60 m the system reported in [22] had a throughput of 500 kbps, with an operating carrier frequency of 1 Mhz. The main application of this system was for undersea robotic maintenance of submerged platforms. A 16-QAM data format was used with an adaptive equaliser adopting the Least Mean Square (LMS) algorithm. The error probability was in the range of i O which was measured over the averaged acquired data packets. The vertical link image transmission system developed by [23] used a phase non-coherent differential P5K (DPSK), with Least Mean Square (LMS) adaptive equalisation, operating at 20 kHz carrier frequency with a data throughput of 16 kbps for surface transmission over 6.5 km. The error probability achieved from field trials was in the range of iO with a Signal-to-Noise Ratio (SNR)ofl5dB.

A network telemetry system for shallow water medium ranges was developed in [24], where a direct-sequence spread spectrum (DS-SS) technique was adopted to aid rejection of muttipath propagation effects. This system had a data throughput of 600 bps, a spreading bandwidth of 10 kHz, with a 30 kHz carrier frequency band. Another development in network telemetry system was reported in [25], using a Quadrature PSK (QPSK) modulation data format with data throughput of 5 kbps. This system was configured for a six-node network operating with a 1 5 kHz frequency band. The problem of SI was tackled using a decision feedback equaliser using the Recursive Least Square (RLS) algorithm.

Methods for Multipath and Doppler Compensation To achieve high data rate transmission, telemetry systems based on phase-coherent data signalling method had to deal with the ISI problem that result from muttipath propagation effect. One of the earliest records of pure phase-coherent data transmission with SI compensation was reported in [26]. The signal processing method was based on joint synchronisation and a fractionally spaced decision-feedback equalisation technique adopting the RLS adaptive algorithm. This system was demonstrated through field tests to exhibit a data throughput of 2 kbps over long range channel, 20 km, and 4 kbps in medium range channel, 5 km. Array processing was adopted in [27] to eliminate or reduce multipath propagation. This approach seeks the use of an array of transmitters to excite a single path of propagation. Rejection of ISI was dealt with by careful positioning of a long receiving array. The channel medium was deemed rapidly changing for an adaptive equatiser to perform tracking in order to achieve the minimum point of the error performance surface. The system of [27] employed a tong array of receiver elements to compensate for possible errors. Both Binary and Quaternary DPSK data signal were used, with data throughput of 10 kbps and 20 kbps respectively. Error probability was recorded to be in the range of 10-2 -iO. It was concluded that this type of configuration was found to be operationally more effective in short range communication.

An adaptive beamforming method used as a means of ISI rejection was reported in [28] - [29]. The adaptive beamformer uses a decision-feedback equaliser operating with the LMS algorithm to steer towards the signal of interest, while nulling other interfering signals. This system was tested in shallow water with a data throughput of 10 kbps with error probability in the range of 10_2 without equalisation and i03 with adaptive equalisation.

Recently, a Doppler compensation scheme adopting block-based interpolation with decision-feedback equalisation was proposed in [30]. The Doppler compensation was obtained by measuring the Doppler shift between two a priori known Linear Frequency Modulated (LFM) chirp' signals in the received data packet. An interpolator structure was then used to perform a sampling rate conversion of the input samples in order to compensate for the Doppler shift. With a data throughput of 10 kbps, an error probability was reported to be in the range of iO -i0. However, this block-based system assumed that Doppler shift variations are relatively small, under some circumstances this assumption does not stand. An alternative, decision directed Maximum Likelihood (ML) cost function used to estimate Doppler shift has been proposed [31]. This system offers a real-time signal processing approach compared to the block based processing as the received signal can be processed immediately whereas the block-based system requires a buffer to hold the received data between each LFM signal prior to Doppler compensation.

Multi-User Detection Network communication research had sparked increased interest in recent years due to the need for environmental data acquisition from fixed and mobile measuring platforms located in the continental sea, as shown in Figure 2. Apart from resolving the problem of ISI arising from multipath propagation, Doppler shifts, environmental noise etc, the receiver in such scenarios has the additional task of mitigating the effects of co-channel interference from other users in the network system. Therefore multiuser communication techniques [32] have to be considered for such underwater acoustic communication applications. Although Frequency Division Multiple Access (FDMA) or Time Division Multiple Access (TDMA) may be considered for underwater acoustic communication in such circumstances, both these techniques exhibits their own problems. In a bandwidth-limited channel, the network users are usually confined to sharing the same frequency band for data signalling. Therefore, the FDMA technique, which operates in orthogonal spectrum bands, will be wasting the already limited channel resources. TDMA technique is subjected to the problem of efficient time-slot allocation, which arises due to the long propagation delay. One possible solution to multi-user underwater network communication is to adopt Code Division Multiple Access (CDMA), where multiple users are allowed to transmit simultaneously both in frequency and time. However, adopting CDMA technique reduces overall data throughput. One recent multiuser detection technique adopting CDMA was reported in [33J. The fundamental principle of this multiuser system was an extension from the system of [26], where array processing, joint synchronisation, channel equalisation in the form of decision-feedback equalisation was adopted. Muftiuser interference cancellation was performed via feedback filters in a cross over manner. See H. K. Yeo's IEEE, lEE journal publication of 2001 and 2002, the proposed multi-user detection (MUD) systems operating in the passband frequencies are complex to realise with current DSP technology. This invention proposes in an embodiment a soft base-band decision multiple output multiple user (MOMU) which differs from the previous proposed strategies.

Summary of the Invention

In a first broad independent aspect, the invention provides a method of underwater acoustic communication between N acoustic transmitter or transmitters and M remotely tocated acoustic signal receiver or receivers to form an underwater network comprising at least one or any combination of the following steps: configuring said transmitters and said receiver to share a common frequency band; ranking signals representing the power of at least two signal transmissions received by said receiver from said transmitter; observing a signal contained within said signal transmission to determine if it has expanded or contracted within a predetermined time period; equalising Inter-Symbol Interference from multipath propagation and phase fluctuations; and cancelling Multiple Access Interference generated from simultaneous reception of a plurality of signals receiving two or more signal transmissions.

In a subsidiary aspect, N is greater or equal to 2.

In a second broad independent aspect, the invention provides a method of underwater acoustic signal transmission comprising the steps of arranging a number of distinct underwater acoustic devices with transmitters for acoustically transmitting to a base station with multiple-output hydrophones; and transmitting signals from said devices to said base unit over a common frequency bandwidth.

In a third broad independent aspect, the invention provides a method of underwater acoustic communication with a base station and a plurality of underwater devices forming a network of underwater receivers and transmitters, comprising the steps of broadcasting signaLs to a plurality of underwater devices from a base station; detecting power received for each underwater response; ranking said devices in terms of power; and broadcasting signals to a selection of underwater devices to increase power transmission for reception.

In a fourth broad independent aspect, the invention provides a method of underwater acoustic communication with a base station and a plurality of underwater devices forming a network of underwater receivers and transmitters, comprising the steps of broadcasting signals to a plurality of underwater devices from a base station; and switching between multi-element and single element receiver output modes dependent upon the evaluation of power, multi-path and noise.

In a fifth broad independent aspect, the invention provides a method of underwater acoustic communication with a base station and a plurality of underwater devices forming a network of underwater receivers and transmitters, comprising no step of broadcasting to transmitters to increase power transmission; and comprising the steps of sending receiver signals to a MOMU detector and returning a signal to individual transmitters.

In a sixth broad independent aspect, the invention provides a method of underwater acoustic communication with a base station and a plurality of underwater devices forming a network of underwater receivers and transmitters, comprising the step of transmitting an identifying code utilising a hybrid of Pseudo-Random Binary Sequences and Linear Frequency Modulation ([FM).

In a subsidiary aspect, the method comprises the step of back-to-back mis-matched filtering.

In a further subsidiary aspect, the method comprises the step of power ranking by measuring the correlation peak output for each transmitter.

In a seventh broad independent aspect, the invention provides a method of underwater signal processing comprising the steps of: io receiving a signal; employing a window for observing a change of frequency component for a base-band symbot period which is carrier modulated to a passband signal; and employing a fast butterfly FF1 (Fast Fourier Transform) for a time-domain window containing samples to determine a passband frequency; whereby Doppler shift is determined.

In a subsidiary aspect, said window takes the form substantially as defined in equation (12).

In a further subsidiary aspect, said fast butterfly FF1 takes the form substantially as defined in equation (13).

In a further subsidiary aspect, said method further comprises the steps of 1) compensating by adding or substracting a frequency component; and then 2) down-mixing to baseband signals.

In an eighth broad independent aspect, said method of underwater signal processing comprises the step of providing one or more adaptive feedforward equalisers.

In a subsidiary aspect, said method comprises the steps of providing said adaptive feedforward equalisers with equaliser taps; sending predetermined training sequences for adapting said equaliser taps' weights; and switching to a decision directed mode.

In a further subsidiary aspect, said adaptive feedforward equaliser is configured to have a complex output substantially as defined in equation (14).

In a further subsidiary aspect, said adaptive feedforward equaliser defines a symbol error estimation substantially as in equation (15).

In a further subsidiary aspect, said adaptive feedlorward equaliser defines a mean square error substantially as in equation (16).

In a further subsidiary aspect, said adaptive feedforward equaliser is configured to have a soft decision complex output based on maximum likelihood estimation where a probability density function is derived from a series of computed complex outputs as in equation (1 7) and the maximum Likelihood estimation of said soft decision complex output is derived from equation (18).

In a further subsidiary aspect, said method incorporates a hard decision complex output operating in a first mode suitable for single user operation; and a second mode suitable for multi-user operation.

In a further subsidiary aspect, said first mode suitable for single user operation incorporates a data mode based on the adaptive decision of a user; the power estimate is set to zero; the maximum likelihood decision is set to zero; and the soft base-band decision is set to zero.

In a further subsidiary aspect, said second mode suitable for multi-user operation incorporates a multiple access interference cancellation step substantially based on equation (18) for soft base-band decision and substantially based on equation (19) for hard base-band decision.

In a further subsidiary aspect, said method incorporates interference cancellation steps substantially based on any one of equations (20) to (26).

In a ninth broad independent aspect, the invention provides a method of underwater signal processing, comprising the steps of: determining an input vector for single or multiple channel inputs to each user depending upon channel conditions; determining initial bits/symbols for N users from their corresponding adaptive feedforward equaliser unit whilst selecting a power estimation; and setting soft-decision base-band estimate to zero; feeding a power estimate and a complex soft decision base-band output from a first user to a second user's decoding block; regenerating bits/symbols with a weighting factor and phase correction; substracting the regenerated bits/symbols from a received signal to obtain a modified received signal; passing said modified received signal to a further stage for the removal of a further user signal; repeating the process of decision estimation, regeneration, weighting and interference cancellation for N-i stages.

In a subsidiary aspect. the method further comprises the step of employing a buffer window to store the time reference or time delay estimation for each user.

In a further subsidiary aspect, a signal vector is fed in a soft-decision base-band parallel interference cancellation process which substantially takes the form of equation (20).

In a further subsidiary aspect, the adaptive feedforward equaliser derives retrievable information which for a user is decoded by summation of maximum likelihood at each stage according to equation (21).

In a further subsidiary aspect, the method comprises the step of obtaining energy statistics from received signals to rank users in descending power.

In a further subsidiary aspect, the method further comprises the step of neglecting users which are weaker than an intended user.

In a further subsidiary aspect, the method further comprises the step of linearly relating the time complexity per bit to the number of users in the system.

In a tenth independent aspect, the invention provides a method of underwater signal processing, comprising the steps of: obtaining energy statistics from received signals to rank users in descending power; performing adaptive symbol estimation of the strongest user; regenerating and cancelling estimated result from received signal; passing substracted received signal to the next weaker user for decoding; and repeating the preceding steps to decode signals of other users.

In a subsidiary aspect, the method comprises the steps of: providing a feedback loop to cancel out summed effects from other users; summing, regenerating, weighting with phase correction and cancelling output decisions of a stronger user from other users to obtain a substracted signal; and decoding the subtracted signal whilst assuming that only background noise is present.

In an eleventh broad independent aspect, the method of underwater signal processing, comprises the steps of: obtaining energy statistics from received signals; randomly selecting a first user if equal energy statistic are obtained for each user; performing adaptive symbol estimation of the first user; regenerating and cancelling estimated result from received signal to obtain substracted received signal; passing substracted received signal to the next user for decoding; repeating the preceding steps to decode signals of other users; providing a feedback Loop to cancel out summed effects from other users; summing, regenerating, weighting with phase correction and cancelling output decisions of said first user from other users to obtain a substracted signal; and decoding the subtracted signal whilst assuming that only background noise is present.

In a twelfth broad independent aspect, the invention provides an underwater communication system, for communication between N acoustic signal transmitter or transmitters and M remotely located acoustic signal receiver or receivers to form an underwater network comprising: means for said transmitter and said receiver to share a common frequency band for at least one signal transmission; means for ranking signals representing the power of at least two signal transmissions received by said receiver from said transmitter; means for said receiver to identify said transmitter; means for observing the information contained within said signal transmission to determine if said signal expanded or contracted within a predetermined time period; means for equalisirig Inter-Symbol Interference from multipath propagation and phase fluctuations; and means for Multiple-User Detection within said network.

In a subsidiary aspect, said transmitter and said receiver are configured to assign a predetermined code for establishing a transmission channel between said transmitter and said receiver over said frequency band.

In a further subsidiary aspect, said transmitter is configured to increase the power for said information transmission when said received ranked signals are below a predetermined power threshold.

In a further subsidiary aspect, said receiver is configured not to broadcast to transmitters to increase power transmission; the receiver incorporates a MOMU detector; said receiver is configured to send received signals to said detector and return a signal to individual transmitters.

In a further subsidiary aspect, said transmitter transmits an identifying code to said receiver.

In a further subsidiary aspect, said identifying code utilises a hybrid of Pseudo-Random Binary Sequences and Linear Frequency Modulation.

In a further subsidiary aspect, said means for equalising Inter-Symbol Interference further comprises a training means and a decision means.

In a further subsidiary aspect, said training means comprises a predetermined training code which simulates at Least one received signal to initially configure said means for equaLising Inter-Symbol Interference.

In a further subsidiary aspect, said decision means receives a pLurality of transmitted symbols embedded within said signal; said symbols represent said information contained within said received signals for subsequent adaptation and equalisation.

In a further subsidiary aspect, said means for Multiple-User Detection further comprises a means for cancelling Multiple Access Interference generated within said network.

In a further subsidiary aspect, said means for cancelling Multiple Access Interference is configured to utilise said received transmitted information; said received information is weighted and reconstructed to form said received signal.

In a further subsidiary aspect, said received transmitted information is weighted and reconstructed to form said received signal is repeated over a plurality of stages.

In a further subsidiary aspect, the means for cancelling Multiple Access Interference within said network is MOMU soft-decision Parallel Interference Cancellation.

In a further subsidiary aspect, said means for cancelling Multiple Access Interference is configured to obtain the highest ranked signal representing a received signal; said highest ranked signal is multiplied with a weighting factor with phase correction and is then subtracted from the received signal.

In a further subsidiary aspect, said means for cancelling Multiple Access Interference is configured to be repeated over a plurality of stages; each stage processes the next ranked signal which is weaker than the previous signal.

In a further subsidiary aspect, said means for cancelling Multiple Access Interference within said network is MOMU soft-decision Successive Interference Cancellation.

In a further subsidiary aspect, one or more decision means are summed together, multiplied with a weighting factor, subtracted from the received signal and fed back to the highest ranked signal for Multiple Access Interference cancellation.

In a further subsidiary aspect, said means for cancelling Multiple Access Interference is configured to be repeated for each ranked signal.

In a further subsidiary aspect, said means for cancelling Multiple Access Interference is MOM U soft-decision Recursive Interference Cancellation.

In a thirteenth broad independent aspect, the invention provides an underwater device configured to operate the method and/or system of any of the preceding aspects.

Brief description of the figures

Figure 1 Multiple Output Multiple User (MOMU) System Level Architecture Figure 2 Underwater virtual network Figure 3 Data packet structure Figure 4 Observation window for the different time of arrival for uplink signal transmission Figure 5 Multiple Output Multiple User (MOMU) System Level Architecture Figure 6 Physical MOMU System Figure 7 Analog Front End Figure 8 System Block Diagram Figure 9 Receiver Block Diagram Figure 10 Distinct Multiple Subsea Devices Communicating Acoustically to the Base-Station MOMU Dection Figure 11 Auto-Correlation of a 13 chip Barker Sequence Figure 12 Cross-Correlation of 2 Gold code 15 chip Sequences Figure 13 Side Lobes with Zero Values in between 2 Gold Sequences Figure 14 Power ranking module Figure 15 Doppler Shift Symbol Period Window Figure 16 Single Output Single User Adaptive Feedforward Equaliser Receiver Figure 1 7 Multiple Output Single User Architecture Figure 18 Soft Decision Base-Band Parallel Interference Cancellation Figure 19 Soft Decision Base-Band Successive Interference Cancellation Figure 20 Soft Decision Base-Band Recursive Successive Interference Cancellation Figure 21 views illustrating a hybrid LFM/PRBS Gold Code Figure 22 Time domain channel impulse response

Detailed description of the figures

Overview An underwater acoustic network consists of a sub-surface device (base-station), communicating acoustically to and from a plurality of mobile sub-sea devices, underwater network. The downlink transmission (base-station to sub-sea devices) consists of a command and control where the base-station will send a broadcast signal to plurality of subsea devices to transmit wireless acoustic signal to the base-station. In the up-link transmission, sub-sea devices users transmit acoustic information over a time-varying channel. The base-station is then required to demodulate the received data for each user as if there were only one user present, while treating the others as additive noise. The communication operation between base-station and sub-sea devices is interchangeable.

Each node is bi-directionaL in an embodiment of the invention. However, this idea is beset by underwater environmental issues like multipath (give rise to Inter-Symbol Interference ISI), signal attenuation, Doppler Shift, noise etc and system issues like co-channel interferences or multipLe access interferences (MAI).

A Multiple Output Multiple User (MOMU) method and device is provided for resolving underwater environmental artefacts and asynchronous muLtipLe access communication operating in the same frequency band in the physical layer.

System Model In an upLink asynchronous communication model, shown in Figure 4, each user is observed to be arriving asynchronously at the base-station; this process is applicable or identical to down-link communication. The data field may consist either of 2 blocks: 1) The data field of the transmitted data consists of a header, Sk' user identification IDk' training sequence, T, and Dk variable data Length. The header is used for initial "coarse" time synchronisation or clock recovery, and the training sequence is used to provide initial training to adapt the equaliser tap weights.

2) Without the training sequence, this is to allow events where signal drops off and the receiver needs to self-adapt to the data stream Reference can be made to figure 3.

The received signal at the base-station can be expressed in complex form, r(i)where r(t)=1 akhkbk (t -r,j+ n(t) (1) where ak(t), hk(t), bk(t) and denote, for each user k, the received amplitude, channel transfer function, transmitted bit sequence and time delay respectively, and n(t) is the Additive White Gaussian Noise (AWGN). In order to model an asynchronous reception, consideration is given to the transmitted bit stream, bk, of the kth user which takes the form b +/ +I k __ /.f_ /./ (2) Thus generalising (1) becomes rO)LzrlLM akhkbk (t -jT -Tk) + n(t) (3) where T is the symbol duration. Symbol-epoch offsets are defined with respect to an arbitrary origin, Tref = 0, which is the time origin of the first detected user at the base-station, as shown in Figure 4.

Physical System Initial work on multi-user detection strategies was demonstrated from the optimal multi-user receiver and its potential improvements in network system capacity and near-far resistance [32J. However, the optimal multi-user receiver was deemed far too complex to be implemented in a practical system, which has led to much research in alternative sub-optimal multi-user detection approaches.

This section presents the design and development of several novel Multi-Output Multi-User (MOMU) detection strategy embodiments of the inventions for underwater acoustic communications, Figure 5 shows the system level architecture of the MOMU receiver and Figure 6 shows the top-level physical architecture of the MOMU receiver.

Figure 6 shows the MOMU system block diagram Figure 7 shows the analog front end architecture of the system Figure 8 shows the system block diagram of the down-mixing for the single output receiver Figure 9 shows the block diagram of the single output receiver Multiple distinct sub-sea devices transmitting over a same frequency bandwidth or distinct bandwidth, thereby increasing data throughput (Embodiment of the Invention) In an embodiment of the invention, multiple distinct subsea acoustic devices transmits individual data stream acoustically via a projector to the base-station Multiple-Output hydrophones. Each distinct subsea device shares a common same bandwidth for transmission, it is possible that the same bandwidth may be multiplexed and split into multiple carriers to reduce the propagation delay spread within the symbol period for each user bandwidth. The acoustic streams transmitted by the subsea devices go through the underwater channel medium which can be defined by as a matrix channel which consists of multiple paths between the subsea devices transmit projector and the multiple-output receive hydrophones at the base-station. The simplified equation for the received vector, r, at the base-station from equation (1) is r Hkbk + k (4) where Hk, bk and k are the underwater channel matrix, transmitted acoustic data and noise vector for K users respectively. The number of multipath solutions or resolution of multipath at the base-station will depends on the physical limitation of the hydrophones available, 1, Figure 10. The number of L hydrophones, L -1 equates the number of solutions for multipath resolution.

ID detection Channel Capacity For the number of user capacity contain within the underwater network, an embodiment of the invention can operate with a closed loop or an open loop defined as follows: 1) Closed loop -Base-station wilt broadcast to plurality of subsea devices and detects the power received for each sub-sea response which will vary in distances from the base-station. The base-station will then rank the power distribution of each user via conventional Singular Value Decomposition (SVD) technique and power allocation via Waterfilling technique. The base-station then re-broadcast to subsea devices that are further away to increase power transmission for reception. This ensures that the received power levels for each user at the base-station are in par, if not almost in par. However this leads to a shorter operation life if the subsea devices are operating on battery pack (application specific). However, if the subsea devices are attached to external entity with power source, this technique wilt be applicable. The signals are then fed into the base-station Multiple-Output Multiple-User (MOMU) detector to decode the signal and return the original signal to the individual users Capacltyclosed loop -E max log2det(I + USUH) (5) 2) Open loop -Base-station will broadcast to plurality of subsea devices and detects the power received for each sub-sea response which will vary in distances from the base-station. The base-station wilt then rank the power distribution of each user via conventional Singular Value Decomposition (SVD) technique and power allocation via Waterfilling technique. However, the base-station will not re-broadcast to subsea devices that are further away to increase power transmission for reception. This process will be applicable for subsea devices operating with battery packs. The power levels for each user at the base-station are not in par, or unequal. The received signal will be send into the base-station MOMU detector within the base-station to decode the signal and return the original signal to the individual users.

Capacityopeni0op = maxE[log2det(I + HHH)] (6) Hybrid Linear Frequency Modulation (LFM) and Pseudo-Random Binary Sequences (Embodiment of the Invention) An embodiment of the invention utitises a hybrid Linear Frequency Modulation ([FM), chirp signal -with wide ambiguity function against Doppler Shift and Pseudo-Random Binary Sequences (PRBS) Like Gold code, with good auto-correlation and low cross-correlation properties to transmit signals.

The individual properties of LFM chirp signal (1954, US Patent 375/285; 333/14; 333/20; 333/28R; 342/201; 375/306; 380/32; 455/111) and Gold codes are well known and it is not the intention here to reiterate their properties.

However, applying the individual LFM and PRBS Gold code in underwater communication are beset by several Limitations: 1) LFM chirp signal -although LFM has wide ambiguity function against Doppler shift effect, which is more Doppler tolerance. In order to achieve a low cross-correlation factor in multi-user/network environment, a high bandwidth is required to provide enough orthogonal bands for chirp signals so that interference can be minimised and cross-correlation factor will be low. The feature of high bandwidth is not possible in a bandwidth limited underwater communication environment that operates in only a Kilo Hertz (KHz) bandwidth.

2) PRBS Gold code -exhibits high auto-correlation yet low cross-correlation properties. However, Doppler tolerance, fDOPPLER' of PRBS Gold code is Limited by its bandwith, B

DOPPLER

Reference to figure 21 can be made.

An embodiment of the invention is to employ -a hybrid LFM/PRBS Gold Code. A baseband Gold code 3 chip sequence n 0 1 0' (top of figure 21), which can be n=?m-1 chips, is synthesized into a modulated signal (second view of figure 21) and LIM signal (third view of figure 21). The individual synthesized signals of Gold code and LFM are combined prior to transmission (bottom of figure 21).

Users ID strength detection and ranking via mis-matched filtering (Embodiment of the Invention) The objective of the ID detection by the base-station is to identify the sub-sea devices that have transmitted acoustically to the base-station. Each subsea-device transmits a carrier modulated PRBS code, Pseudo-Random Binary Sequences, which consists of a hybrid combination of Linear Frequency Modulation (LFM) and Gold code, which offers good auto-correlation, low cross-correlation factor and wide ambiguity function against Doppler shift.

In the event that the base-station receiver knows a priori the sub-sea device that is transmitting back a data stream, the base-station can employ detection of the ID based on auto-correlation: M PkTk+i (7) An ideal code sequence to employ for ID sequence detection is a 13 chip Barker Code [+1 +1 +1 +1 �1 -1 -1 +1 �1 -1 +1 -1 +1] which gives the following auto-correlation output.

Figure 11 shows the element M, is the main lobe, peak at index 13, while the remaining side Lobes are zeros. This maximum peak, with zeros side lobes ensures that probability of false detection is kept to minimum, ie near 0.

This is an ideal case of ID sequence detection for a single user ID detection. However, due to the fact that the Barker code only has 1 PRBS with such properties, it is not possible to allocate different ID's for each users. The alternative is to employ mis-match filtering.

In a cross-correlation, R, of 2 or more sequences, C and D of period i is R1 = (8) Taking C as sequence to be cross-correlated with D, composing of complex numbers. The sequences C and D is related by the weighting factor, T.

D-TC (9)

In this instance, D is considered a "mis-matched fitter" to Cand the normaUsed filter D can be expressed as, =1 (10) Figure 12 shows the cross-correlation of a hybrid 2 Gold codes/LFM (2 users) 15 chips sequences, it can be seen that the side lobes for each sides of index 13 are high, which increases the probability of false detection once more asynchronous users reception are in the system as the cross-correlation factor will grow with each user, although the theoretical users capacity (not the scope of this invention) with a 15 chip sequence is 2" -I = 2' -I = 15. This is an example of a 15 chips hybrid sequence Gold code/LFM, the system is configured to be adaptable by for example adopting 2-1 chips depending on network capacity.

However, employing a back-to-back mis-matched filtering, the sidelobes within the two peaks are ensured to be zero, see Figure 13. This ensures and allows: 1) False detections for multiple user IDs are kept to minima by averaging the side lobes values between the 2 peaks 2) Propagation delay spread (multipath) in the time-domain via cross-correlation mainly contain the channel transfer function, h(i).

Power Ranking (Embodiment of the Invention) Figure 14 shows the header for each user data stream consisting of a N series of back-to-back PRBS hybrid Gold Code/LFM codes to differentiate its ID, Pseudo-Random Binary Sequences. Power ranking is measured by the correlation peak output for each user.

Doppler Shift Doppler Shift arises from the physical movement of either the sub-sea device or base-station due to the underwater time-varying current. The time-domain passband signaL will either expand or contract in time, this is equivalent to the change in carried frequency. A one-way Doppler Shift from either side of the acoustic transmission entity can be expressed as (11) Doppler Shift Compensation (Embodiment of the Invention) The acoustic transmission time from the base-station to the sub-sea devices last usually for few hundred milliseconds and Doppler Shift is considered minimum. However the transmission time from the sub-sea device to the base-station can last for a few seconds to tens of seconds per burst, therefore Doppler Shift compensation is considered for the uplink path, although it is applicable for both directions.

A Doppler Shift compensation is proposed here. For each base-band symbol period, Tç which is carrier modulated to a passband signal, when undergoing Doppler Shift, it will either expand or contract with time. A window is used to observe this change of frequency component (Figure 15), (12) The term 2AT denotes a partial past symbol period and future symbol period.

For each time-domain window containing r(n) samples, the frequency content can be determined by performing a fast Butterfly FF1 such as fixed point embedded algorithm within a general DSP, R*(k) Jolr*(n)W, (13) where = e12'/N. The passband frequency determined by the FF1 will be the Af in (11), or Doppler Shift. This frequency drift will be added or subtracted from f to perform the down-mixing of the complex signals.

Multiple-Output Multiple-User (MOMU) architecture Multiple-Output Adaptive Feedforward Equaliser (Embodiment of invention) 1) Multiple-Output (MO) adaptive Feedforward Equaliser 2) Soft-Decision Output for Multi-User detection strategy -i.) Single-User mode -set to zero. ii.) Multi-user mode -Summation of Multi-User (MU) Soft-Decision interference cancellation from interfering users back to intended user for signal cancellation 3) Hard-Decision Output -i.) Single-User mode -Detected data output sequence ii.) Multi-User mode -Summation of Hard-Decision fed back to intended user for hard-decision detection.

As each subsea units/remote devices are transmitting narrow bandwidth data each simultaneously. The requirement for an adaptive DFE (Decision Feedback Equaliser) is not required for 2 reasons: 1) The uplink bandwidth for each remote devices is narrow, therefore the channel propagation delay spread (or multipath) will tend not to exceed each symbol period, 7',.

2) Adopting a DFE in a multi-user environment will introduce feedback erroneous cancellation that will propagate for several symbols long when the data packet of an unintended user arrives. This is due to the DFE having a sudden surge in the MSE (Mean Square Error) and will need to adapt the filters coefficients accordingly.

The primary task of the adaptive Feedforward Equaliser (FE), shown in Figure 16, is to equalise the SI (Inter-symbol interference) which result from multipath propagation and phase fluctuations. The single-output feedforward equatisers for each user are used to remove 1St and provide phase compensation.

The operation of the adaptive FE is divided into two phases -the training mode and decision directed mode. In the training mode, a short priori known training sequence which is embedded in the receiver system is used as the desired signals to provide initial -to training for adapting the equaliser tap weights. At the end of the training mode, the equaliser would have attained convergence close to the optimal values, convergence is considered to be obtained when the MSE is below 3dB point. The receiver then switches to the decision directed mode where the detected symbols are treated as the desired signal for further adaptation and equalisation so that variations in the channel can be tracked.

At time n1, where Tç <)B the complex output of the adaptive FE for user K is a(n) f,X(n) (14) where fk and X are the complex filter coefficients, nT spaced samples buffered in the feediorward filters for user K at time nT,.

The symbol error estimation can be defined as e(n) d.(n) -a(n) (15) where d.(n) and a(n) are the hard-decision decision is the pre-decision variable. The corresponding mean square error (MSE) is depicted as MSEK = E(Ie(n)I} (16) The soft decision compLex output, , is based on Maximum Likelihood Estimation (MLE), where a probability density function, Z, is derived from ak(fl) , a series of computed complex output from the adaptive feedforward equaliser from user k, a1, a2 ak(m_1) Z4(a1, a2 ak(m_l)Isk) (17) and the MLE of complex is determined from L(.) = 1ogZ(a(fl)s) (18) The hard decision complex output, d operates in 2 modes: 1) Single user mode -i.) Phase 1 -Training mode, d complex output is based on the priori known training sequence of user k.

ii.) Phase 2 -Data mode, d complex output is based on the adaptive decision of user k.

iii.) In single user mode, the Power Estimate is set to 0', thus the input to the feedforward fiLter only contains Tase(fl) complex signal.

iv.) MLE L(d) set to zero v.) Soft base-band decision n) set to zero 2) Multi user mode -i.) Phase 1 -Training mode, d complex output is based on the priori known training sequence of user k.

ii.) Phase 2 -MAI cancellation, d complex output based on equation (18) for Mth stage interference cancellation.

L(d) = sgn (> 1o9Yã(a(fl)(d)) fl = 1 (19) where is the probability density function derived from a series of Yã(a1, a2 ak(m_1) k1k) The Multiple Output architecture comprises of a series of receiver of Figure 16, shown in Figure 17.

Multiple Output Multiple-User (MOMU) Detection Strategy Apart from the problems accrued from time-varying multipath propagation and Doppler fluctuations, the capacity and performance of a network system is also limited by multiple access interference (MAI). As the Multiple-Output adaptive Feedforward equaliser does not take into account the existence of MAI from other users, by which each user in the system is detected separately without regard for other users. The effect of MAt will become substantial as the number of interferences or power differences increases in the network system. A better detection strategy is one of Multiple Output Multi-User Detection (MOMU). Here, the information of multiple users is used jointly in order to better detect each individual user in the system. By utilising MOMU algorithms, there is significant added benefit in providing reliable communication in a network system.

In this section, 3 methods of MOMU schemes for multiple access interference cancellation are proposed and developed: i.) MOMU Soft decision base-band Parallel Interference Cancellation (SDBB-PIC) ii.) MOMU Soft decision base-band Successive Interference Cancellation (SDBB-SIC) iii.) MOMU Soft decision base-band Recursive Interference Cancellation (SDBB-RIC).

MOMU Soft Decision Base-Band Parallel Interference Cancellation (SDBB-PIC) MU Strategy (Embodiment of the Invention) Multi-user receiver structure based on parallel interference cancellation (PlC) estimates and subtracts all the MAt for k users concurrently [42] -[43]. Recent work in mobile communication has shown that the performance of PlC can be improved by performing an initial partial cancellation [44J -[46]. The partial cancellation involves multiplying the estimated symbol of each user with a factor less than unity prior to any interference cancellation. This takes into account the fact that the tentative decisions made in the earlier stages are less reliable than those of the later stages. However, the act of employing these PlC schemes in underwater acoustic communications is unrealistic since these MUD structures, developed in mobile communications, do not take into account the predominant effects of ISI, phase fluctuations and Doppler effects encountered in the underwater acoustic environment.

The pass-band PlC MUD structure proposed by H. K. Yeo [47} based on weighted parallel interference cancellation (PlC) adopts a DEE for each user. The complex hard decision output for each user is weighted and use to re-constructs the pass-band signal for parallel interference cancellation. However, there are 2 limitations with such MUD detection with increased users.

i.) With the increase in users, the probability of the feedback path of the DFE propagating errors will increase during the first stage of MAI cancellation, therefore increasing the MSE for each user and it will take a longer time for the adaptive filters to converge.

ii.) With the increased feedback propagated error via the DEE for each user, the re-generated pass-band carrier frequency component may be distorted which then results in signal phase cancellation.

In an embodiment of the invention, a Mth-stage MOMU receiver structure based on soft decision base-band weighted parallel interference cancellation (PlC) with adaptive EE is proposed.

Input to Stage 1 The input vector, rLBase(n) and rase(n), are the single channel L input or multiple channel input to each of the user, depending on the channel conditions.

The conditions of selecting single channel or multiple channels are based on the following: a.) Power ranking (invention 4) -determine how many users are detected.

b.) Time domain channel impulse response -based on 1O.log(Time Domain Impulse Response), shown right of Eigure 22.

c.) Signal to Noise Ratio (SNR) measurement -based on a measurement of

background noise and signal level.

Output from Stage 1 In stage 1 of the soft-decision base-band PlC structure, the initial bits/symbols for alt users k = 1,2,3 K, are estimated from the corresponding adaptive FE units. The power estimation, P, for all users and the soft-decision base-band estimate, , is set to zero.

Input to Stage 2 till Stages (M-1) In stage 2 to (M-1), a circular rotation for each user is performed within the DSP by which each user is rotated by one step for MAI cancellation. For clarity sake, the power estimate, P,1, and complex soft decision base-band output, (n), from user 1 is fed into user 2 decoding block.

The estimated bit decisions are then regenerated with a weighting factor and phase correction. The regenerated signal is then subtracted from the received signal at the receiver array elements. The modified received signal, having one fewer interfering signals, is then passed to the next stage for processing and the removal of a further user signal.

This process of parallel decision estimation, regeneration, weighting and interference cancellation is repeated for M stages, where the last stage are where all MAI have been removed between users. The T1 term in Figure 18 denotes the time delay of the received signal to be summed with the regenerated MAI signals of other users at the (M- 1)th stage.

A buffer window is used to store the time reference or time delay estimation for each user for asynchronous reception. At stage M-1, the signal vector that is fed either to single element input vector, rLBse(n) or array elements rase(n) of user K, at stage K-i, in the soft-decision base-band parallel interference canceLlation (SDBB-PlC) in stage K-i, is expressed as: K K-i M-1 K-i = a1 h1 b1 (t -nT) -P(n) . L ( m (n)) + p(n) k=1 k=1 m=lk=1 (20) where the second term and third term are the power-estimation, soft-decision for all users, k = l,2,3,...(K -1), and the residual noise, respectively.

At stage K, the retrieved information from the adaptive FE for user K from the output of stage M is decoded by the summation of maximum likelihood at each stage, d(n) = L( t,m(n1)) = Sgfl(>_i 1ogYd(a(fl)Id)) (21) In order for a new set of signals to be regenerated for better data estimation in the next stage, the PlC structure assumes that the decision of the previous stage has been estimated correctly. Therefore any estimation error, contributed by any user, will degenerate the removal of MAI for other users. This problem arises in a "near-far" scenario, where the received signal for the weak user, coupled with the strong MAI from the other users that are fed into the DFE structure, will encounter difficulties in estimating the data correctly. Therefore, it can be seen that the PlC receiver structure is superior in a weLl-power-controlled channel, where all signals from separate users are at an almost equal power level.

MOMU Soft Decision Base-Band Successive Interference Cancellation (Embodiment of the Invention) By contrast to the adaptive PlC receiver structure, the successive interference cancellation (SIC) strategy uses a successive approach towards MAI cancellation [48] -[50]. The approach for SIC is based on a simple but elegant idea [48]. If a decision has been made about the interfering strong user's bit, then the interfering signal can be regenerated at the receiver and subtracted from the received signal. The resulting subtracted signal should then be free from the strong interfering signal. However, this assumption relies greatly on the accuracy of the decision made for the interfering signal; if this decision is incorrect it will double the contribution of the interfering weaker signal. Once the interfering signal is stripped away from the received signal, the signal processing side of the receiver takes the view that the resulting signal contains one fewer users. The process is then repeated with the other weaker users, until the last user (weakest user) has been demodulated. The SIC structure of [48] uses decisions produced by single-user matched filters, which neglects the presence of the interfering signals. This approach has been reported to perform well in a near-far situation under AWGN.

The SIC receiver structure, proposed here employs a soft decision base-band weighted parallel interference cancellation (SDBB_SIC) as shown in Figure 19. It is assumed, without loss of generality, that the powers of k = 1,2 K users are in descending order and that perfect delay estimation is achieved. In this case, the strongest user, K = 1, correct bit decision is regenerated by multiplying with a weight factor, w,k, with phase correction and is then subtracted from the received signal. Therefore, this approach aims to remove the strongest MAt from the received signal. The detector then makes a decision for the next strongest user (k = 2) from the subtracted signal. This process of decision-making, regeneration, weighting, phase correction, and cancellation from the received signal continues until the weakest or last user, K, has been decoded. The retrieved information symbol from the output of user K adaptive DFE in stage M can also be determined from equations (20) -(21), however, it should be noted that the MAI reductions are performed successively.

The technique of removing the MAI of the strongest user from the received signal aids in the estimation of signals for weaker users. Therefore SIC can be seen to be superior in a non-well-power-controlled channel. However, one prime disadvantage of such system is that the strongest user does not benefit from the reduction of MAt, which means that the summed MAI effects from all other weaker users will, to a certain degree, affect the correct data estimation for the strongest user. The embodiment presented here for the adaptive MOMU SIC strategy can be described in the following algorithmic form: 1) Obtain energy statistics from the received signal to rank users in descending power.

2) Perform adaptive symbol estimation of strongest user (amplitude and phase).

3) Estimated result is regenerated and cancelled from received signal.

4) Subtracted received signal is passed to next weaker user for decoding.

5) Repeat 1) to 4) until the last or weakest user K is decoded.

The practical implementation features of the MOMU SIC can be summarised as follows: 1) Prior to adaptive signal processing, knowledge of the received power for all users in the network cell is required so that interference cancellation can be performed successively. Any errors in the estimation translate directly into additive interference for further decision making for weaker users.

2) Users weaker than the intended user are neglected for SIC.

3) The delay time for demodulation for SIC grows linearly with the number of users.

4) Time complexity per bit is linearly related to the number of users in the system.

Adopting the SIC strategy has a number of general advantages [5i], [52]. Firstly, the receiver has the best chance of estimating the correct decision for the strongest user in the system. Secondly, removing the strongest user in the system gives the most benefit to the remaining weaker users. The SIC structure can be considered to be effective if the received power for users are widely variable. A major shortcoming of the adaptive SIC processor is that its performance is asymmetric, where users of equal received power are demodulated with disparate reliability. This is the opposite of the adaptive PlC, which means that the summed MAI effects from alt other weaker users will, to a certain degree, affect correct data estimation.

MOMU Soft Decision Base-Band Recursive Interference Cancellation (An embodiment of the Invention) In order to circumvent the short falls of the PlC and SIC structures, a new MOMU technique of MAI cancellation based on recursive successive interference cancellation (RSIC) has been proposed in [531 -[54].

The embodiment presented here is based on MOMU soft decision base-band recursive interference cancellation (SDBB-RIC), shown in Figure 20. In the case of unequal power reception, the receiver, having a priori knowledge of K users, first detects and obtains statistics from the received signal, r(t), to rank the users in order of descending power. The selector then switches to the corresponding adaptive FE of the detected strongest user.

Subsequently, the output decision of the strongest user is regenerated, multiplied by the weighting factor, Pw,k, with phase correction and is then cancelled from the received signal.

The subtracted received signal is then passed to the next strongest user for decoding as if the received signal consists of K -1 user. This process is repeated until the last (weakest) user has been decoded. The distinctive feature of the SDBB-RIC structure, as compared to the SIC structure, is the feedback loop in Figure 20. This allows the strongest user to cancel out the summed effects from other users. The output decisions from all other users are summed, regenerated, weighted with phase correction and cancelled from the received signal. With the subtracted signal, the strongest user is decoded again, with the assumption that only background noise is present. Decoding for the rest of the users is then performed for a predefined number of loops.

In the case of equal power reception, all users are placed with the same priority and the selector switches to the first available user for adaptive FE estimation. The procedure of decoding of K equal power users is the same as that of unequal power reception, except there is now only an arbitrary priority between users.

Assuming again that the power of k = 1, 2 K users are in descending order and there is perfect delay estimation. The information symbols for user K are retrieved successively as described in previous section, this aspect is identical to the SDBB-SIC structure. During the loop back, the output symbol decisions for users k = 2, 3 K, are summed, regenerated and cancelled from the received signal. The subtracted signal that is fed-back to user 1 can then be decoded free from MAt of other users. The output from the adaptive FE of user 1 at (M+ 1)th stage can be expressed as _t( M "l,(M+l) -. m=2 (22) where the signal, that is fed to single element single element input vector, , (n) or array elements r; (n) of user 1, is Xl,M+l a1 h1 b1 (t -nT1) -P(n) . L ( (n)) + p(n) k=1 k=2 m=2k=2 (23) Expanding (22) leads to

M

di(M+1) L(a;m(n)) = sgn 1ogYa;(a(fl)Id) -C1,M(fl)) (24) where clM(n) is for the removal of postcursor 1St for user 1 at stage (M + 1). At stage 2M, for a single loop-back, the retrieved information for user K is depicted as: d2M(n) = sgn(1 1ogYa;(a(fl)Id) -CK,(2M_1)(fl)) (25) where the single element input vector, rage (n) or array elements rase (n) of user K, is K K-i 2M K-i XK2M1 = -nTr1) -P(n) .L ( m (n)) + p(n) k=1 k=i m=2 ki (26) Operation of this detector can be described in the following algorithmic form: 1) Obtain statistics from the received signal to rank users in descending power. If the received powers are equal, switch to the DFE for the first available user. Process steps ii) to vii) of the algorithmic flow.

ii) Perform adaptive DEE symbol estimation of the strongest/first user (amplitude and phase).

iii) Estimated result is regenerated and cancelled from received signal.

iv) Subtracted received signal is passed to the next strongest user DEE for decoding.

v) Repeat i) to iv) until the last or weakest user K is decoded.

vi) Decisions of all subsequent users are then summed, regenerated, cancelled from the received signal and fed back to the strongest user for MAI cancellation.

vii) Repeat ii) to v) where regeneration and MAI cancellation is performed for the next weaker user, k = 2, 3, 4 K for a pre-defiried number of iterations.

The advantages of implementing the adaptive SDBB-RSIC structure are threefold. Eirstly, the RSIC structure offers the flexibility to self-adapt to handle equal or unequal power reception. In the case of equal power reception, the RSIC structure operates identically as the PlC structure. Whereas in an unequal power reception, the recursive loop back feature allows the strongest user to benefit from the reduction of MAI from other users. Secondly, with the MAI reduced, the adaptive EE block that is incorporated for each user can then effectively cope with the multipath fading propagation and inter-symbol interference (ISI).

And finally, implementing the RSIC structure can effectively tackle the problem of power control inefficiency in horizontal-link communication. Therefore, the adaptive RSIC MUD structure manifests itself to be a superior candidate for implementation in SWAN for both well-power-controlled and non-well-power-controlled channels. Erom the analysis, the adaptive PlC receiver structure has a processing load of (K x M), whereas the adaptive SIC receiver structure has only a processing load of M. Although the adaptive RSIC receiver structure has an increased load, (2M), compared to the SIC structure, it requires a much lower computational load than the PlC structure. One major gain of the RSIC over the SIC MUD structure is that users weaker than the intended user are accounted for by the toop-back feature.

Prior art references

[1] R. Conot, "Thomas A. Edison:A Streak of Luck," Da Capo, New York, 1979.

[2] Shallow Water Acoustic Network, MAST-HI project web page: http://www.mcli nk.it/com/swan/.

[3] I. Dyer, M. J. Crocker, "Handbook of Acoustic," 1t Edition, John Wiley & Sons, 1998, Chapter 30, pg. 339-344.

[4] MacCurdy, E. "The Notebooks of Leonardo da Vinci," Garden City, N. Y., Garden Publishing Co. Inc, 1942, Chapter X. [5] "Sound," The New Encyclopaedia Britannica, voL 17, Chicago: Encyclopaedia Britannica, pg. 19, 1974.

[6] Strutt, John W. (Lord Rayleigh), "Theory of Sound," vols. I and II, MacGraw Hill, New York, Chapter 1, 1967.

[7] William S. Burdic, "Underwater Acoustic System Analysis," 2nd Edition, Prentice Hall, Englewood Cliffs, 1991.

[8] RobertJ. Urick, "Principles of underwater sound," 3' Edition, McGraw HiLl, 1983.

[9] H. 0. Berktay, B. Gasey, and C. A. Teer, "Underwater Communication, Past, Present and Future," J. Sound Vib., vol. 7, pg. 62-70, 1968.

[10] A. Baggeroer, "Acoustic Telemetry-An Overview," IEEE Journal of Oceanic Eng., vol. 9, No. 4, pg. 229-235, October 1984.

[11] IEEE J. Oceanic Eng., "Special Issue on Ocean Acoustic Data Telemetiy," vol. 16, no. 1, pp. 1-177, 1991.

[12] A. Clark, "Diver Communication -The Case for Single Sideband," Underwater System Design, pp. 16-18, 1989.

[13] W. Dow, "A Telemetering Hydrophone," Deep-Sea Research, vol. 7, pp. 142-147, 1960.

[14] P. Hearn, "Underwater Acoustic Telemetiy," IEEE Trans. Communication Tech., vol. CT-14, pp. 839-843, 1966.

[151 F. R. Mackelburg, S. J. Watson, A. Gordon, "Benthic 4800 bits/second Acoustic Telemetry," Proc. Oceans, pp. 78-82, 1981.

[16] John Proakis, "Digital Communications," 3rd Edition, Mcgraw Hill, New York, 1995.

[17] J. Catipovic, A. B Baggeroer, K. Von Der Heydt, D. Koelsch, "Design and Performance Analysis of Digital Acoustic Telemetry Systems for the Short Range Unde,water Channel," IEEE J. Ocean Eng., vol. 9., no. 4, pp. 242-252, 1984.

[181 http://www.datasonic.com/products/modems/atm850.htm [19] j. Catipovic, M. Deffenbaugh, L. Freitag and D. Frye, "An Acoustic Telemetry System for Deep Ocean Mooring Data Acquisition and Control," Proc. Oceans, Seattle, WA, pp. 887-892, October 1989.

[20] S. J. Roberts, "An Echo Cancellation Technique Applied to an Underwater Acoustic Data Link," Ph.D Thesis, Department of Electrical and Electronic Engineering, Herriot Watt University, Edinburgh, Scotland, September 1983.

[21] C. Sandsmark, "The Feasibility of Adaptive Equalisation in High Speed Underwater Acoustic Data Transmission," Ph.D Thesis, NIH, Irondheim, Norway, 1990.

[22] A. Kaya and S. Yauchi, "An Acoustic communication System for Subsea Robot," Proc. Ocean'89, Seattle, WA, no. 765-770, Oct. 1989.

[23] M. Suzuki and I. Sasaki, "Digital Acoustic Image Transmission System for Deep Sea Research Submersile," Proc. Ocean'92, Newport, RI, pp. 567-5 70, Oct 1992.

[24] J. Fischer, K. Bennett, S. Reible, J. Cafarella and I. Yao, "A High Rate, Underwater Acoustic Data Communication Transceiver," Proc. Ocean'92, Newport, RI, pp. 571-576, Oct. 1992.

[25] M. Johnson, D. Herold and J. Catipovic, "The Design and Performance of a Compact Uride,waterAcoustic network Node," Proc. Oceans'94, Brest, France, pp. 467-471, Sept. 1994.

[26] M. Stojanovic, J. A. Catipovic and J. G. Proakis, "Phase Coherent Digital Communications for Underwater Acoustic Channels," IEEE J. Oceanic Eng., vol. 19, pp. 100-111, Jan. 1994.

[27] R. Calvin and R. F. W Coates, "Analysis of the Performance of An Underwater Acoustic Communication System and Comparison with a Stochastic Model," Proc. Oceans'94, Brest, France, pp. 478-482, Sept. 1994.

[28] G. S. Howe, P. S. D. Tarbit, 0. R. Hinton, B. S. Sharif and A. E. Adams, "Sub-sea Acoustic Remote Communications Utilizing an Adaptive Receiving Beamformer for Multipath suppression," Proc. Ocean'94, Brest, France, pp. 313-316, Sept. 1994.

[29] P. S. D Tarbit, G. Howe, 0. R. Hinton, A. E. Adams and B. S. Sharif, "Development of a Real-Time Adaptive Equaliser [or a High-Rate Underwater Acoustic Data Communication Link," Proc. Oceans'94, Brest, France, pp. 307-312, Sept. 1994.

[30] B. S. Sharif, J. Neasham, 0. R. Hinton & A. E. Adams. "A computationally Efficient Doppler Compensation System for Underwater Acoustic Communications," IEEE J. Oceanic Eng., vol. 25, no.1, January 2000.

[3J B. S. Sharif, J. Neasham, 0. R. Hinton & A. E. Adams. "Adaptive Doppler compensation for coherent acoustic communications," lEE Proc. Radar, Sonar Navigation, Vol. 147(5), Oct 2000.

[32] S. Verdu, "Adaptive Multiuser Detection," Proc. IEEE 3rd mt. Symp. Spread Spectrum Tech. Applicat., Outu, Finland, pp. 43-50, July 1994.

[33] M. Stojanovic and Z. Zvonar, "Multichannel Processing of Broadband Multiuser Communication Signals in Shallow WaterAcoustic Channels," IEEE j. Ocean Eng., voL. 12, pp. 156-166, April 1996.

[34] S. Verdu, Minimum Probability of Error [or Asynchronous Gaussain Multiple Access Channels," IEEE Trans. Inform. Theory, vol. 32, no. 1, pp. 85-96, 1986.

[35] L. E. Franks, "Carrier and Bit Synchronization in Data Communications: A Tutorial Review," IEEE Trans. Comms, vol. 28, no.8, pp. 1107-121, 1980.

[36] A. B. Carlson, "Communication Systems," McGraw Hill, 1989.

[37] john Proakis, "Digital Communication," 3rd Edition, pp. 781, McGraw Hill, Singapore, 1995.

[38] B. S. Sharif, Jeff Neasham, A. E. Adams, 0. R. Hinton, "Adaptive Doppler Compensation for Coherent Acoustic Communications," I E E Proceedings, 2000.

[39] M. Stojanovic, L. A. Catipovic and J. G. Proakis, "Phase Coherernt Digital Communications for Unde,water Acoustic Channels," IEEE J. Oceanic Eng., vol. 19, pp. 100-111, 1994.

[40] Barry D. Van Veen and Kevin M. Buckley, "Beamforming: A Versatile Approach to Spatial Filtering," IEEE ASSP Magazine, pp. 4-24, April 1988.

[41] M. Stojavonic, Zoran Zvonar, "Multichannel processing of broad-band multiuser communications signals in shallow water acoustic channels," IEEE Journal of Oceanic Engineering, vol. 21, no. 2, pp. 156, April 1996.

[421 M. K. Varanasi and B. Aazhang, "Multistage Detection in Asynchronous Code-Division Multiple Access Communications," IEEE Transactions on Communications, vol. 38. No 4, pp. 509-5 19, April 1990.

[43] R. M. Buehrer and B. D. Woerner, "Analysis of Adaptive Multistage Interference Cancellation for CDMA Using an Improved Gaussian Approximation," IEEE Trans. on Comms, vol. 44, no. 10, PP. 1308-1321, Oct 1996.

[44] R. M. Buehrer and S. P. Nicoloso, "Comments on "Partial Parallel Interference Cancellation for CDMA"," IEEE Trans. on Comms, vol. 47, no. 5, pp. 658-661, May 1999.

[45] D. Divsalar, M. K. Simon and D. Raphelti, "Improved Parallel Interference Cancellation," IEEE Trans. of Comrns, vol. 46, Pp. 258-268, 1998.

[46] N. Correal, R. M. Buehrer and B. D. Woerner, "A DSP based DS CDMA Multiuser Receiver Based on Partial Interference Cancellation," IEEE J. of Select. Areas Comm., vol. 17, pp 613-630, April1999.

[47] H. K. Yeo, B. S. Sharif, A. E. Adams and 0. R. Hinton, "Performances of Multi-Element Multi-User detection strategies in Shallow Water Acoustic Network (SWAN)", IEEE Journal of Ocean Engineering, voL. 26, Issue 4, pg. 604 -611, October 2001.

[48] Pulin Patel, Jack HoLtzman, "Analysis of a simple successive interference cancellation scheme in a DSICDMA system," IEEE Journal on selected areas in Comms, PP. 798 -807, June 1994.

[49] Y. Cho, J. H. Lee, Analysis of an Adaptive SIC for Near-Far Resistant DS-CDMA," IEEE Transactions on Communications, PP. 1429-1433, November 1998.

[50] Andrew L. C. Hui and Khaled Ben Letaief, "Successive Interference Cancellation for multiuser asynchronous DS/CDMA detectors in multipath fading links," IEEE Trans. of Communications, vol. 46, no. 3, PP. 384-391, March 1998.

[51] A. Duet-Hatlen, J. Holtzman and Z. Zvonar, "Multi-UserDetection for CDMA systems," IEEE Pers. Commun., vol. 2, PP. 46-58, 1995.

[52] R. Kohno, "Combination of an Adaptive Array Antenna and a Canceller of Interference for Direct-Sequence Spread-Spectrum Multiple Access System," IEEE JSAC, vol. 8, no. 4, pp. 6 75-682, 1990.

[53] H. K. Yeo, B. S. Sharif, A. E. Adams and 0. R. Hinton, "Multi-user Detection for a Time-Variant Multipath Environment," IEEE Proceedings of the 2000 International Symposium for Underwater Technology, Tokyo, Japan, pp. 399-404, May 2000.

[54] H. K. Yeo, B. S. Sharif, A. E. Adams and 0. R. Hinton, "Implementation of Multi-user Detection Strategies for Coherent Underwater Acoustic Communication", IEEE Journal of Ocean Engineering, voL 27, Issue 1, pg. 17-27, January 2002.

Claims (55)

1. Claims 1. A method of underwater acoustic communication between N acoustic transmitters and M remotety located acoustic signal receiver to form an underwater network comprising at least one or any combination of the following steps: configuring said transmitters and said receiver to share a common frequency band; ranking signals representing the power of at least two signal transmissions received by said receiver from said transmitter; observing a signal contained within said signal transmission to determine if it has expanded or contracted within a predetermined time period; equalising Inter-Symbol Interference from multipath propagation and phase fluctuations; and cancelling Multiple Access Interference generated from simultaneous reception of a plurality of signals receiving two or more signal transmissions.
2. 2. A method according to claim 1, wherein N is greater or equal to 2.
3. 3. A method of underwater acoustic signal transmission comprising the steps of arranging a number of distinct underwater acoustic devices with transmitters for acoustically transmitting to a base station with multiple-output hydrophones; and transmitting signals from said devices to said base unit over a common frequency bandwidth.
4. 4. A method of underwater acoustic communication with a base station and a plurality of underwater devices forming a network of underwater receivers and transmitters, comprising the steps of broadcasting signals to a plurality of underwater devices from a base station; detecting power received for each underwater response; ranking said devices in terms of power; and broadcasting signals to a selection of underwater devices to increase power transmission for reception.
5. 5. A method of underwater acoustic communication with a base station and a plurality of underwater devices forming a network of underwater receivers and transmitters, comprising the steps of broadcasting signals to a plurality of underwater devices from a base station; and switching between multi-element and single element receiver output modes dependent upon the evaluation of power, multi-path and noise.
6. 6. A method of underwater acoustic communication with a base station and a plurality of underwater devices forming a network of underwater receivers and transmitters, comprising no step of broadcasting to transmitters to increase power transmission; and the steps of sending receiver signals to a MOMU detector and returning a signal to individual transmitters.
7. 7. A method of underwater acoustic communication with a base station and a plurality of underwater devices forming a network of underwater receivers and transmitters, comprising the step of transmitting an identifying code utilisirig a hybrid of Pseudo-Random Binary Sequences and Linear Frequency Modulation (LFM).
8. 8. A method according to claim 7, comprising the step of back-to-back mis-matched filtering.
9. 9. A method according to either of the preceding claims, comprising the step of power ranking by measuring the correlation peak output for each transmitter.
10. 10. A method of underwater signal processing comprising the steps of: receiving a signal; employing a window for observing a change of frequency component for a base-band symbol period which is carrier modulated to a passband signal; and employing a fast butterfly FFT (Fast Fourier Transform) for a time-domain window containing samples to determine a passband frequency; whereby Doppler shift is determined.
11. 11. A method according to claim 10, wherein said window takes the form substantially as defined in equation (12).
12. 12. A method according to either of the preceding claims, wherein said fast butterfly FFT takes the form substantially as defined in equation (13).
13. 13. A method according to any of claim 10 to 12, wherein said method further comprises the steps of 1) compensating by adding or substracting a frequency component; and then 2) down-mixing to baseband signals.
14. 14. A method of underwater signal processing, comprising the step of providing one or more adaptive feedforward equalisers.
15. 15. A method of underwater signal processing according to claim 14, comprising the steps of providing said adaptive feedforward equalisers with equaliser taps; sending predetermined training sequences for adapting said equaliser taps' weights; and switching to a decision directed mode.
16. 16. A method according to either of the preceding claims, wherein said adaptive feedforward equaliser is configured to have a complex output substantially as defined in equation (14).
17. 17. A method according to any of claims 14 to 16, wherein said adaptive feedforward equaliser defines a symbol error estimation substantially as in equation (15).
18. 18. A method according to any of claims 14 to 17, wherein said adaptive feedforward equaliser defines a mean square error substantially as in equation (16).
19. 19. A method according to any of claims 14 to 18, wherein said adaptive feedforward equaliser is configured to have a soft decision complex output based on maximum likelihood estimation where a probability density function is derived from a series of computed complex outputs as in equation (17) and the maximum likelihood estimation of said soft decision complex output is derived from equation (18).
20. 20. A method according to any of claims 14 to 19, wherein said method incorporates a hard decision complex output operating in a first mode suitable for single user operation; and a second mode suitable for multi-user operation.
21. 21. A method according to claim 20, wherein said first mode suitable for single user operation incorporates a data mode based on the adaptive decision of a user; the power estimate is set to zero; the maximum likelihood decision is set to zero; and the soft base-band decision is set to zero.
22. 22. A method according to claim 20, wherein said second mode suitable for multi-user operation incorporates a multiple access interference cancellation step substantially based on equation (18) for soft base-band decision and substantially based on equation (19) for hard base-band decision.
23. 23. A method according to claim 20, wherein said method incorporates interference cancellation steps substantially based on any one of equations (20) to (26).
24. 24. A method of underwater signal processing, comprising the steps of: determining an input vector for single or multiple channel inputs to each user depending upon channel conditions; determining initial bits/symbols for N users from their corresponding adaptive feedforward equaliser unit whilst selecting a power estimation; and setting soft-decision base-band estimate to zero; feeding a power estimate and a complex soft decision base-band output from a first user to a second user's decoding block; regenerating bits/symbols with a weighting factor and phase correction; substracting the regenerated bits/symbols from a received signal to obtain a modified received signal; passing said modified received signal to a further stage for the removal of a further user signal; repeating the process of decision estimation, regeneration, weighting and interference cancellation for N-i stages.
25. 25. A method according to claim 24, further comprising the step of employing a buffer window to store the time reference or time delay estimation for each user.
26. 26. A method according to either of the preceding claims 23 and 25, wherein a signal vector is fed in a soft-decision base-band parallel interference cancellation process which substantially takes the form of equation (20).
27. 27. A method according to claim 26, wherein the adaptive feedforward equaliser derives retrievable information which for a user is decoded by summation of maximum likelihood at each stage according to equation (21).
28. 28. A method according to any of claims 24 to 27, further comprising the step of obtaining energy statistics from received signals to rank users in descending power.
29. 29. A method according to claim 28, further comprising the step of neglecting users which are weaker than an intended user.
30. 30. A method according to either of the preceding claims 28 and 29, further comprising the step of linearly relating the time complexity per bit to the number of users in the system.
31. 31. A method of underwater signal processing, comprising the steps of: obtaining energy statistics from received signals to rank users in descending power; performing adaptive symbol estimation of the strongest user; regenerating and cancelling estimated result from received signal; passing substracted received signal to the next weaker user for decoding; and repeating the preceding steps to decode signals of other users.
32. 32. A method of underwater signal processing according to claim 31, comprising the steps of: providing a feedback loop to cancel out summed effects from other users; summing, regenerating, weighting with phase correction and cancelling output decisions of a stronger user from other users to obtain a substracted signal; and decoding the subtracted signal whilst assuming that only background noise is present.
33. 33. A method of underwater signal processing, comprising the steps of: obtaining energy statistics from received signals; randomly selecting a first user if equal energy statistic are obtained for each user; performing adaptive symbol estimation of the first user; regenerating and cancelling estimated result from received signal to obtain substracted received signal; passing substracted received signal to the next user for decoding; repeating the preceding steps to decode signals of other users; providing a feedback ioop to cancel out summed effects from other users; summing, regenerating, weighting with phase correction and cancelling output decisions of said first user from other users to obtain a substracted signal; and decoding the subtracted signal whilst assuming that only background noise is present.
34. 34. A method of underwater communication substantially as hereinbefore described with reference to and/or as illustrated in any appropriate of the accompanying text and/or figures.
35. 35. An underwater communication system, for communication between N acoustic signal transmitters and M remotely located acoustic signal receiver to form an underwater network comprising: means for said transmitter and said receiver to share a common frequency band for at least one signal transmission; means for ranking signals representing the power of at least two signal transmissions received by said receiver from said transmitter; means for said receiver to identify said transmitter; means for observing the information contained within said signal transmission to determine if said signal expanded or contracted within a predetermined time period; means for equalising Inter-Symbol Interference from multipath propagation and phase fluctuations; and means for Multiple-User Detection within said network.
36. 36. A system according to claim 35, wherein said transmitter and said receiver are configured to assign a predetermined code for establishing a transmission channel between said transmitter and said receiver over said frequency band.
37. 37. A system according to either of claims 35 and 36, wherein said transmitter is configured to increase the power for said information transmission when said received ranked signals are below a predetermined power threshold.
38. 38. A system according to any of claims 35 to 37, wherein said receiver is configured not to broadcast to transmitters to increase power transmission; the receiver incorporates a MOMU detector; said receiver is configured to send received signals to said detector and return a signal to individual transmitters.
39. 39. A system according to any of claims 35 to 38, wherein said transmitter transmits an identifying code to said receiver.
40. 40. A system according to any of claims 35 to 39, wherein said identifying code utilises a hybrid of Pseudo-Random Binary Sequences and Linear Frequency Modulation.
41. 41. A system according to any of claims 35 to 40, wherein said means for equalising Inter-Symbol Interference further comprises a training means and a decision means.
42. 42. A system according to claim 41, wherein said training means comprises a predetermined training code which simulates at least one received signal to initially configure said means for equalising Inter-Symbol Interference.
43. 43. A system according to claim 41, wherein said decision means receives a plurality of transmitted symbols embedded within said signal; said symbols represent said information contained within said received signals for subsequent adaptation and equalisation.
44. 44. A system according to any of claims 35 to 43, wherein said means for Multiple-User Detection further comprises a means for cancelling Multiple Access Interference generated
45. 45. A system according to claim 44, wherein said means for cancelling Multiple Access Interference is configured to utilise said received transmitted information; said received information is weighted and reconstructed to form said received signal.
46. 46. A system according to claim 45, wherein said received transmitted information is weighted and reconstructed to form said received signal is repeated over a plurality of stages.
47. 47. A system according to either cLaim 44 or 45, wherein the means for cancelling Multiple Access Interference within said network is MOMU soft-decision base-band Parallel Interference Cancellation.
48. 48. A system according to any of claims 35 to 47, wherein said means for cancelling Multiple Access Interference is configured to obtain the highest ranked signal representing a received signal; said highest ranked signal is multiplied with a weighting factor with phase correction and is then subtracted from the received signal.
49. 49. A system according to any of claims 35 to 44 and 48, wherein said means for cancelling Multiple Access Interference is configured to be repeated over a plurality of stages; each stage processes the next ranked signal which is weaker than the previous signal.
50. 50. A system according to any of claims 35 to 44, 48 and 49, wherein said means for cancelling Multiple Access Interference within said network is MOMU soft-decision base-band Successive Interference Cancellation.
51. 51. A system according to any of claims 35 to 44 and 48, wherein one or more decision means are summed together, multiplied with a weighting factor, subtracted from the received signal and fed back to the highest ranked signal for Multiple Access Interference cancellation.
52. 52. A system according to any of claims 35 to 44, 48 and 51, wherein said means for cancelling Multiple Access Interference is configured to be repeated for each ranked signal.
53. 53. A system according to any of claims 35 to 44, 48, 51 and 5?, wherein said means for cancelling Multiple Access Interference is MOMU soft-decision base-band Recursive Interference Cancellation.
54. 54. An underwater communication system substantially as hereinbefore described with reference to and/or as illustrated in any appropriate of the accompanying text and/or figures.
55. 55. An underwater device configured to operate the method and/or system of any of the io preceding claims.